Publications

GitChameleon: Evaluating AI Code Generation Against Python Library Version Incompatibilities. Nizar Islah, Justine Gehring, Diganta Misra, Eilif Muller, Irina Rish, Eilif Benjamin Muller, Massimo Caccia. At Annual Meeting of the Association for Computational Linguistics (ACL), 2026.

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No, of Course I Can! Deeper Fine-Tuning Attacks That Bypass Token-Level Safety Mechanisms. Joshua Kazdan, Abhay Puri, Rylan Schaeffer, Lisa Yu, Chris Cundy, Jason Stanley, Sanmi Koyejo, Krishnamurthy (Dj) Dvijotham. At International Conference on Learning Representations, 2026.

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Grounding Computer Use Agents on Human Demonstrations. Aarash Feizi, Shravan Nayak, Xiangru Jian, Kevin Qinghong Lin, Kaixin Li, Rabiul Awal, Xing Han Lu, Johan Obando, Juan A. Rodriguez, Nicolas Chapados, David Vazquez, Adriana Romero Soriano, Reihaneh Rabbany, Perouz Taslakian, Christopher Pal, Spandana Gella, Sai Rajeswar Mudumba. At International Conference on Learning Representations, 2026.

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DRBench: A Realistic Benchmark for Enterprise Deep Research. Amirhossein Abaskohi, Tianyi Chen, Miguel Muñoz-Mármol, Curtis Fox, Amrutha Ramesh, Étienne Marcotte, Xing Han Lu, Nicolas Chapados, Spandana Gella, Christopher Pal, Alexandre Drouin, Issam H. Laradji. At International Conference on Learning Representations, 2026.

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StarFlow: Generating Structured Workflow Outputs From Sketch Images. Patrice Béchard, Chao Wang, Juan A. Rodriguez, Amirhossein Abaskohi, Christopher Pal, David Vazquez, Spandana Gella, Sai Rajeswar Mudumba, Perouz Taslakian. At European Chapter of the Association for Computational Linguistics (EACL), 2026.

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PipelineRL: Faster On-policy Reinforcement Learning for Long Sequence Generation. Rafael Pardinas, Ehsan Kamalloo, Alexandre Piche, Dzmitry Bahdanau. At Transactions on Machine Learning Research (TMLR), 2026.

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How to Train Your LLM Web Agent: A Statistical Diagnosis. Dheeraj Vattikonda, Santhoshi Ravichandran, Emiliano Penaloza, Hadi Nekoei, Thibault Le Sellier De Chezelles, Megh Thakkar, Nicolas Gontier, Miguel Muñoz-Mármol, Sahar Omidi Shayegan, Stefania Raimondo, Xue Steve Liu, Alexandre Drouin, Alexandre Piche, Alexandre Lacoste, Massimo Caccia. At Workshop at the Neural Information Processing Systems (NeurIPS), 2025.

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GitChameleon: Evaluating AI Code Generation Against Python Library Version Incompatibilities. Nizar Islah, Justine Gehring, Diganta Misra, Eilif Muller, Irina Rish, Eilif Benjamin Muller, Massimo Caccia. At Workshop at the Neural Information Processing Systems (NeurIPS), 2025.

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Beyond Naïve Prompting: Strategies for Improved Zero-shot Context-aided Forecasting with LLMs. Arjun Ashok, Andrew Williams, Vincent Zhihao Zheng, Irina Rish, Nicolas Chapados, Étienne Marcotte, Valentina Zantedeschi, Alexandre Drouin. At Workshop at the Neural Information Processing Systems (NeurIPS), 2025.

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The Promise of RL for Autoregressive Image Editing. Saba Ahmadi, Rabiul Awal, Ankur Sikarwar, Amirhossein Kazemnejad, Ge Ya Luo, Juan A. Rodriguez, Sai Rajeswar Mudumba, Siva Reddy, Christopher Pal, Benno Krojer, Aishwarya Agrawal. At Neural Information Processing Systems (NeurIPS), 2025.

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Rendering-Aware Reinforcement Learning for Vector Graphics Generation. Juan A. Rodriguez, Haotian Zhang, Abhay Puri, Rishav Pramanik, Aarash Feizi, Pascal Wichmann, Arnab Mondal, Mohammad Reza Samsami, Rabiul Awal, Perouz Taslakian, Spandana Gella, Sai Rajeswar Mudumba, David Vazquez, Christopher Pal, Marco Pedersoli. At Neural Information Processing Systems (NeurIPS), 2025.

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How to Train Your LLM Web Agent: A Statistical Diagnosis. Dheeraj Vattikonda, Santhoshi Ravichandran, Emiliano Penaloza, Hadi Nekoei, Thibault Le Sellier De Chezelles, Megh Thakkar, Nicolas Gontier, Miguel Muñoz-Mármol, Sahar Omidi Shayegan, Stefania Raimondo, Xue Steve Liu, Alexandre Drouin, Alexandre Piche, Alexandre Lacoste, Massimo Caccia. At Neural Information Processing Systems (NeurIPS), 2025.

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Causal Differentiating Concepts: Interpreting LM Behavior via Causal Representation Learning. Navita Goyal, Hal Daumé III, Alexandre Drouin, Dhanya Sridhar. At Neural Information Processing Systems (NeurIPS), 2025.

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AlignVLM: Bridging Vision and Language Latent Spaces for Multimodal Document Understanding. Ahmed Masry, Juan A. Rodriguez, Tianyu Zhang, Suyuchen Wang, Chao Wang, Aarash Feizi, Akshay Kalkunte, Abhay Puri, Xiangru Jian, Pierre-André Noël, Sathwik Madhusudhan, Marco Pedersoli, Bang Liu, Nicolas Chapados, Yoshua Bengio, Enamul Hoque Prince , Christopher Pal, Issam H. Laradji, David Vazquez, Perouz Taslakian, Spandana Gella, Sai Rajeswar Mudumba. At Neural Information Processing Systems (NeurIPS), 2025.

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Breaking the Bottleneck with DiffuApriel: High-Throughput Diffusion LMs with Mamba Backbone. Vaibhav Singh, Oleksiy Ostapenko, Pierre-André Noël, Torsten Scholak. At arXiv, 2025.

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WebMMU: A Benchmark for Multimodal Multilingual Website Understanding and Code Generation. Sai Rajeswar Mudumba, Christopher Pal, Perouz Taslakian, Spandana Gella, Rabiul Awal, Aarash Feizi, Mahsa Massoud, Zichao Li, Siva Reddy, David Vazquez, Suyuchen Wang. At NOW AI, 2025.

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WebMMU: A Benchmark for Multimodal Multilingual Website Understanding and Code Generation. Rabiul Awal, Mahsa Massoud, Zichao Li, Aarash Feizi, Suyuchen Wang, Christopher Pal, Aishwarya Agrawal, David Vazquez, Perouz Taslakian, Spandana Gella, Sai Rajeswar Mudumba. At Conference on Empirical Methods in Natural Language Processing (EMNLP), 2025.

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Unifying Autoregressive and Diffusion-Based Sequence Generation. Nima Fathi, Torsten Scholak, Pierre-André Noël. At NOW AI, 2025.

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StarVLM ReRank: Better UI Grounding via Enhanced Visual Input and Element Position Perception. Suyuchen Wang, Tianyu Zhang, Ahmed Masry, Christopher Pal, Bang Liu, Perouz Taslakian, Spandana Gella. At NOW AI, 2025.

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StarUI: Learning to Ground Agentic Perception in Desktop GUIs. Aarash Feizi, Shravan Nayak, Kevin Qinghong Lin, Kaixin Li, Rabiul Awal, Xiangru Jian, Juan A. Rodriguez, Nicolas Chapados, David Vazquez, Reihaneh Rabbany, Adriana Romero Soriano, Perouz Taslakian, Christopher Pal, Spandana Gella, Sai Rajeswar Mudumba. At NOW AI, 2025.

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Shifting AI Security to the Left: Design-Time Defenses to Mitigate the Risks of Prompt Injections. Abhay Puri, Kevin Kasa, Kiarash Mohammadi, Georges Belanger Albarran, Mihir Bansal, Yanick Chénard, Marc-Etienne Brunet , Jason Stanley. At NOW AI, 2025.

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Revisiting Fine-Tuning for Task-Oriented Dialogues With ABCD 2.0. Christopher Pal, Matthew Fortier, Derek Chen, Stefania Raimondo. At NOW AI, 2025.

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How to Train Your LLM Web Agent: A Statistical Diagnosis. Dheeraj Vattikonda, Santhoshi Ravichandran, Emiliano Penaloza, Hadi Nekoei, Thibault Le Sellier De Chezelles, Megh Thakkar, Nicolas Gontier, Miguel Muñoz-Mármol, Sahar Omidi Shayegan, Stefania Raimondo, Xue Steve Liu, Alexandre Drouin, Alexandre Piche, Alexandre Lacoste, Massimo Caccia. At NOW AI, 2025.

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Hinting Around: Helping Web Agents Solve Tasks via Hints. Orlando Marquez, Patrice Béchard, Alexandre Drouin, Alexandre Lacoste. At NOW AI, 2025.

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FM2DS: Few-Shot Multimodal Multihop Data Synthesis with Knowledge Distillation for Question Answering. Amirhossein Abaskohi, Spandana Gella, Giuseppe Carenini, Issam H. Laradji. At Conference on Empirical Methods in Natural Language Processing (EMNLP), 2025.

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Faster On-Policy Reinforcement Learning for Long Sequence Generation. Alexandre Piche, Ehsan Kamalloo, Rafael Pardinas, Xiaoyin Chen, Dzmitry Bahdanau. At NOW AI, 2025.

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EDRA: Enterprise Deep Research Agent. Jordan Prince Tremblay, Chris Tyler, Daniel Tremblay, Oleh Shliazhko, Issam H. Laradji, Christopher Pal, Gabriel Huang, Gaurav Sahu, Miguel Muñoz-Mármol, Tianyi Chen, Sébastien Paquet, Alexandre Drouin, Krishnamurthy (Dj) Dvijotham, Jason Stanley, Nicolas Chapados. At NOW AI, 2025.

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DualChronos: Context-Aided Time Series Forecasting with Dual Modalities. Vincent Zhihao Zheng, Arjun Ashok, Andrew Williams, Valentina Zantedeschi, Nicolas Chapados, Alexandre Drouin, Étienne Marcotte. At NOW AI, 2025.

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DRBench: A Comprehensive Benchmark for Enterprise Deep Research. Tianyi Chen, Miguel Muñoz-Mármol, Amirhossein Abaskohi, David Vazquez, Jason Stanley, Oleh Shliazhko, Alexandre Lacoste, Jordan Prince Tremblay, Deepak Venkatanarasimhan, Sunny Chopra, Daniel Tremblay, Chris Tyler, Gabriel Huang, Christopher Pal, Alexandre Drouin, Issam H. Laradji. At NOW AI, 2025.

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ColMate: Contrastive Late Interaction and Masked Text for Multimodal Document Retrieval. Ahmed Masry, Megh Thakkar, Patrice Béchard, Sathwik Madhusudhan, Rabiul Awal, Shambhavi Mishra, Akshay Kalkunte, Enamul Hoque Prince , Spandana Gella, Torsten Scholak, Sai Rajeswar Mudumba. At Conference on Empirical Methods in Natural Language Processing (EMNLP), 2025.

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BigCharts-R1: Enhanced Chart Reasoning With Visual Reinforcement Finetuning. Sai Rajeswar Mudumba, Perouz Taslakian, Ahmed Masry, David Vazquez, Christopher Pal, Abhay Puri, Megh Thakkar, Masoud Hashemi, Khyati Mahajan, Spandana Gella. At NOW AI, 2025.

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Attack What Matters: Integrating Expert Insight and Automation in Threat-Model-Aligned Red Teaming. Kiarash Mohammadi, Abhay Puri, Georges Belanger Albarran, Mihir Bansal, Navdeep Gill, Yanick Chénard, Segan Subramanian, Marc-Etienne Brunet , Jason Stanley. At NOW AI, 2025.

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Apriel-SSM: Converting Pre-Trained Transformer LLMs Into Subquadratic Hybrid Models Through Iterative End-to-End Distillation. Oleksiy Ostapenko, Shambhavi Mishra, Luke Kumar, Denis Kocetkov, Raymond Li, Joel Lamy Poirier, Sébastien Paquet, Torsten Scholak. At NOW AI, 2025.

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Apriel-MTP: Multi-Token Prediction for Faster and More Efficient Language. Raymond Li, Nanda Harishankar Krishna, Oleksiy Ostapenko, Luke Kumar, Torsten Scholak. At NOW AI, 2025.

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AlignVLM: Bridging Vision and Language Latent Spaces for Multimodal Document Understanding. Perouz Taslakian, Sai Rajeswar Mudumba, Spandana Gella, Ahmed Masry, Tianyu Zhang, Juan A. Rodriguez, Chao Wang, Abhay Puri, Xiangru Jian, Pierre-André Noël, Issam H. Laradji. At NOW AI, 2025.

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AgentLab Controller: Level Up Your Web Agent with Step-Through Debugging. Patrice Béchard, Orlando Marquez, Alexandre Drouin, Alexandre Lacoste. At NOW AI, 2025.

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GEOBench-VLM: Benchmarking Vision-Language Models for Geospatial Tasks. Muhammad Sohail Danish, Muhammad Akhtar Munir, Syed Roshaan Ali Shah, Kartik Kuckreja, Fahad Shahbaz Khan, Paolo Fraccaro, Alexandre Lacoste, Salman Khan. At International Conference on Computer Vision (ICCV), 2025.

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Using Scaling Laws for Data Source Utility Estimation in Domain-Specific Pre-Training. Oleksiy Ostapenko, Charles Guille-Escuret, Luke Kumar, Max Tian, Denis Kocetkov, Gopeshh Subbaraj, Raymond Li, Joel Lamy Poirier, Sébastien Paquet, Torsten Scholak. At Conference on Language Modeling Workshops, 2025.

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Unifying Autoregressive and Diffusion-Based Sequence Generation. Nima Fathi, Torsten Scholak, Pierre-André Noël. At Conference on Language Modeling (COLM), 2025.

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Exploring Sparse Adapters for Scalable Merging of Parameter Efficient Experts. Samin Yeasar Arnob , Zhan Su, Minseon Kim, Oleksiy Ostapenko, Riyasat Ohib, Esra’a Saleh, Doina Precup, Lucas Caccia, Alessandro Sordoni. At Conference on Language Modeling (COLM), 2025.

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DoomArena: A framework for Testing AI Agents Against Evolving Security Threats. Léo Boisvert, Mihir Bansal, Chandra Kiran Reddy Evuru, Gabriel Huang, Abhay Puri, Avinandan Bose, Maryam Fazel, Quentin Cappart, Jason Stanley, Alexandre Lacoste, Alexandre Drouin, Krishnamurthy (Dj) Dvijotham. At Conference on Language Modeling (COLM), 2025.

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BiXSE: Improving Dense Retrieval via Probabilistic Graded Relevance Distillation. Christos Tsirigotis, Vaibhav Adlakha, João Monteiro, Aaron Courville, Perouz Taslakian. At Conference on Language Modeling (COLM), 2025.

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BigCharts-R1: Enhanced Chart Reasoning with Visual Reinforcement Finetuning. Ahmed Masry, Abhay Puri, Masoud Hashemi, Juan A. Rodriguez, Megh Thakkar, Khyati Mahajan, Vikas Yadav, Sathwik Tejaswi Madhusudhan, Alexandre Piche, Dzmitry Bahdanau, Christopher Pal, David Vazquez, Enamul Hoque Prince , Perouz Taslakian, Sai Rajeswar Mudumba, Spandana Gella. At Conference on Language Modeling (COLM), 2025.

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Beyond Naïve Prompting: Strategies for Improved Zero-shot Context-aided Forecasting with LLMs. Arjun Ashok, Andrew Williams, Vincent Zhihao Zheng, Irina Rish, Nicolas Chapados, Étienne Marcotte, Valentina Zantedeschi, Alexandre Drouin. At Conference on Language Modeling (COLM), 2025.

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LLMs can learn self-restraint through iterative self-reflection. Alexandre Piche, Aristides Milios, Dzmitry Bahdanau, Christopher Pal. At Transactions on Machine Learning Research (TMLR), 2025.

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Multi-task retriever fine-tuning for domain-specific and efficient RAG. Patrice Béchard, Orlando Marquez. At Knowledge Discovery and Data Mining, 2025.

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Fine-Tune an SLM or Prompt an LLM? The Case of Generating Low-Code Workflows. Orlando Marquez, Patrice Béchard, Emily Chen, Maggie Baird, JingFei Chen. At Knowledge Discovery and Data Mining, 2025.

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AgentAda: Skill-Adaptive Data Analytics for Tailored Insight Discovery. Amirhossein Abaskohi, Amrutha Ramesh, Shailesh Nanisetty, Chirag Goel, David Vazquez, Christopher Pal, Spandana Gella, Giuseppe Carenini, Issam H. Laradji. At Workshop at the Annual Meeting of the Association for Computational Linguistics (ACL), 2025.

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Understanding the Influence of Synthetic Data for Text Embedders. Jacob Mitchell Springer, Vaibhav Adlakha, Siva Reddy, Aditi Raghunathan, Marius Mosbach. At Annual Meeting of the Association for Computational Linguistics (ACL), 2025.

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Silent Sabotage: Injecting Backdoors into AI Agents Through Fine-Tuning. Léo Boisvert, Abhay Puri, Chandra Kiran Reddy Evuru, Joshua Kazdan, Avinandan Bose, Quentin Cappart, Maryam Fazel, Sai Rajeswar Mudumba, Jason Stanley, Nicolas Chapados, Alexandre Drouin, Krishnamurthy (Dj) Dvijotham. At Workshop at the International Conference of Machine Learning (ICML), 2025.

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How to Train Your LLM Web Agent: A Statistical Diagnosis (Oral). Dheeraj Vattikonda, Santhoshi Ravichandran, Emiliano Penaloza, Hadi Nekoei, Thibault Le Sellier De Chezelles, Megh Thakkar, Nicolas Gontier, Miguel Muñoz-Mármol, Sahar Omidi Shayegan, Stefania Raimondo, Xue Steve Liu, Alexandre Drouin, Alexandre Piche, Alexandre Lacoste, Massimo Caccia. At Workshop at the International Conference of Machine Learning (ICML), 2025.

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DoomArena: A framework for Testing AI Agents Against Evolving Security Threats. Léo Boisvert, Abhay Puri, Gabriel Huang, Mihir Bansal, Chandra Kiran Reddy Evuru, Avinandan Bose, Quentin Cappart, Maryam Fazel, Alexandre Lacoste, Alexandre Drouin, Jason Stanley, Krishnamurthy (Dj) Dvijotham. At Workshop at the International Conference of Machine Learning (ICML), 2025.

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Adaptive Diffusion Denoised Smoothing : Certified Robustness via Randomized Smoothing with Differentially Private Guided Denoising Diffusion. Frederick Shpilevskiy, Saiyue Lyu, Krishnamurthy (Dj) Dvijotham, Mathias Lécuyer, Pierre-André Noël. At Workshop at the International Conference of Machine Learning (ICML), 2025.

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UI-Vision: A Desktop-centric GUI Benchmark for Visual Perception and Interaction. Shravan Nayak, Xiangru Jian, Kevin Lin, Juan A. Rodriguez, Motek Kalsi, Nicolas Chapados, Tamer Özsu, Aishwarya Agrawal, David Vazquez, Christopher Pal, Perouz Taslakian, Spandana Gella, Sai Rajeswar Mudumba. At International Conference on Machine Learning (ICML), 2025.

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SafeArena: Evaluating the Safety of Autonomous Web Agents. Ada Tur, Nicholas Meade, Xing Han Lu, Alejandra Zambrano, Arkil Patel, Esin Durmus, Spandana Gella, Karolina Stanczak, Siva Reddy. At International Conference on Machine Learning (ICML), 2025.

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Generalization Bounds via Meta-Learned Model Representations: PAC-Bayes and Sample Compression Hypernetworks. Benjamin Leblanc, Mathieu Bazinet, Nathaniel D'Amours, Alexandre Drouin, Pascal Germain. At International Conference on Machine Learning (ICML), 2025.

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Context is Key: A Benchmark for Forecasting with Essential Textual Information. Andrew Williams, Arjun Ashok, Étienne Marcotte, Valentina Zantedeschi, Jithendaraa Subramanian, Roland Riachi, James Requeima, Alexandre Lacoste, Irina Rish, Nicolas Chapados, Alexandre Drouin. At International Conference on Machine Learning (ICML), 2025.

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WebMMU: A Benchmark for Multimodal Multilingual Website Understanding and Code Generation. Rabiul Awal, Mahsa Massoud, Zichao Li, Aarash Feizi, Suyuchen Wang, Christopher Pal, Aishwarya Agrawal, David Vazquez, Siva Reddy, Juan A. Rodriguez, Perouz Taslakian, Sai Rajeswar Mudumba. At Workshop at the Computer Vision and Pattern Recognition Conference (CVPR), 2025.

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StarVector: Generating Scalable Vector Graphics Code from Images and Text. Juan A. Rodriguez, Abhay Puri, Shubham Agarwal, Issam H. Laradji, Pau Rodriguez, Sai Rajeswar Mudumba, David Vazquez, Christopher Pal, Marco Pedersoli. At Computer Vision and Pattern Recognition (CVPR), 2025.

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Backpropagating from Customer Success. Midam Kim, Fabio Casati, Darrell Penta, Ihnaee Choi, Minyoung Kim. At Conference on Human Factors in Computing Systems (ACM-CHI), 2025.

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The Landscape of Causal Discovery Data: Grounding Causal Discovery in Real-World Applications. Philippe Brouillard, Chandler Squires, Jonas Wahl, Konrad P. Kording, Karen Sachs, Dhanya Sridhar, Alexandre Drouin. At Causal Learning and Reasoning (CLeaR), 2025.

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Sample compression unleashed: New generalization bounds for real valued losses. Mathieu Bazinet, Valentina Zantedeschi, Pascal Germain. At International Conference on Artificial Intelligence and Statistics (AISTATS), 2025.

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Keeping up with dynamic attackers: Certifying robustness to adaptive online data poisoning. Avinandan Bose, Laurent Lessard, Maryam Fazel, Krishnamurthy (Dj) Dvijotham. At International Conference on Artificial Intelligence and Statistics (AISTATS), 2025.

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Fast Convergence of Softmax Policy Mirror Ascent. Reza Asad, Reza Babanezhad, Issam H. Laradji, Nicolas Le Roux, Sharan Vaswani. At International Conference on Artificial Intelligence and Statistics (AISTATS), 2025.

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Prompting with Phonemes: Enhancing LLM Multilinguality for non-Latin Script Languages. Hoang Nguyen, Khyati Mahajan, Vikas Yadav, Philip S. Yu, Masoud Hashemi, Rishabh Maheshwary. At North American Chapter of the Association for Computational Linguistics (NAACL), 2025.

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M2Lingual: Enhancing Multilingual, Multi-Turn Instruction Alignment in Large Language Models. Rishabh Maheshwary, Vikas Yadav, Hoang Nguyen, Khyati Mahajan, Sathwik Tejaswi Madhusudhan. At North American Chapter of the Association for Computational Linguistics (NAACL), 2025.

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Auto-Cypher: Improving LLMs on Cypher generation via LLM-supervised generation-verification framework. Aman Tiwari, Shiva Krishna Reddy Malay, Vikas Yadav, Masoud Hashemi, Sathwik Tejaswi Madhusudhan. At North American Chapter of the Association for Computational Linguistics (NAACL), 2025.

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A Guide To Effectively Leveraging LLMs for Low-Resource Text Summarization: Data Augmentation and Semi-supervised Approaches. Gaurav Sahu, Olga Vechtomova, Issam H. Laradji. At North American Chapter of the Association for Computational Linguistics (NAACL), 2025.

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WebMMU: A Benchmark for Multimodal Multilingual Website Understanding and Code Generation. Rabiul Awal, Mahsa Massoud, Zichao Li, Aarash Feizi, Suyuchen Wang, Christopher Pal, Aishwarya Agrawal, David Vazquez, Siva Reddy, Juan A. Rodriguez, Perouz Taslakian, Spandana Gella, Sai Rajeswar Mudumba. At Workshop at the International Conference of Learning Representation (ICLR), 2025.

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Unifying Autoregressive and Diffusion-Based Sequence Generation. Nima Fathi, Torsten Scholak, Pierre-André Noël. At Workshop at the International Conference of Learning Representation (ICLR), 2025.

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The Landscape of Causal Discovery Data: Grounding Causal Discovery in Real-World Applications. Philippe Brouillard, Chandler Squires, Jonas Wahl, Konrad P. Kording, Karen Sachs, Alexandre Drouin, Dhanya Sridhar. At Workshop at the International Conference of Learning Representation (ICLR), 2025.

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Societal Alignment Frameworks Can Improve LLM Alignment. Karolina Stanczak, Nicholas Meade, Mehar Bhatia, Hattie Zhou, Konstantin Böttinger, Jeremy Barns, Jason Stanley, Nicolas Papernot, Nicolas Chapados, Denis Therien, Timothy P Lillicrap, Ana Marasovic, Sylvie Delacroix, Gillian K Hadfield, Siva Reddy. At Workshop at the International Conference of Learning Representation (ICLR), 2025.

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No, of course I can! Refusal Mechanisms Can Be Exploited Using Harmless Fine-Tuning Data. Joshua Kazdan, Krishnamurthy (Dj) Dvijotham, Sanmi Koyejo. At Workshop at the International Conference of Learning Representation (ICLR), 2025.

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Learning to Defer for Causal Discovery with Imperfect Experts. Oscar Clivio, Divyat Mahajan, Perouz Taslakian, Sara Magliacane, Ioannis Mitliagkas, Valentina Zantedeschi, Alexandre Drouin. At Workshop at the International Conference of Learning Representation (ICLR), 2025.

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Generating a Low-code Complete Workflow via Task Decomposition and RAG. Orlando Marquez, Patrice Béchard. At Conference on AI Engineering (CAIN), 2025.

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AlignVLM: Bridging Vision and Language Latent Spaces for Multimodal Understanding. Ahmed Masry, Juan A. Rodriguez, Tianyu Zhang, Suyuchen Wang, Chao Wang, Aarash Feizi, Akshay Kalkunte, Abhay Puri, Xiangru Jian, Pierre-André Noël, Sathwik Madhusudhan, Marco Pedersoli, Bang Liu, Nicolas Chapados, Yoshua Bengio, Enamul Hoque Prince , Christopher Pal, Issam H. Laradji, David Vazquez, Perouz Taslakian, Spandana Gella, Sai Rajeswar Mudumba. At Workshop at the International Conference of Learning Representation (ICLR), 2025.

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Unpacking Trust Dynamics in the LLM Supply Chain: An Empirical Exploration to Foster Trustworthy LLM Production & Use. Agathe Balayn, Mireia Yurrita, Fanny Rancourt, Fabio Casati, Ujwal Gadiraju. At CHI conference on Human Factors in Computing Systems (CHI), 2025.

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VCR: Visual Caption Restoration. Tianyu Zhang, Suyuchen Wang, Lu Li, Ge Zhang, Perouz Taslakian, Sai Rajeswar Mudumba, Jie Fu, Bang Liu, Yoshua Bengio. At International Conference of Learning Representations (ICLR), 2025.

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Seq-VCR: Preventing Collapse in Intermediate Transformer Representations for Enhanced Reasoning. Md Rifat Arefin, Gopeshh Subbaraj, Nicolas Gontier, Yann LeCun, Irina Rish, Ravid Shwartz-Ziv, Christopher Pal. At International Conference of Learning Representations (ICLR), 2025.

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MMTEB: Massive Multilingual Text Embedding Benchmark. Kenneth Enevoldsen, Isaac Chung, Imene Kerboua, Márton Kardos, Ashwin Mathur, David Stap, Jay Gala, Wissam Siblini, Dominik Krzeminski, Genta Indra Winata, Saba Sturua, Saiteja Utpala, Mathieu Ciancone, Marion Schaeffer, Gabriel Sequeira, Diganta Misra, Shreeya Dhakal, Jonathan Rystrøm, Roman Solomatin, Ömer Çagatan, Akash Kundu, Martin Bernstorff, Shitao Xiao, Akshita Sukhlecha, Bhavish Pahwa, Rafał Poswiata, Kranthi Kiran GV, Shawon Ashraf, Daniel Auras, Björn Plüster, Jan Philipp Harries, Loïc Magne, Isabelle Mohr, Mariya Hendriksen, Dawei Zhu, Hippolyte Gisserot-Boukhlef, Tom Aarsen, Jan Kostkan, Konrad Wojtasik, Taemin Lee, Marek Šuppa, Crystina Zhang, Roberta Rocca, Mohammed Hamdy, Andrianos Michail, John Yang, Manuel Faysse, Aleksei Vatolin, Nandan Thakur, Manan Dey, Dipam Vasani, Pranjal Chitale, Simone Tedeschi, Nguyen Tai, Artem Snegirev, Michael Günther, Mengzhou Xia, Weijia Shi, Xing Han Lu, Jordan Clive, Gayatri Krishnakumar, Anna Maksimova, Silvan Wehrli, Maria Tikhonova, Henil Pancha, Aleksandr Abramov, Malte Ostendorff, Zheng Liu, Simon Clematide, Lester James Miranda, Alena Fenogenova, Guangyu Song, Ruqiya Bin Saf, Wen-Ding Li, Alessia Borghini, Federico Cassano, Hongjin Su, Jimmy Lin, Howard Yen, Lasse Hansen, Sara Hooker, Chenghao Xiao, Vaibhav Adlakha, Orion Weller, Siva Reddy, Niklas Muennighoff. At International Conference of Learning Representations (ICLR), 2025.

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InsightBench: Evaluating Business Analytics Agents Through Multi-Step Insight Generation. Gaurav Sahu, Abhay Puri, Juan A. Rodriguez, Amirhossein Abaskohi, Mohammad (Aaron) Chegini , Alexandre Drouin, Perouz Taslakian, Valentina Zantedeschi, Alexandre Lacoste, David Vazquez, Nicolas Chapados, Christopher Pal, Sai Rajeswar Mudumba, Issam H. Laradji. At International Conference of Learning Representations (ICLR), 2025.

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BigDocs: An Open and Permissively-Licensed Dataset for Training Multimodal Models on Document and Code Tasks. Juan A. Rodriguez, Xiangru Jian, Siba Smarak Panigrahi, Tianyu Zhang, Aarash Feizi, Abhay Puri, Akshay Kalkunte, Francois Savard, Ahmed Masry, Shravan Nayak, Rabiul Awal, Mahsa Massoud, Amirhossein Abaskohi, Zichao Li, Suyuchen Wang, Pierre-André Noël, Mats L. Richter, Saverio Vadacchino, Shubham Agarwal, Sanket Biswas, Sara Shanian, Ying Zhang, Sathwik Tejaswi Madhusudhan, João Monteiro, Krishnamurthy (Dj) Dvijotham, Torsten Scholak, Nicolas Chapados, Sepideh Kharaghani, Sean Hughes, Tamer Özsu, Siva Reddy, Marco Pedersoli, Yoshua Bengio, Christopher Pal, Issam H. Laradji, Spandana Gella, Perouz Taslakian, David Vazquez, Sai Rajeswar Mudumba. At International Conference of Learning Representations (ICLR), 2025.

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LitLLMs, LLMs for Literature Review: Are We There Yet?. Gaurav Sahu, Abhay Puri, Shubham Agarwal, Issam H. Laradji, Krishnamurthy (Dj) Dvijotham, Jason Stanley, Laurent Charlin, Christopher Pal. At Transactions on Machine Learning Research (TMLR), 2025.

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Pruning Sparse Tensor Neural Networks Enables Deep Learning for 3D Ultrasound Localization Microscopy. Brice Rauby, Paul Xing, Jonathan Porée, Maxime Gasse, Jean Provost. At IEEE Transactions on Image Processing (IEEE TIP), 2025.

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Deep Learning in Ultrasound localization Microscopy: Applications and perspectives. Brice Rauby, Paul Xing, Maxime Gasse, Jean Provost. At IEEE Transactions on Ultrasonics, Ferroelectrics, and Frequency Control (IEEE TUFFC), 2025.

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EarthView: A Large Scale Remote Sensing Dataset for Self-Supervision. Diego Velazquez, Pau Rodriguez, Sergio Alonso, Josep M. Gonfaus, Jordi Gonzalez, Gerardo Richarte, Javier Marin, Yoshua Bengio, Alexandre Lacoste. At Workshop at the Winter Conference on Applications of Computer Vision (WACV), 2025.

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StarVector: Generating Scalable Vector Graphics Code from Images and Text. Juan A. Rodriguez, Abhay Puri, Shubham Agarwal, Issam H. Laradji, Pau Rodriguez, Sai Rajeswar Mudumba, David Vazquez, Christopher Pal, Marco Pedersoli. At AAAI Demos, 2025.

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Bidding in day-ahead electricity markets: A dynamic programming framework. Jérôme De Boeck, Bernard Fortz, Martine Labbé, Étienne Marcotte, Patrice Marcotte, Gilles Savard. At Computers & Operations Research (COR), 2025.

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Do LLMs Know When to NOT Answer? Investigating Abstention Abilities of Large Language Models. Nishanth Madhusudhan, Sathwik Tejaswi Madhusudhan, Vikas Yadav, Masoud Hashemi. At International Conference on Computational Linguistics (COLING), 2025.

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Ecma TC56 NLIP (Natural Language Interaction Protocol). Sean Hughes. At Ecma TC56 NLIP (Natural Language Interaction Protocol), 2025.

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The BrowserGym Ecosystem for Web Agent Research. Thibault Le Sellier De Chezelles, Maxime Gasse, Alexandre Drouin, Massimo Caccia, Léo Boisvert, Megh Thakkar, Tom Marty, Rim Assouel, Sahar Omidi Shayegan, Siva Reddy, Quentin Cappart, Graham Neubig, Nicolas Chapados, Alexandre Lacoste. At Transactions on Machine Learning Research (TMLR), 2025.

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Few-shot Learning for Sign Language Recognition with Embedding Propagation. Amjad Alsulami,, KHAWLAH BAJBAA, Hamzah Luqman, Issam H. Laradji. At Nafath, 2024.

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XC-Cache: Cross-Attending to Cached Context for Efficient LLM Inference. João Monteiro, Étienne Marcotte, Pierre-André Noël, Valentina Zantedeschi, David Vazquez, Nicolas Chapados, Christopher Pal, Perouz Taslakian. At Workshop at the Neural Information Processing Systems (NeurIPS), 2024.

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WorkArena++: Towards Compositional Planning and Reasoning-based Common Knowledge Work Tasks. Léo Boisvert, Megh Thakkar, Maxime Gasse, Massimo Caccia, Thibault Le Sellier De Chezelles, Quentin Cappart, Nicolas Chapados, Alexandre Lacoste, Alexandre Drouin. At NeurIPS Datasets and Benchmarks Track (NeurIPS Datasets), 2024.

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VCR: Visual Caption Restoration. Tianyu Zhang, Suyuchen Wang, Lu Li, Ge Zhang, Perouz Taslakian, Sai Rajeswar Mudumba, Jie Fu, Bang Liu, Yoshua Bengio. At Workshop at the Neural Information Processing Systems (NeurIPS), 2024.

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Sample compression unleashed: New generalization bounds for real valued losses. Mathieu Bazinet, Valentina Zantedeschi, Pascal Germain. At Workshop at the Neural Information Processing Systems (NeurIPS), 2024.

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Sample Compression Hypernetworks: From Generalization Bounds to Meta-Learning. Benjamin Leblanc, Mathieu Bazinet, Nathaniel D'Amours, Pascal Germain, Alexandre Drouin. At Workshop at the Neural Information Processing Systems (NeurIPS), 2024.

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Representing Positional Information in Generative World Models for Object Manipulation. Stefano Ferraro, Pietro Mazzaglia, Tim Verbelen, Sai Rajeswar Mudumba. At Workshop at the Neural Information Processing Systems (NeurIPS), 2024.

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RepLiQA: A Question-Answering Dataset for Benchmarking LLMs on Unseen Reference Content. João Monteiro, Pierre-André Noël, Étienne Marcotte, Sai Rajeswar Mudumba, Valentina Zantedeschi, David Vazquez, Nicolas Chapados, Christopher Pal, Perouz Taslakian. At NeurIPS Datasets and Benchmarks Track (NeurIPS Datasets), 2024.

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Multimodal foundation world models for generalist embodied agents. Pietro Mazzaglia, Tim Verbelen, Bart Dhoedt, Aaron Courville, Sai Rajeswar Mudumba. At Neural Information Processing Systems (NeurIPS), 2024.

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Fine-Tuning Web Agents: It Works, But It's Trickier Than You Think. Massimo Caccia, Megh Thakkar, Léo Boisvert, Thibault Le Sellier De Chezelles, Alexandre Piche, Nicolas Chapados, Alexandre Drouin, Maxime Gasse, Alexandre Lacoste. At Workshop at the Neural Information Processing Systems (NeurIPS), 2024.

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Fast Convergence of Softmax Policy Mirror Ascent for Bandits & Tabular MDPs. Issam H. Laradji, Reza Asad, Sharan Vaswani. At Workshop at the Neural Information Processing Systems (NeurIPS), 2024.

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Expecting The Unexpected: Towards Broad Out-Of-Distribution Detection. Charles Guille-Escuret, Pierre-André Noël, Ioannis Mitliagkas, David Vazquez, João Monteiro. At NeurIPS Datasets and Benchmarks Track (NeurIPS Datasets), 2024.

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Evaluating Interventional Reasoning Capabilities of Large Language Models. Tejas Kasetty, Divyat Mahajan, Gintare Karolina Dziugaite, Alexandre Drouin, Dhanya Sridhar. At Workshop at the Neural Information Processing Systems (NeurIPS), 2024.

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Context is Key: A Benchmark for Forecasting with Essential Textual Information. Andrew Williams, Arjun Ashok, Étienne Marcotte, Valentina Zantedeschi, Jithendaraa Subramanian, Roland Riachi, James Requeima, Alexandre Lacoste, Irina Rish, Nicolas Chapados, Alexandre Drouin. At Workshop at the Neural Information Processing Systems (NeurIPS), 2024.

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BlockLLM: Memory-Efficient Adaptation of LLMs by Selecting and Optimizing the Right Coordinate Blocks. Issam H. Laradji, Amrutha Ramesh, Mark Schmidt. At Workshop at the Neural Information Processing Systems (NeurIPS), 2024.

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BigDocs: A Permissively-Licensed Dataset for Training Vision-Language Models on Document and Code Tasks. Juan A. Rodriguez, Xiangru Jian, Siba Smarak Panigrahi, Tianyu Zhang, Aarash Feizi, Abhay Puri, Akshay Kalkunte, Francois Savard, Amirhossein Abaskohi, Ahmed Masry, Shravan Nayak, Mahsa Massoud, Rabiul Awal, Pierre-André Noël, Mats L. Richter, Saverio Vadacchino, Shubham Agarwal, Sanket Biswas, Ying Zhang, Sathwik Tejaswi Madhusudhan, João Monteiro, Krishnamurthy (Dj) Dvijotham, Torsten Scholak, Nicolas Chapados, Sean Hughes, Tamer Özsu, Aishwarya Agrawal, Marco Pedersoli, Christopher Pal, Perouz Taslakian, David Vazquez, Issam H. Laradji, Spandana Gella, Sai Rajeswar Mudumba. At Workshop at the Neural Information Processing Systems (NeurIPS), 2024.

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AgentMerge: Enhancing Generalization in Fine-Tuned LLM Agents. Megh Thakkar, Léo Boisvert, Thibault Le Sellier De Chezelles, Alexandre Piche, Maxime Gasse, Alexandre Lacoste, Massimo Caccia. At Workshop at the Neural Information Processing Systems (NeurIPS), 2024.

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Context is Key: A Benchmark for Forecasting with Essential Textual Information. Andrew Williams, Arjun Ashok, Étienne Marcotte, Valentina Zantedeschi, Jithendaraa Subramanian, Roland Riachi, James Requeima, Alexandre Lacoste, Irina Rish, Nicolas Chapados, Alexandre Drouin. At Foundation Models for Time Series, 2024.

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XC-Cache: Cross-Attending to Cached Context for Efficient LLM Inference. João Monteiro, Étienne Marcotte, Pierre-André Noël, Valentina Zantedeschi, David Vazquez, Nicolas Chapados, Christopher Pal, Perouz Taslakian. At Conference on Empirical Methods in Natural Language Processing (EMNLP), 2024.

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Curry-DPO: Enhancing Alignment using Curriculum Learning & Ranked Preferences. Pulkit Pattnaik, Rishabh Maheshwary, Kelechi Ogueji, Vikas Yadav, Sathwik Tejaswi Madhusudhan. At Conference on Empirical Methods in Natural Language Processing (EMNLP), 2024.

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Change Is the Only Constant: Dynamic LLM Slicing based on Layer Redundancy. Razvan-Gabriel Dumitru, Paul-Ioan Clotan, Vikas Yadav, Darius Peteleaza, Mihai Surdeanu. At Conference on Empirical Methods in Natural Language Processing (EMNLP), 2024.

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Representing Positional Information in Generative World Models for Object Manipulation. Stefano Ferraro, Pietro Mazzaglia, Tim Verbelen, Sai Rajeswar Mudumba. At Learning Effective Abstractions for Planning, 2024.

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Fine-Tuning Web Agents: It Works, But It's Trickier Than You Think. Massimo Caccia, Megh Thakkar, Léo Boisvert, Thibault Le Sellier De Chezelles, Alexandre Piche, Nicolas Chapados, Alexandre Drouin, Maxime Gasse, Alexandre Lacoste. At NOW AI Conference (NOWAI), 2024.

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TACTIS-2: Better, Faster, Simpler Attentional Copulas for Multivariate Time Series. Arjun Ashok, Étienne Marcotte, Valentina Zantedeschi, Nicolas Chapados, Alexandre Drouin. At Montreal AI Symposium (MAIS), 2024.

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Context is Key: A Benchmark for Forecasting with Essential Textual Information. Andrew Williams, Arjun Ashok, Étienne Marcotte, Valentina Zantedeschi, Jithendaraa Subramanian, Roland Riachi, James Requeima, Alexandre Lacoste, Irina Rish, Nicolas Chapados, Alexandre Drouin. At Montreal AI Symposium (MAIS), 2024.

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An Ecosystem for Web Agents: WorkArena, BrowserGym, AgentLab and more. Alexandre Lacoste, Maxime Gasse, Thibault Le Sellier De Chezelles, Massimo Caccia, Léo Boisvert, Megh Thakkar, Alexandre Drouin, Nicolas Chapados. At Montreal AI Symposium (MAIS), 2024.

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LLM2Vec: Large Language Models Are Secretly Powerful Text Encoders. Parishad BehnamGhader, Vaibhav Adlakha, Marius Mosbach, Dzmitry Bahdanau, Nicolas Chapados, Siva Reddy. At Conference on Language Modeling (COLM), 2024.

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Performance Control in Early Exiting to Deploy Large Models at the Same Cost of Smaller Ones. Mehrnaz Mofakhami, Reza Bayat, Ioannis Mitliagkas, João Monteiro, Valentina Zantedeschi. At Workshop at the International Conference of Machine Learning (ICML), 2024.

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Multimodal foundation world models for generalist embodied agents. Pietro Mazzaglia, Tim Verbelen, Bart Dhoedt, Aaron Courville, Sai Rajeswar Mudumba. At Workshop at the International Conference of Machine Learning (ICML), 2024.

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WorkArena: How Capable are Web Agents at Solving Common Knowledge Work Tasks?. Alexandre Drouin, Maxime Gasse, Massimo Caccia, Issam H. Laradji, Manuel Del Verme, Tom Marty, Léo Boisvert, Megh Thakkar, Quentin Cappart, David Vazquez, Nicolas Chapados, Alexandre Lacoste. At International Conference on Machine Learning (ICML), 2024.

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A Sparsity Principle for Partially Observable Causal Representation Learning. Danru Xu, Dingling Yao, Sébastien Lachapelle, Perouz Taslakian, Sara Magliacane, Francesco Locatello, Julius von Kügelgen. At International Conference on Machine Learning (ICML), 2024.

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PAG-LLM: Paraphrase and Aggregate with Large Language Models for Minimizing Intent Classification Errors. Vikas Yadav, Zheng Tang, Vijay Srinivasan. At nternational ACM SIGIR Conference on Research and Development in Information Retrieval, 2024.

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EquiAdapt: Equivariant Adaptation of Large Pretrained Models. Arnab Mondal, Siba Smarak Panigrahi, Siamak Ravanbakhsh, Sai Rajeswar Mudumba. At Workshop at the Computer Vision and Pattern Recognition Conference (CVPR), 2024.

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Reducing hallucination in structured outputs via Retrieval-Augmented Generation. Patrice Béchard, Orlando Marquez. At North American Chapter of the Association for Computational Linguistics (NAACL), 2024.

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Evaluating In-Context Learning of Libraries for Code Generation. Arkil Patel, Siva Reddy, Dzmitry Bahdanau, Pradeep Dasigi. At North American Chapter of the Association for Computational Linguistics (NAACL), 2024.

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Exploring validation metrics for offline model-based optimisation with diffusion models. Christopher Beckham, Alexandre Piche, David Vazquez, Christopher Pal. At Transactions on Machine Learning Research (TMLR), 2024.

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Understanding Stakeholders' Perceptions and Needs Across the LLM Supply Chain. Agathe Balayn, Lorenzo Corti, Fanny Rancourt, Fabio Casati, Ujwal Gadiraju. At Conference on Human Factors in Computing Systems (ACM-CHI), 2024.

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Investigating Interaction Friction in Generative AI: Improving User Experience and Decision-Making. Pauline Malaguti, Alexander J. Karran, Di Le, Hayley Mortin, Constantinos K. Coursaris, Sylvain Sénécal, Pierre-Majorique Léger. At Special Interest Group On Computer-Human Interaction, 2024.

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An Empirical Exploration of Trust Dynamics in LLM Supply Chains. Agathe Balayn, Mireia Yurrita, Fanny Rancourt, Fabio Casati, Ujwal Gadiraju. At Conference on Human Factors in Computing Systems (ACM-CHI), 2024.

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Workflow discovery in low data regimes. Amine El Hattami, Issam H. Laradji, Stefania Raimondo, David Vazquez, Pau Rodriguez, Christopher Pal. At International Conference of Learning Representations (ICLR), 2024.

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WorkArena: How Capable are Web Agents at Solving Common Knowledge Work Tasks?. Alexandre Drouin, Maxime Gasse, Massimo Caccia, Issam H. Laradji, Manuel Del Verme, Tom Marty, David Vazquez, Nicolas Chapados, Alexandre Lacoste. At Workshop at the International Conference of Learning Representation (ICLR), 2024.

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TACTIS-2: Better, Faster, Simpler Attentional Copulas for Multivariate Time Series. Arjun Ashok, Étienne Marcotte, Valentina Zantedeschi, Nicolas Chapados, Alexandre Drouin. At International Conference of Learning Representations (ICLR), 2024.

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Self-evaluation and self-prompting to improve the reliability of LLMs. Alexandre Piche, Aristides Milios, Dzmitry Bahdanau, Christopher Pal. At Workshop at the International Conference of Learning Representation (ICLR), 2024.

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Multi-View Causal Representation Learning with Partial Observability. Dingling Yao, Danru Xu, Perouz Taslakian, Sébastien Lachapelle, Sara Magliacane, Julius von Kügelgen, Francesco Locatello. At International Conference of Learning Representations (ICLR), 2024.

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IntentGPT: Few-shot Intent Discovery with Large Language Models. Juan A. Rodriguez, Nicholas Botzer, David Vazquez, Christopher Pal, Marco Pedersoli, Issam H. Laradji. At Workshop at the International Conference of Learning Representation (ICLR), 2024.

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Efficient Dynamics Modeling in Interactive Environments with Koopman Theory. Arnab Mondal, Siba Smarak Panigrahi, Siamak Ravanbakhsh, Sai Rajeswar Mudumba. At International Conference of Learning Representations (ICLR), 2024.

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Generalization bounds with arbitrary complexity measures. Paul Viallard, Remi Emonet, Emilie Morvant, Amaury Habrard, Valentina Zantedeschi. At International Conference on Artificial Intelligence and Statistics (AISTATS), 2024.

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Evaluating Correctness and Faithfulness of Instruction-Following Models for Question Answering. Vaibhav Adlakha, Parishad BehnamGhader, Xing Han Lu, Nicholas Meade, Siva Reddy. At Transactions of the Association for Computational Linguistics (TACL), 2024.

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Towards Disentangled High-level Causal Explanations in Text. Navita Goyal, Hal Daumé III, Alexandre Drouin, Dhanya Sridhar. At Mid-Atlantic Student Colloquium on Speech, Language and Learning, 2024.

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The BigCode Project Governance Card. Sean Hughes, Harm de Vries, Jennifer Robinson, Carlos Muñoz Ferrandis, Loubna Ben Allal, Leandro von Werra, Jennifer Ding, Sébastien Paquet, Yacine Jernite. At ArXiv, 2024.

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TapeAgents: a Holistic Framework for Agent Development and Optimization. Dzmitry Bahdanau, Nicolas Gontier, Gabriel Huang, Ehsan Kamalloo, Rafael Pardinas, Alexandre Piche, Torsten Scholak, Oleh Shliazhko, Jordan Prince Tremblay, Karam Ghanem, Soham Parikh, Mitul Tiwari, Quaizar Vohra. At ArXiv, 2024.

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StarVector: Generating Scalable Vector Graphics Code from Images and Text. Juan A. Rodriguez, Shubham Agarwal, Abhay Puri, Issam H. Laradji, Sai Rajeswar Mudumba, Pau Rodriguez, David Vazquez, Christopher Pal, Marco Pedersoli. At ArXiv, 2024.

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StarCoder 2 and The Stack v2: The Next Generation. Anton Lozhkov, Raymond Li, Loubna Ben Allal, Federico Cassano, Joel Lamy Poirier, Nouamane Tazi, Ao Tang, Dmytro Pykhtar, Jiawei Liu, Yuxiang Wei, Tianyang Liu, Max Tian, Denis Kocetkov, Arthur Zucker, Younes Belkada, Zijian Wang, Dmitry Abulkhanov, Indraneil Paul, Zhuang Li, Wen-Ding Li, Megan Risdal, Jia Li, Terry Yue Zhuo, Nii Osae Osae Dade, Lucas Krauß, Naman Jain, Yixuan Su, Xuanli He, Edoardo Abati, Yekun Chai, Xiangru Tang, Christopher Akiki, Chenghao Mou, Binyuan Hui, Nicolas Patry, Canwen Xu, Julian McAuley, Han Hu, Torsten Scholak, Sébastien Paquet, Jennifer Robinson, Carolyn Jane Anderson, Nicolas Chapados, Mostofa Patwary, Nima Tajbakhsh, Yacine Jernite, Carlos Muñoz Ferrandis, Lingming Zhang, Sean Hughes, Thomas Wolf , Arjun Guha, Leandro von Werra, Harm de Vries, Alex Gu, Armel Zebaze, Evgenii Zheltonozhskii, Jian Zhu, Manan Dey, Marc Marone, Mayank Mishra, Muhtasham Oblokulov, Olivier Dehaene, Qian Liu, Tri Dao, Wenhao Yu, Niklas Muennighoff. At ArXiv, 2024.

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Pruning Sparse Tensor Neural Networks Enables Deep Learning for 3D Ultrasound Localization Microscopy. Brice Rauby, Paul Xing, Jonathan Porée, Maxime Gasse, Jean Provost. At ArXiv, 2024.

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NNetscape Navigator: Complex Demonstrations for Web Agents Without a Demonstrator. Shikhar Murty, Hao Zhu, Dzmitry Bahdanau, Chris Manning. At ArXiv, 2024.

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MixSumm: Topic-based Data Augmentation using LLMs for Low-resource Extractive Text Summarization. Issam H. Laradji, Gaurav Sahu. At ArXiv, 2024.

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M-RewardBench: Evaluating Reward Models in Multilingual Settings. Srishti Gureja, Lester James V. Miranda, Shayekh Bin Islam, Rishabh Maheshwary, Drishti Sharma, Gusti Winata, Nathan Lambert, Sebastian Ruder, Sara Hooker, Marzieh Fadaee. At ArXiv, 2024.

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LitLLM: A Toolkit for Scientific Literature Review. Shubham Agarwal, Abhay Puri, Issam H. Laradji, Laurent Charlin, Christopher Pal. At ArXiv, 2024.

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Layered gradient accumulation and modular pipeline parallelism: fast and efficient training of large language models. Joel Lamy Poirier. At ArXiv, 2024.

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Layer-Wise Quantization: A Pragmatic and Effective Method for Quantizing LLMs Beyond Integer Bit-Levels. Razvan-Gabriel Dumitru, Vikas Yadav, Rishabh Maheshwary, Paul-Ioan Clotan, Sathwik Tejaswi Madhusudhan, Mihai Surdeanu. At ArXiv, 2024.

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Language Decision Transformers with Exponential Tilt for Interactive Text Environments. Nicolas Gontier, Pau Rodriguez, Issam H. Laradji, David Vazquez, Christopher Pal. At ArXiv, 2024.

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InCoRo: In-Context Learning for Robotics Control with Feedback Loops. Jiaquiang Ye Zhu, Carla Gomez, David Vazquez, Michal Drozdzal. At ArXiv, 2024.

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FM2DS: Few-Shot Multimodal Multihop Data Synthesis with Knowledge Distillation for Question Answering. Issam H. Laradji, Amirhossein Abaskohi, Giuseppe Carenini, Spandana Gella. At ArXiv, 2024.

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Automatic Data Augmentation Learning using Bilevel Optimization for Histopathological Images. Saypraseuth Mounsaveng, Issam H. Laradji, David Vazquez, Marco Pedersoli, Ismail Ben Ayed. At ArXiv, 2024.

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3rd Continual Learning Workshop Challenge on Egocentric Category and Instance Level Object Understanding. Lorenzo Pellegrini, Chenchen Zhu, Fanyi Xiao, Zhicheng Yan, Antonio Carta, Matthias De Lange, Vincenzo Lomonaco, Roshan Sumbaly, Pau Rodriguez, David Vazquez. At ArXiv, 2024.

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The Unsolved Challenges of LLMs in Open-Ended Web Tasks: A Case Study. Rim Assouel, Tom Marty, Massimo Caccia, Issam H. Laradji, Alexandre Drouin, Sai Rajeswar Mudumba, Hector Palacios, Quentin Cappart, David Vazquez, Nicolas Chapados, Maxime Gasse, Alexandre Lacoste. At Workshop at the Neural Information Processing Systems (NeurIPS), 2023.

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Surrogate Minimization: An Optimization Algorithm for Training Large Neural Networks with Model Parallelism. Reza Asad, Reza Babanezhad, Issam H. Laradji, Nicolas Le Roux, Sharan Vaswani. At Workshop at the Neural Information Processing Systems (NeurIPS), 2023.

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Multi-View Causal Representation Learning with Partial Observability. Dingling Yao, Danru Xu, Perouz Taslakian, Sébastien Lachapelle, Sara Magliacane, Julius von Kügelgen, Francesco Locatello. At Workshop at the Neural Information Processing Systems (NeurIPS), 2023.

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Lag-Llama: A Foundation Model for Probabilistic Time Series Forecasting. Kashif Rasul, Arjun Ashok, Marin Bilos, Andrew Williams, Arian Khorasani, George Adamopoulos, Rishika Bhagwatkar, Hena Ghonia, Nadhir Hassen, Anderson Schneider, Sahil Garg, Alexandre Drouin, Nicolas Chapados, Yuriy Nevmyvaka, Irina Rish. At Workshop at the Neural Information Processing Systems (NeurIPS), 2023.

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Capture the Flag: Uncovering Data Insights with Large Language Models. Issam H. Laradji, Perouz Taslakian, Sai Rajeswar Mudumba, Valentina Zantedeschi, Alexandre Lacoste, Nicolas Chapados, David Vazquez, Christopher Pal, Alexandre Drouin. At Workshop at the Neural Information Processing Systems (NeurIPS), 2023.

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A Sparsity Principle for Partially Observable Causal. Danru Xu, Dingling Yao, Perouz Taslakian, Sébastien Lachapelle, Julius von Kügelgen, Francesco Locatello, Sara Magliacane. At Workshop at the Neural Information Processing Systems (NeurIPS), 2023.

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GEO-Bench: Toward Foundation Models for Earth Monitoring. Alexandre Lacoste, Nils Lehmann, Hannah Kerner, Hamed Alemohammad, Björn Lütjens, Jeremy Irvin, David Dao, Pau Rodriguez, Alexandre Drouin, David Vazquez, Evan D. Sherwin. At NeurIPS Datasets and Benchmarks Track (NeurIPS Datasets), 2023.

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TK-KNN: A Balanced Distance-Based Pseudo Labeling Approach for Semi-Supervised Intent Classification. Nicholas Botzer, David Vazquez, Tim Weninger, Issam H. Laradji. At Conference on Empirical Methods in Natural Language Processing (EMNLP), 2023.

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PromptMix: A Class Boundary Augmentation Method for Large Language Model Distillation. Gaurav Sahu, Olga Vechtomova, Dzmitry Bahdanau, Issam H. Laradji. At Conference on Empirical Methods in Natural Language Processing (EMNLP), 2023.

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MAGNIFICO: Evaluating the In-Context Learning Ability of Large Language Models to Generalize to Novel Interpretations. Arkil Patel, Satwik Bhattamishra, Siva Reddy, Dzmitry Bahdanau. At Conference on Empirical Methods in Natural Language Processing (EMNLP), 2023.

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LLM aided semi-supervision for efficient Extractive Dialog Summarization. Nishant Mishra, Gaurav Sahu, Iacer Calixto, Ameen Abu-Hanna, Issam H. Laradji. At Conference on Empirical Methods in Natural Language Processing (EMNLP), 2023.

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Group Robust Classification Without Any Group Information. Christos Tsirigotis, João Monteiro, Pau Rodriguez, Aaron Courville. At Conference on Neural Information Processing Systems (NeurIPS), 2023.

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Equivariant Adaptation of Large Pre-Trained Models. Arnab Mondal, Siba Smarak Panigrahi, Sai Rajeswar Mudumba, Siamak Ravanbakhsh. At Conference on Neural Information Processing Systems (NeurIPS), 2023.

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Egocentric Planning for Scalable Embodied Task Achievement. Xiaotian Liu, Hector Palacios, Christian Muise. At Conference on Neural Information Processing Systems (NeurIPS), 2023.

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CADet: Fully Self-Supervised Out-Of-Distribution Detection With Contrastive Learning. Charles Guille-Escuret, Pau Rodriguez, David Vazquez, Ioannis Mitliagkas, João Monteiro. At Conference on Neural Information Processing Systems (NeurIPS), 2023.

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Are Diffusion Models Vision-And-Language Reasoners?. Benno Krojer, Elinor Poole-Dayan, Vikram Voleti, Christopher Pal, Siva Reddy. At Conference on Neural Information Processing Systems (NeurIPS), 2023.

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On Stochastic Mirror Descent: Convergence Analysis and Adaptive Variants. Ryan D'Orazio, Nicolas Loizou, Issam H. Laradji, Ioannis Mitliagkas. At Transactions on Machine Learning Research (TMLR), 2023.

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Efficient Dynamics Modeling in Interactive Environments with Koopman Theory. Arnab Mondal, Sai Rajeswar Mudumba, Siamak Ravanbakhsh, Siba Smarak Panigrahi. At European Workshop on Reinforcement Learning (EWRL), 2023.

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Bridging the Gap Between Target Networks and Functional Regularization. Alexandre Piche, Valentin Thomas, Joseph Marino, Gian Maria Marconi, Mohammad Emtiyaz Khan, Christopher Pal. At Transactions on Machine Learning Research (TMLR), 2023.

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Using Confounded Data in Latent Model-Based Reinforcement Learning. Maxime Gasse, Damien Grasset, Pierre-Yves Oudeyer, Guillaume Gaudron. At Transactions on Machine Learning Research (TMLR), 2023.

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Invariant Causal Set Covering Machines. Thibaud Godon, Baptiste Bauvin, Pascal Germain, Jacques Corbeil, Alexandre Drouin. At Workshop on Spurious Correlations, Invariance, and Stability (ICML), 2023.

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Workflow discovery in low data regimes. Amine El Hattami, Issam H. Laradji, Stefania Raimondo, David Vazquez, Pau Rodriguez, Christopher Pal. At Workshop at the International Conference on Machine Learning (ICML), 2023.

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OC-NMN: Object-centric Compositional Neural Module Network for Generative Visual Analogical Reasoning. Rim Assouel, Pau Rodriguez, Perouz Taslakian, David Vazquez, Yoshua Bengio. At Workshop at the International Conference on Machine Learning (ICML), 2023.

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Explaining Graph Neural Networks Using Interpretable Local Surrogates. Perouz Taslakian, Guillaume Rabusseau, Farzaneh Heidari. At Workshop at the International Conference on Machine Learning (ICML), 2023.

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Causal Discovery with Language Models as Imperfect Experts. Stephanie Long, Alexandre Piche, Valentina Zantedeschi, Tibor Schuster, Alexandre Drouin. At Workshop on Structured Probabilistic Inference & Generative Modeling (ICML), 2023.

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Benchmarking Bayesian Causal Discovery Methods for Downstream Treatment Effect Estimation. Chris Chinenye Emezue, Alexandre Drouin, Tristan Deleu, Stefan Bauer , Yoshua Bengio. At Workshop on Structured Probabilistic Inference & Generative Modeling (ICML), 2023.

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Regions of Reliability in the Evaluation of Multivariate Probabilistic Forecasts. Étienne Marcotte, Valentina Zantedeschi, Alexandre Drouin, Nicolas Chapados. At International Conference on Machine Learning (ICML), 2023.

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Mastering the Unsupervised Reinforcement Learning Benchmark from Pixels. Sai Rajeswar Mudumba, Pietro Mazzaglia, Tim Verbelen, Alexandre Piche, Bart Dhoedt, Aaron Courville, Alexandre Lacoste. At International Conference on Machine Learning (ICML), 2023.

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Let's Make Block Coordinate Descent Converge Faster: Faster Greedy Rules, Message-Passing, Active-Set Complexity, and Superlinear Convergence. julie nutini, Issam H. Laradji, Mark Schmidt. At International Conference on Machine Learning (ICML), 2023.

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RepoFusion: Training Code Models to Understand Your Repository. Disha Shrivastava, Denis Kocetkov, Harm de Vries, Dzmitry Bahdanau, Torsten Scholak. At ArXiv, 2023.

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Breadth-First Pipeline Parallelism. Joel Lamy Poirier. At Conference on Machine Learning and Systems (MLSYS), 2023.

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Affinity Learning With Blind-spot Self-supervision for Image Denoising. Yuhongze Zhou, Liguang Zhou, Issam H. Laradji, Tin Lun Lam, Yangsheng Xu. At International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2023.

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StarCoder: may the source be with you!. Raymond Li, Loubna Ben Allal, Yangtian Zi, Denis Kocetkov, Chenghao Mou, Christopher Akiki, Jia Li, Jenny Chim, Terry Yue Zhuo, Thomas Wang, Mishig Davaadorj, João Monteiro, Oleh Shliazhko, Nicolas Gontier, Nicholas Meade, Ming-Ho Yee, Logesh Kumar Umapathi, Benjamin Lipkin, Zhiruo Wang, Rudra Murthy, Jason Stillerman, Siva Sankalp Patel, Dmitry Abulkhanov, Marco Zocca, Zhihan Zhang, Nour Fahmy, Urvashi Bhattacharyya, Swayam Singh, Sasha Luccioni, Paulo Villegas, Maxim Kunakov, Fedor Zhdanov, Manuel Romero, Tony Lee, Nadav Timor, Jennifer Ding, Claire Schlesinger, Hailey Schoelkopf, Jan Ebert, Jennifer Robinson, Carolyn Jane Anderson, Brendan Dolan-Gavitt, Danish Contractor, Siva Reddy, Daniel Fried, Dzmitry Bahdanau, Yacine Jernite, Carlos Muñoz Ferrandis, Sean Hughes, Thomas Wolf , Arjun Guha, Leandro von Werra, Harm de Vries, Joel Lamy Poirier, Alex Gu, Armel Zebaze, Jian Zhu, Manan Dey, Marc Marone, Mayank Mishra, Muhtasham Oblokulov, Olivier Dehaene, Qian Liu, Tri Dao, Wenhao Yu, Niklas Muennighoff. At Transactions on Machine Learning Research (TMLR), 2023.

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Explaining explainable AI. Nicolas Chapados, Richard Zuroff. At Book Chapter, 2023.

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BigCode Governance Card. Sean Hughes, Harm de Vries, Jennifer Robinson, Carlos Muñoz Ferrandis, Loubna Ben Allal, Leandro von Werra, Jennifer Ding, Sébastien Paquet, Yacine Jernite. At No Conference, 2023.

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The StatCan Dialogue Dataset: Retrieving Data Tables through Conversations with Genuine Intents. Xing Han Lu, Siva Reddy, Harm de Vries. At European Chapter of the Association for Computational Linguistics (EACL), 2023.

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MAPL: Parameter-Efficient Adaptation of Unimodal Pre-Trained Models for Vision-Language Few-Shot Prompting. Oscar Manas, Pau Rodriguez, Saba Ahmadi, Aida Nematzadeh, Yash Goyal, Aishwarya Agrawal. At European Chapter of the Association for Computational Linguistics (EACL), 2023.

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SantaCoder: don't reach for the stars!. Harm de Vries, Raymond Li, Joel Lamy Poirier, Dzmitry Bahdanau, Denis Kocetkov, Sean Hughes. At Workshop at the International Conference on Learning Representations (ICLR), 2023.

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Multilingual Code Retrieval Without Paired Data: A New Benchmark and Experiments. João Monteiro, Torsten Scholak, Virendra Mehta, David Vazquez, Christopher Pal. At Workshop at the International Conference on Learning Representations (ICLR), 2023.

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FigGen: Text to Scientific Figure Generation. Juan A. Rodriguez, David Vazquez, Issam H. Laradji, Marco Pedersoli, Pau Rodriguez. At International Conference of Learning Representations (ICLR), 2023.

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Deep Hyperbolic Reinforcement Learning for Continuous Control. Omar Salemohamed, Edoardo Cetin, Sai Rajeswar Mudumba, Arnab Mondal. At ICLR, Tiny Papers, 2023.

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DAG Learning on the Permutahedron. Valentina Zantedeschi, Luca Franceschi, Jean Kaddour, Matt Kusner, Vlad Niculae. At International Conference of Learning Representations (ICLR), 2023.

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Constraining Representations Yields Models That Know What They Don't Know. João Monteiro, Pau Rodriguez, Pierre-André Noël, Issam H. Laradji, David Vazquez. At International Conference of Learning Representations (ICLR), 2023.

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Choreographer: Learning and Adapting Skills in Imagination. Pietro Mazzaglia, Tim Verbelen, Bart Dhoedt, Alexandre Lacoste, Sai Rajeswar Mudumba. At International Conference of Learning Representations (ICLR), 2023.

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Knowledge Hypergraph Embedding Meets Relational Algebra. Bahare Fatemi, Perouz Taslakian, David Vazquez, David Poole. At Journal of Machine Learning Research (JMLR), 2023.

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Towards Learning to Imitate from a Single Video Demonstration. Glen Berseth, Florian Golemo, Christopher Pal. At Journal of Machine Learning Research (JMLR), 2023.

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Leveraging Human Preferences to Master Poetry. Rafael Pardinas, Gabriel Huang, David Vazquez, Alexandre Piche. At AAAI Workshops, 2023.

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Workflow discovery in low data regimes. Amine El Hattami, Issam H. Laradji, Stefania Raimondo, David Vazquez, Pau Rodriguez, Christopher Pal. At Transactions on Machine Learning Research, 2023.

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Flaky Performances when Pretraining on Relational Databases. Shengchao Liu, David Vazquez, Jian Tang, Pierre-André Noël. At AAAI-23 Student Abstract and Poster Program, 2023.

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OCR-VQGAN: Taming Text-within-Image Generation. Juan A. Rodriguez, David Vazquez, Marco Pedersoli, Issam H. Laradji, Pau Rodriguez. At Winter Conference on Applications of Computer Vision (WACV), 2023.

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Haptics-based Curiosity for Sparse-reward Tasks. Sai Rajeswar Mudumba, Cyril Ibrahim, Nitin Surya, Florian Golemo, David Vazquez, Aaron Courville, Pedro O. Pinheiro. At Conference on Robot Learning (CoRL), 2022.

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Advancing ethics review practices in AI research. Madhulika Srikumar, Grace Abuhamad, Joelle Pineau, Rebecca Finlay , Carolyn Ashurst , Rosie Campbell, Emily Campbell-Ratcliffe, Hudson Hongo, Sarah R. Jordan , Joseph Lindley, Aviv Ovadya. At Nature Machine Intelligence, 2022.

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UnifiedSKG: Unifying and Multi-Tasking Structured Knowledge Grounding with Text-to-Text Language Models. Tianbao Xie, Chen Wu, Peng Shi, Ruiqi Zhong, Torsten Scholak, Michihiro Yasunaga, Chien-Sheng Wu, Ming Zhong, Pengcheng Yin, Sida I. Wang, Victor Zhong, Bailin Wang, Chengzu Li, Connor Boyle, Ansong Ni, Ziyu Yao, Dragomir Radev, Caiming Xiong, Lingpeng Kong, Rui Zhang, Noah A. Smith, Luke Zettlemoyer, Tao Yu. At Conference on Empirical Methods in Natural Language Processing (EMNLP), 2022.

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On the Compositional Generalization Gap of In-Context Learning. Dzmitry Bahdanau, Arian Hosseini, Aaron Courville, Alessandro Sordoni, Ankit Vani. At Conference on Empirical Methods in Natural Language Processing (EMNLP), 2022.

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In-Context Learning for Text Classification with Many Labels. Aristides Milios, Dzmitry Bahdanau, Siva Reddy. At Workshop at the Conference on Empirical Methods in Natural Language Processing (EMNLP), 2022.

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Azimuth: Systematic Error Analysis for Text Classification. Gabrielle Gauthier Melançon, Orlando Marquez, Lindsay Brin, Chris Tyler, Frédéric Branchaud-Charron, Joseph Marinier, Karine Grande, Di Le. At Conference on Empirical Methods in Natural Language Processing (EMNLP), 2022.

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Using Confounded Data in Offline RL. Maxime Gasse, Damien Grasset, Guillaume Gaudron, Pierre-Yves Oudeyer. At Workshop at the Neural Information Processing Systems (NeurIPS), 2022.

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On Margins and Generalisation for Voting Classifiers. Felix Biggs, Valentina Zantedeschi, Benjamin Guedj. At Conference on Neural Information Processing Systems (NeurIPS), 2022.

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Neural Attentive CIrcuits. Martin Weiss, Yoshua Bengio, Nassim, Bernhard Schölkopf, Christopher Pal. At Conference on Neural Information Processing Systems (NeurIPS), 2022.

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Masked Conditional Video Diffusion for Prediction, Generation, and Interpolation. Vikram Voleti, Alexia Jolicoeur-Martineau, Christopher Pal. At Conference on Neural Information Processing Systems (NeurIPS), 2022.

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Learning Discrete Directed Acyclic Graphs via Backpropagation. Andrew J. Wren, Pasquale Minervini, Luca Franceschi, Valentina Zantedeschi. At Workshop at the Neural Information Processing Systems (NeurIPS), 2022.

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Implicit Offline Reinforcement Learning via Supervised Learning. Alexandre Piche, Rafael Pardinas, David Vazquez, Igor Mordatch, Christopher Pal. At Workshop at the Neural Information Processing Systems (NeurIPS), 2022.

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Exploring the Design Space of Generative Diffusion Processes for Sparse Graphs. Pierre-André Noël, Pau Rodriguez. At Workshop at the Neural Information Processing Systems (NeurIPS), 2022.

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Contrastive Self-supervision Defines General-Purpose Similarity Functions. Charles Guille-Escuret, Pau Rodriguez, David Vazquez, Ioannis Mitliagkas, João Monteiro. At Workshop at the Neural Information Processing Systems (NeurIPS), 2022.

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Constraining Low-level Representations to Define Effective Confidence Scores. João Monteiro, Pau Rodriguez, Pierre-André Noël, Issam H. Laradji, David Vazquez. At Workshop at the Neural Information Processing Systems (NeurIPS), 2022.

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Choreographer: Learning and Adapting Skills in Imagination . Pietro Mazzaglia, Tim Verbelen, Bart Dhoedt, Alexandre Lacoste, Sai Rajeswar Mudumba. At Workshop at the Neural Information Processing Systems (NeurIPS), 2022.

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Can large language models build causal graphs?. Tibor Schuster, Stephanie Long, Alexandre Piche. At Workshop at the Neural Information Processing Systems (NeurIPS), 2022.

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Breadth-First Pipeline Parallelism. Joel Lamy Poirier. At Workshop at the Neural Information Processing Systems (NeurIPS), 2022.

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Attention-based Neural Cellular Automata. Mattie Tesfaldet, Derek Nowrouzezahrai, Christopher Pal. At Conference on Neural Information Processing Systems (NeurIPS), 2022.

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A General Purpose Neural Architecture for Geospatial Systems. Alexandre Lacoste, Martin Weiss, Nasim Rahaman. At Workshop at the Neural Information Processing Systems (NeurIPS), 2022.

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Attention for Compositional Modularity. Oleksiy Ostapenko, Alexandre Lacoste, Pau Rodriguez. At Workshop at the Neural Information Processing Systems (NeurIPS), 2022.

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OSM: An Open Set Matting Framework with OOD Detection and Few-Shot Learning. Issam H. Laradji, Yuhongze Zhou, Derek Nowrouzezahrai. At British Machine Vision Conference (BMVC), 2022.

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Does entity abstraction help generative Transformers reason? . Nicolas Gontier, Siva Reddy, Christopher Pal. At Transactions on Machine Learning Research, 2022.

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Consistency-CAM: Towards Improved Weakly Supervised Semantic Segmentation. Sai Rajeswar Mudumba, Issam H. Laradji, Pau Rodriguez, David Vazquez, Aaron Courville. At British Machine Vision Conference (BMVC), 2022.

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Countering Language Drift with KL Regularization. Issam H. Laradji, Michael Noukhovitch, Aaron Courville. At Workshop on Interactive Learning for Natural Language Processing (NeurIPS Workshop), 2022.

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The Stack: 3 TB of permissively licensed source code. Denis Kocetkov, Raymond Li, Loubna Ben Allal, Jia Li, Chenghao Mou, Carlos Muñoz Ferrandis, Yacine Jernite, Margaret Mitchell, Sean Hughes, Thomas Wolf , Dzmitry Bahdanau, Leandro von Werra, Harm de Vries. At Transactions on Machine Learning Research (TMLR), 2022.

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TACTiS: Transformer-Attentional Copulas for Time Series. Alexandre Drouin, Étienne Marcotte, Nicolas Chapados. At Montreal AI Symposium (MAIS), 2022.

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S-LLM: Semi-Supervised Large Language Model for Chat Summarization. Issam H. Laradji, Sathwik Tejaswi Madhusudhan, Orlando Marquez, Pau Rodriguez, David Vazquez. At Montreal AI Symposium (MAIS), 2022.

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Leveraging Activation Patterns to Define Classifiers Able to Detect and Reject Anomalies. João Monteiro, Pau Rodriguez, Pierre-André Noël, Issam H. Laradji, David Vazquez. At Montreal AI Symposium (MAIS), 2022.

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Explaining by Example: A Practitioner’s Perspective. Marc-Etienne Brunet , Masoud Hashemi. At Montreal AI Symposium (MAIS), 2022.

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Explainable, Sensible and Virtuous Workplace Chatbots. Gabriel Huang, Valérie Bécaert, David Vazquez. At Montreal AI Symposium (MAIS), 2022.

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Data Augmentation for Intent Classification with Off-the-shelf Large Language Models. Gaurav Sahu, Pau Rodriguez, Issam H. Laradji, Parmida Atighhehchian, David Vazquez, Dzmitry Bahdanau. At Montreal AI Symposium (MAIS), 2022.

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Continual Learning with self-selecting specialized modules through expansion and pruning. Oleksiy Ostapenko, Pau Rodriguez, Alexandre Lacoste, Laurent Charlin. At Montreal AI Symposium (MAIS), 2022.

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A Planning based Neural-Symbolic Approach for Embodied Instruction Following. Xiaotian Liu, Hector Palacios, Christian Muise. At Montreal AI Symposium (MAIS), 2022.

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Competition exacerbates Language Drift. Michael Noukhovitch, Aaron Courville, Issam H. Laradji. At Machine Learning and the Evolution of Language (JCoLE Workshop), 2022.

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A Closer Look at Embedding Propagation for Manifold Smoothing. Diego Velazquez, Pau Rodriguez, Josep M. Gonfaus, F. Xavier Roca, Jordi Gonzalez. At Journal of Machine Learning Research (JMLR), 2022.

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RaVAEn: Unsupervised Change Detection of Extreme Events Using ML On-Board Satellites. Vít Růžička, Anna Vaughan, Daniele De Martini, James Fulton, Valentina Salvatelli, Chris Bridges, Gonzalo Mateo-Garcia, Valentina Zantedeschi. At Nature Scientific Reports, 2022.

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Monotonicity Regularization: Improved Penalties and Novel Applications to Disentangled Representation Learning and Robust Classification. João Monteiro, Mohamed Osama Ahmed, Hossein Hajimirsadeghi, Greg Mori. At Conference on Uncertainty in Artificial Intelligence (UAI), 2022.

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Examining Responsibility and Deliberation in AI Impact Statements and Ethics Reviews. Grace Abuhamad, David Liu, Sarah Sakha, Priyanka Nanayakkara, Jessica Hullman, Nicholas Diakopoulos, Tina Eliassi-Rad, Su Lin Blodgett. At AAAI/ACM Conference on AI, Ethics, and Society (AIES), 2022.

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Overcoming challenges in leveraging GANs for few-shot data augmentation. Christopher Beckham, Issam H. Laradji, Pau Rodriguez, David Vazquez, Derek Nowrouzezahrai, Christopher Pal. At Workshop at the Conference on Lifelong Learning Agents (CoLLAs), 2022.

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Continual Learning with Foundation Models: An Empirical Study of Latent Replay. Oleksiy Ostapenko, Timothee Lesort, Pau Rodriguez, Arthur Douillard, Irina Rish, Laurent Charlin. At Conference on Lifelong Learning Agents (CoLLAs), 2022.

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Scaling up ML-based Black-box Planning with Partial STRIPS Models. Matias Greco, Alvaro Torralba, Jorge Baier, Hector Palacios. At Workshop at International Join Conference on Artificial Intelligence (IJCAI), 2022.

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Unsupervised Model-based Pre-training for Data-efficient Reinforcement Learning from Pixels. Sai Rajeswar Mudumba, Pietro Mazzaglia, Tim Verbelen, Alexandre Piche, Aaron Courville, Alexandre Lacoste. At Workshop at the International Conference on Machine Learning (ICML), 2022.

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TACTiS: Transformer-Attentional Copulas for Time Series. Alexandre Drouin, Étienne Marcotte, Nicolas Chapados. At International Conference on Machine Learning (ICML), 2022.

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Flaky Performances when Pre-Training on Relational Databases with a Plan for Future Characterization Efforts. Shengchao Liu, David Vazquez, Jian Tang, Pierre-André Noël. At Workshop at the International Conference on Machine Learning (ICML), 2022.

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Direct Behavior Specification via Constrained Reinforcement Learning. Julien Roy, Roger Girgis, Joshua Romoff, Pierre-Luc Bacon, Christopher Pal. At International Conference on Machine Learning (ICML), 2022.

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Neural Point Light Fields. Julian Ost, Issam H. Laradji, Alejandro Newell, Yuval Bahat, Felix Heide. At Computer Vision and Pattern Recognition (CVPR), 2022.

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Multi-label Iterated Learning for Image Classification with Label Ambiguity. Sai Rajeswar Mudumba, Pau Rodriguez, Soumye Singhal, David Vazquez, Aaron Courville. At Computer Vision and Pattern Recognition (CVPR), 2022.

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Kubric: A scalable dataset generator. Klaus Greff, Francois Belletti, Lucas Beyer, Carl Doersch, Yilun Du, Daniel Duckworth, David J. Fleet, Dan Gnanapragasam, Florian Golemo, Charles Herrmann, Thomas Kipf, Abhijit Kundu, Dmitry Lagun, Issam H. Laradji, Hsueh-Ti (Derek)Liu, Henning Meyer, Yishu Miao, Derek Nowrouzezahrai, Cengiz Oztireli, Etienne Pot, Noha Radwan, Daniel Rebain, Sara Sabour, Mehdi S. M. Sajjadi, Matan Sela, Vincent Sitzmann, Austin Stone, Deqing Sun, Suhani Vora, Ziyu Wang, Tianhao Wu, Kwang Moo Yi, Fangcheng Zhong, Andrea Tagliasacchi. At Computer Vision and Pattern Recognition (CVPR), 2022.

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Continual Learning with Foundation Models: An Empirical Study of Latent Replay. Oleksiy Ostapenko, Timothee Lesort, Pau Rodriguez, Arthur Douillard, Irina Rish, Laurent Charlin. At Workshop at the Conference on Computer Vision and Pattern Recognition (CVPR), 2022.

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A Planning based Neural-Symbolic Approach for Embodied Instruction Following. Xiaotian Liu, Hector Palacios, Christian Muise. At Workshop at the Conference on Computer Vision and Pattern Recognition (CVPR), 2022.

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Scaling up ML-based Black-box Planning with Partial STRIPS Models. Matias Greco, Alvaro Torralba, Jorge Baier, Hector Palacios. At ICAPS'22 Workshop on Reliable Data-Driven Planning and Scheduling, 2022.

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Deconfounding Dynamic Treatment Regimes. David Berger, Alexandre Drouin, Alexandre Lacoste, Laurent Charlin. At SSC, 2022.

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Data Augmentation for Intent Classification with Off-the-shelf Large Language Models. Gaurav Sahu, Pau Rodriguez, Parmida Atighhehchian, Issam H. Laradji, David Vazquez, Dzmitry Bahdanau. At Workshop at the Annual Meetings of the Association for Computational Linguistics (ACL), 2022.

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The Power of Prompt Tuning for Low-Resource Semantic Parsing. Nathan Schucher, Siva Reddy, Harm de Vries. At Annual Meeting of the Association for Computational Linguistics (ACL), 2022.

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LAGr: Labeling Aligned Graphs for Improving Systematic Generalization in Semantic Parsing. Dora Jambor, Dzmitry Bahdanau. At Annual Meeting of the Association for Computational Linguistics (ACL), 2022.

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Compositional Generalization in Dependency Parsing. Emily Goodwin, Siva Reddy, Timothy J. O'Donnell, Dzmitry Bahdanau. At Annual Meeting of the Association for Computational Linguistics (ACL), 2022.

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Geoscience language models and their intrinsic evaluation. Christopher J.M. Lawley, Stefania Raimondo, Tianyi Chen, Lindsay Brin, Anton Zakharov, Daniel Kur, Jenny Hui, Glen Newton, Sari L. Burgoyne, Geneviève Marquis. At Applied Computing and Geosciences, 2022.

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Object-centric Compositional Imagination for Visual Abstract Reasoning. Rim Assouel, Perouz Taslakian, David Vazquez, Pau Rodriguez, Yoshua Bengio. At Workshop at the International Conference on Learning Representations (ICLR), 2022.

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Learning to Guide and to Be Guided in the Architect-Builder Problem. Paul Barde, Tristan Karch, Derek Nowrouzezahrai, Clément Moulin-Frier, Christopher Pal, Pierre-Yves Oudeyer. At International Conference on Learning Representations (ICLR), 2022.

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Latent Variable Sequential Set Transformers for Joint Multi-Agent Motion Prediction. Roger Girgis, Florian Golemo, Felipe Codevilla, Martin Weiss, Jim Aldon D'Souza, Samira Ebrahimi Kahou, Felix Heide, Christopher Pal. At International Conference on Learning Representations (ICLR), 2022.

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Can Active Learning Preemptively Mitigate Fairness Issues?. Frédéric Branchaud-Charron, Parmida Atighhehchian, Pau Rodriguez, Grace Abuhamad, Alexandre Lacoste. At Workshop at the International Conference on Learning Representations (ICLR), 2022.

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A Probabilistic Perspective on Reinforcement Learning via Supervised Learning. Alexandre Piche, Rafael Pardinas, David Vazquez, Christopher Pal. At Workshop at the International Conference on Learning Representations (ICLR), 2022.

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Let's Make Block Coordinate Descent Converge Faster: Faster Greedy Rules, Message-Passing, Active-Set Complexity, and Superlinear Convergence. julie nutini, Issam H. Laradji, Mark Schmidt. At Journal of Machine Learning Research (JMLR), 2022.

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Visual Question Answering From Another Perspective: CLEVR Mental Rotation Tests. Christopher Beckham, Martin Weiss, Florian Golemo, Sina Honari, Derek Nowrouzezahrai, Christopher Pal. At Pattern Recognition (PR), 2022.

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VIM: Variational Independent Modules for Video Prediction. Rim Assouel, Lluis Castrejon, Aaron Courville, Nicolas Ballas, Yoshua Bengio. At Causal Learning and Reasoning (CLeaR), 2022.

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Typing assumptions improve identification in causal discovery - theory and algorithms. Philippe Brouillard, Perouz Taslakian, Alexandre Lacoste, Sébastien Lachapelle, Alexandre Drouin. At Causal Learning and Reasoning (CLeaR), 2022.

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Disentanglement via Mechanism Sparsity Regularization: A New Principle for Nonlinear ICA. Sébastien Lachapelle, Pau Rodriguez, Yash Sharma, Katie Everett , Rémi Le Priol, Alexandre Lacoste, Simon Lcoste-Julien. At Causal Learning and Reasoning (CLeaR), 2022.

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A Soft Labeling Approach to Develop Automated Algorithms that Incorporate Uncertainty in Pulmonary Opacification on Chest CT using COVID-19 Pneumonia. Keegan Lensink, Fu Jorden Lo, Rachel L Eddy, Marco Law, Issam H. Laradji, Eldad Haber, Savvas Nicolaou, Darra Murphy, William A Parker. At Academic Radiology, 2022.

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Typing assumptions improve identification in causal discovery - Report and comments on future directions. Philippe Brouillard, Perouz Taslakian, Alexandre Lacoste, Sébastien Lachapelle, Alexandre Drouin. At Workshop at the Neural Information Processing Systems (NeurIPS), 2021.

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Towards Neural Functional Program Evaluation. Torsten Scholak, Jonathan Pilault, Joey Velez-Ginorio. At Conference on Neural Information Processing Systems (NeurIPS), 2021.

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Toward Foundation Models for Earth Monitoring: Proposal for a Climate Change Benchmark. Alexandre Lacoste, Hannah Kerner, Hamed Alemohammad, Björn Lütjens, Jeremy Irvin, David Dao, Alex Chang, Mehmet Gunturkun, Alexandre Drouin, Pau Rodriguez, David Vazquez, Evan D. Sherwin. At Workshop at the Neural Information Processing Systems (NeurIPS), 2021.

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The Dynamics of Functional Diversity throughout Neural Network Training. Lee Zamparo, Marc-Etienne Brunet , Thomas George, Sepideh Kharaghani, Gintare Karolina Dziugaite. At Conference on Neural Information Processing Systems (NeurIPS), 2021.

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Systematic Generalization with Edge Transformers. Leon Bergen, Timothy J. O'Donnell, Dzmitry Bahdanau. At Conference on Neural Information Processing Systems (NeurIPS), 2021.

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Systematic Evaluation of Causal Discovery in Visual Model Based Reinforcement Learning. Nan Rosemary Ke, Aniket Didolkar, Sarthak Mittal, Anirudh Goyal, Guillaume Lajoie, Danilo Rezende, Yoshua Bengio, Christopher Pal, Stefan Bauer , Michael C. Mozer. At Conference on Neural Information Processing Systems (NeurIPS), 2021.

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On the Value of ML Models. Fabio Casati, Pierre-André Noël, Jie Yang. At Workshop at the Neural Information Processing Systems (NeurIPS), 2021.

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Continual Learning via Local Module Composition. Oleksiy Ostapenko, Pau Rodriguez, Massimo Caccia, Laurent Charlin. At Conference on Neural Information Processing Systems (NeurIPS), 2021.

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Picard: Parsing Incrementally for Constrained Auto-Regressive Decoding from Language Models. Torsten Scholak, Nathan Schucher, Dzmitry Bahdanau. At Conference on Empirical Methods in Natural Language Processing (EMNLP), 2021.

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A Survey of Self-Supervised and Few-Shot Object Detection. Gabriel Huang, Issam H. Laradji, David Vazquez, Simon Lcoste-Julien, Pau Rodriguez. At IEEE Transactions on Pattern Analysis and Machine Intelligence (T-PAMI), 2021.

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A Deep Learning Localization Method for Measuring Abdominal Muscle Dimensions in Ultrasound Images. Alzayat Saleh, Issam H. Laradji, Corey Lammie, David Vazquez, Carol A Flavell, Mostafa Rahimi Azghadi. At IEEE Journal of Biomedical and Health Informatics, 2021.

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SSR: Semi-supervised Soft Rasterizer for single-view 2D to 3D Reconstruction. Issam H. Laradji, Pau Rodriguez, David Vazquez, Derek Nowrouzezahrai. At Workshop at the International Conference on Computer Vision (ICCV), 2021.

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Seasonal Contrast: Unsupervised Pre-Training from Uncurated Remote Sensing Data. Oscar Manas, Alexandre Lacoste, Xavier Giro-i-Nieto, David Vazquez, Pau Rodriguez. At International Conference on Computer Vision (ICCV), 2021.

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Generative Compositional Augmentations for Scene Graph Prediction. Boris Knyazev, Harm de Vries, Cătălina Cangea, Graham W. Taylor, Aaron Courville, Eugene Belilovsky. At International Conference on Computer Vision (ICCV), 2021.

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Beyond Trivial Counterfactual Explanations with Diverse Valuable Explanations. Pau Rodriguez, Massimo Caccia, Alexandre Lacoste, Lee Zamparo, Issam H. Laradji, Laurent Charlin, David Vazquez. At International Conference on Computer Vision (ICCV), 2021.

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TopiOCQA: Open-domain Conversational Question Answering with Topic Switching. Vaibhav Adlakha, Shehzaad Dhuliawala, Kaheer Suleman, Harm de Vries, Siva Reddy. At Transactions of the Association for Computational Linguistics (TACL), 2021.

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Weakly Supervised Underwater Fish Segmentation Using Affinity LCFCN. Issam H. Laradji, Alzayat Saleh, Pau Rodriguez, Derek Nowrouzezahrai, Mostafa Rahimi Azghadi, David Vazquez. At Nature Scientific Reports, 2021.

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Logo Detection with no Priors. Diego Velazquez, Josep M. Gonfaus, Pau Rodriguez, F. Xavier Roca, Seiichi Ozawa, Jordi Gonzalez. At The Multidisciplinary Open Access Journal (IEEE Access), 2021.

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3D Perception with Slanted Stixels on GPU. Daniel Hernandez, Antonio Espinosa, David Vazquez, Antonio M. Lopez, Juan C. Moure. At IEEE Transactions on Parallel and Distributed Systems, 2021.

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RandomSCM: interpretable ensembles of sparse classifiers tailored for omics data. Thibaud Godon, Pier-Luc Plante, Baptiste Bauvin, Élina Francovic-Fontaine, Alexandre Drouin, François Laviolette, Jacques Corbeil. At Intelligent Systems for Molecular Biology and European Conference on Computational Biology, 2021.

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Variational Causal Networks: Approximate Bayesian Inference over Causal Structures. Yashas Annadani, Jonas Rothfuss, Alexandre Lacoste, Nino Scherrer, Anirudh Goyal, Yoshua Bengio, Stefan Bauer . At Workshop at the International Conference on Machine Learning (ICML), 2021.

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Typing assumptions improve identification in causal discovery. Philippe Brouillard, Perouz Taslakian, Alexandre Lacoste, Sébastien Lachapelle, Alexandre Drouin. At Workshop at the International Conference on Machine Learning (ICML), 2021.

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Understanding by Understanding Not: Modeling Negation in Language Models. Arian Hosseini, Siva Reddy, Dzmitry Bahdanau, R Devon Hjelm, Alessandro Sordoni, Aaron Courville. At North American Chapter of the Association for Computational Linguistics (NAACL), 2021.

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DuoRAT: Towards Simpler Text-to-SQL Models. Torsten Scholak, Raymond Li, Dzmitry Bahdanau, Harm de Vries, Christopher Pal. At North American Chapter of the Association for Computational Linguistics (NAACL), 2021.

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Reinforcement Learning with Random Delays. Simon Ramstedt, Yann Bouteiller, Giovanni Beltrame, Christopher Pal, Jonathan Binas. At International Conference on Learning Representations (ICLR), 2021.

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Pruning Neural Networks at Initialization: Why are We Missing the Mark?. Jonathan Frankle, Gintare Karolina Dziugaite, Daniel M. Roy, Michael Carbin. At International Conference on Learning Representations (ICLR), 2021.

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Predicting Infectiousness for Proactive Contact Tracing. Yoshua Bengio, Prateek Gupta, Tegan Maharaj, Nasim Rahaman, Martin Weiss, Tristan Deleu, Eilif Benjamin Muller, Meng Qu, victor schmidt, Pierre-luc St-charles, hannah alsdurf, Olexa Bilaniuk, david buckeridge, gaetan caron, pierre luc carrier, Joumana Ghosn, satya ortiz gagne, Christopher Pal, Irina Rish, Bernhard Schölkopf, abhinav sharma, Jian Tang. At International Conference on Learning Representations (ICLR), 2021.

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Conditionally Adaptive Multi-Task Learning: Improving Transfer Learning in NLP Using Fewer Parameters & Less Data. Jonathan Pilault, Amine El Hattami, Christopher Pal. At International Conference on Learning Representations (ICLR), 2021.

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Stochastic polyak step-size for sgd: An adaptive learning rate for fast convergence. Nicolas Loizou, Sharan Vaswani, Issam H. Laradji, Simon Lcoste-Julien. At International Conference on Artificial Intelligence and Statistics (AISTATS), 2021.

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On the role of data in PAC-Bayes bounds. Gintare Karolina Dziugaite, Kyle Hsu, Waseem Gharbieh, Gabriel Arpino, Daniel M. Roy. At International Conference on Artificial Intelligence and Statistics (AISTATS), 2021.

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Combining Reinforcement Learning and Constraint Programming for Combinatorial Optimization. Quentin Cappart, Thierry Moisan, Louis-Martin Rousseau, Isabeau Prémont-Schwarz, Andre Cire. At Association for the Advancement of Artificial Intelligence (AAAI), 2021.

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Overnet: Lightweight multi-scale super-resolution with overscaling network. Parichehr Behjati, Pau Rodriguez, Armin Mehri, Isabelle Hupont, Jordi Gonzalez, Carles Fernandez. At Winter Conference on Applications of Computer Vision (WACV), 2021.

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Learning Data Augmentation with Online Bilevel Optimization for Image Classification. Saypraseuth Mounsaveng, Issam H. Laradji, Ismail Ben Ayed, David Vazquez, Marco Pedersoli. At Winter Conference on Applications of Computer Vision (WACV), 2021.

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A Weakly Supervised Consistency-based Learning Method for COVID-19 Segmentation in CT Images. Issam H. Laradji, Pau Rodriguez, Oscar Manas, Keegan Lensink, Marco Law, Lironne Kurzman, David Vazquez, Derek Nowrouzezahrai, William A Parker. At Winter Conference on Applications of Computer Vision (WACV), 2021.

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Unsupervised Learning of Dense Visual Representations. Pedro O. Pinheiro, Amjad Almahairi, Ryan Benmalek, Florian Golemo, Aaron Courville. At Conference on Neural Information Processing Systems (NeurIPS), 2020.

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Synbols: Probing Learning Algorithms with Synthetic Datasets. Alexandre Lacoste, Pau Rodriguez, Frederic Branchaud, Parmida Atighhehchian, Massimo Caccia, Issam H. Laradji, Alexandre Drouin, Matt Craddock, Laurent Charlin, David Vazquez. At Conference on Neural Information Processing Systems (NeurIPS), 2020.

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Sharpened Generalization Bounds based on Conditional Mutual Information and an Application to Noisy-Gradient Iterative Algorithms. Mahdi Haghifam, Jeffrey Negrea, Ashish Khisti, Daniel M. Roy, Gintare Karolina Dziugaite. At Conference on Neural Information Processing Systems (NeurIPS), 2020.

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Pruning Neural Networks at Initialization: Why Are We Missing the Mark? . Jonathan Frankle, Gintare Karolina Dziugaite, Daniel M. Roy, Michael Carbin. At Workshop at the Neural Information Processing Systems (NeurIPS), 2020.

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Promoting Coordination through Policy Regularization in Multi-Agent Deep Reinforcement Learning. Julien Roy, Paul Barde, Félix G. Harvey, Derek Nowrouzezahrai, Christopher Pal. At Conference on Neural Information Processing Systems (NeurIPS), 2020.

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Online Fast Adaptation and Knowledge Accumulation: a New Approach to Continual Learning. Massimo Caccia, Pau Rodriguez, Oleksiy Ostapenko, Fabrice Normandin, Min Lin, Lucas Caccia, Issam H. Laradji, Irina Rish, Alexandre Lacoste, David Vazquez, Laurent Charlin. At Conference on Neural Information Processing Systems (NeurIPS), 2020.

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Measuring Systematic Generalization in Neural Proof Generation with Transformers. Nicolas Gontier, Koustuv Sinha, Siva Reddy, Christopher Pal. At Conference on Neural Information Processing Systems (NeurIPS), 2020.

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Like A Researcher Stating Broader Impact for the Very First Time. Grace Abuhamad, Claudel Rheault. At Workshop at the Neural Information Processing Systems (NeurIPS), 2020.

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In search of robust measures of generalization. Gintare Karolina Dziugaite, Alexandre Drouin, Brayden (Brady) Neal, Nitarshan Rajkumar, Ethan Victor Caballero, Linbo Wang, Ioannis Mitliagkas, Daniel M. Roy. At Conference on Neural Information Processing Systems (NeurIPS), 2020.

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Differentiable Causal Discovery from Interventional Data. Philippe Brouillard, Sébastien Lachapelle, Alexandre Lacoste, Simon Lcoste-Julien, Alexandre Drouin. At Conference on Neural Information Processing Systems (NeurIPS), 2020.

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Counting Cows: Tracking Illegal Cattle Ranching From High-Resolution Satellite Imagery. Issam H. Laradji, Pau Rodriguez, Alfredo Kalaitzis, David Vazquez, Ross Young, Ed Davey, Alexandre Lacoste. At Workshop at the Neural Information Processing Systems (NeurIPS), 2020.

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An empirical study of loss landscape geometry and evolution of the data-dependent Neural Tangent Kernel. Stanislav Fort, Gintare Karolina Dziugaite, Mansheej Paul, Sepideh Kharaghani, Daniel M. Roy, Surya Ganguli. At Conference on Neural Information Processing Systems (NeurIPS), 2020.

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Adversarial Soft Advantage Fitting: Imitation Learning without Policy Optimization. Paul Barde, Julien Roy, Wonseok Jeon, Joelle Pineau, Christopher Pal, Derek Nowrouzezahrai. At Conference on Neural Information Processing Systems (NeurIPS), 2020.

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Hierarchical Residual Attention Network for Single Image Super-Resolution. Parichehr Behjati, Pau Rodriguez, Armin Mehri, Isabelle Hupont, Carles Fernandez, Jordi Gonzalez. At ArXiv, 2020.

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On Extractive and Abstractive Neural Document Summarization with Transformer Language Models. Sandeep Subramanian, Raymond Li, Jonathan Pilault, Christopher Pal. At Conference on Empirical Methods in Natural Language Processing (EMNLP), 2020.

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On the Information Complexity of Proper Learners for VC Classes in the Realizable Case. Mahdi Haghifam, Gintare Karolina Dziugaite, Shay Moran, Daniel M. Roy. At ArXiv, 2020.

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Enforcing Interpretability and its Statistical Impacts: Trade-offs between Accuracy and Interpretability. Gintare Karolina Dziugaite, Shai Ben-David, Daniel M. Roy. At ArXiv, 2020.

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Proposal-based Instance Segmentation with Point Supervision. Issam H. Laradji, Negar Rostamzadeh, Pedro O. Pinheiro, David Vazquez, Mark Schmidt. At International Conference on Image Processing (ICIP), 2020.

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LOOC: Localize Overlapping Objects with Count Supervision. Issam H. Laradji, Rafael Pardinas, Pau Rodriguez, David Vazquez. At International Conference on Image Processing (ICIP), 2020.

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CVPR 2020 Continual Learning in Computer Vision Competition: Approaches, Results, Current Challenges and Future Directions. Vincenzo Lomonaco, Lorenzo Pellegrini, Pau Rodriguez, Massimo Caccia, Qi She, Yu Chen, Quentin Jodelet, Ruiping Wang, Zheda Mai, David Vazquez, German I. Parisi, Nikhil Churamani, Marc Pickett, Issam H. Laradji, Davide Maltoni. At Artificial Intelligence Journal, 2020.

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Generative Compositional Augmentations for Scene Graph Prediction. Boris Knyazev, Harm de Vries, Cătălina Cangea, Graham W. Taylor, Aaron Courville, Eugene Belilovsky. At Britsh Machine Vision Conference (BMVC), 2020.

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Embedding Propagation: Smoother Manifold for Few-Shot Classification. Pau Rodriguez, Issam H. Laradji, Alexandre Drouin, Alexandre Lacoste. At European Conference on Computer Vision (ECCV), 2020.

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Towards Ecologically Valid Research on Language User Interfaces. Harm de Vries, Dzmitry Bahdanau, Chris Manning. At ArXiv, 2020.

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Online Learned Continual Compression with Adaptive Quantization Modules. Lucas Caccia, Eugene Belilovsky, Massimo Caccia, Joelle Pineau. At International Conference on Machine Learning (ICML), 2020.

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Linear Mode Connectivity and the Lottery Ticket Hypothesis. Jonathan Frankle, Gintare Karolina Dziugaite, Daniel M. Roy, Michael Carbin. At International Conference on Machine Learning (ICML), 2020.

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In Defense of Uniform Convergence: Generalization via derandomization with an application to interpolating predictors. Jeffrey Negrea, Gintare Karolina Dziugaite, Daniel M. Roy. At International Conference on Machine Learning (ICML), 2020.

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AR-DAE: Towards Unbiased Neural Entropy Gradient Estimation. Jae Hyun Lim, Aaron Courville, Christopher Pal, Chin-Wei Huang. At International Conference on Machine Learning (ICML), 2020.

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Bayesian active learning for production, a systematic study and a reusable library. Parmida Atighhehchian, Frederic Branchaud, Alexandre Lacoste. At Workshop at the International Conference on Machine Learning (ICML), 2020.

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Knowledge Hypergraphs: Prediction Beyond Binary Relations. Bahare Fatemi, Perouz Taslakian, David Vazquez, David Poole. At International Join Conference on Artificial Intelligence (IJCAI), 2020.

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Estimating Carbon Emissions of Artificial Intelligence [Opinion]. Alexandra Luccioni, Alexandre Lacoste, victor schmidt. At IEEE Technology and Society Magazine (TSM), 2020.

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Online Fast Adaptation and Knowledge Accumulation: a New Approach to Continual Learning. Massimo Caccia, Pau Rodriguez, Oleksiy Ostapenko, Fabrice Normandin, Min Lin, Lucas Caccia, Issam H. Laradji, Irina Rish, Alexandre Lacoste, David Vazquez, Laurent Charlin. At Workshop at the Conference on Computer Vision and Pattern Recognition (CVPR), 2020.

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Stochastic Neural Network with Kronecker Flow. Chin-Wei Huang, Ahmed Touati, Pascal Vincent, Gintare Karolina Dziugaite, Alexandre Lacoste, Aaron Courville. At International Conference on Artificial Intelligence and Statistics (AISTATS), 2020.

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RelatIF: Identifying Explanatory Training Examples via Relative Influence. Elnaz Barshan, Marc-Etienne Brunet , Gintare Karolina Dziugaite. At International Conference on Artificial Intelligence and Statistics (AISTATS), 2020.

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Fast and Furious Convergence: Stochastic Second Order Methods under Interpolation. Si Yi Meng, Sharan Vaswani, Issam H. Laradji, Mark Schmidt, Simon Lcoste-Julien. At International Conference on Artificial Intelligence and Statistics (AISTATS), 2020.

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The Variational Bandwidth Bottleneck: Stochastic Evaluation on an Information Budget. Anirudh Goyal, Yoshua Bengio, Matthew Botvinick, Sergey Levine. At International Conference on Learning Representations (ICLR), 2020.

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Reinforced Active Learning for Image Segmentation. Arantxa Casanova, Pedro O. Pinheiro, Negar Rostamzadeh, Christopher Pal. At International Conference on Learning Representations (ICLR), 2020.

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N-BEATS: Neural basis expansion analysis for interpretable time series forecasting. Boris N. Oreshkin, Dmitri Carpov, Nicolas Chapados, Yoshua Bengio. At International Conference on Learning Representations (ICLR), 2020.

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Gradient-Based Neural DAG Learning with Interventions. Philippe Brouillard, Alexandre Drouin, Sébastien Lachapelle, Alexandre Lacoste, Simon Lcoste-Julien. At Workshop at the International Conference on Learning Representations (ICLR), 2020.

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Finding and Visualizing Weaknesses of Deep Reinforcement Learning Agents. Christian Rupprecht, Cyril Ibrahim, Christopher Pal. At International Conference on Learning Representations (ICLR), 2020.

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A Meta-Transfer Objective for Learning to Disentangle Causal Mechanisms. Yoshua Bengio, Tristan Deleu, Nasim Rahaman, Nan Rosemary Ke, Sébastien Lachapelle, Olexa Bilaniuk, Anirudh Goyal, Christopher Pal. At International Conference on Learning Representations (ICLR), 2020.

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A Closer Look at the Optimization Landscapes of Generative Adversarial Networks. Hugo Berard, Gauthier Gidel, Amjad Almahairi, Pascal Vincent, Simon Lcoste-Julien. At International Conference on Learning Representations (ICLR), 2020.

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Phylogenetic Manifold Regularization: a semi-supervised approach to predict transcription factor binding sites. Faizy Ahsan, Alexandre Drouin, François Laviolette, Doina Precup, Mathieu Blanchette. At International Conference on Bioinformatics and Biomedicine (BIBM), 2020.

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HighRes-net: Recursive Fusion for Multi-Frame Super-Resolution of Satellite Imagery. Michel Deudon, Alfredo Kalaitzis, Israel Goytom, Zhichao Lin, Kris Sankaran, Vincent Michalski, Samira E. Kahou, Julien Cornebise, Yoshua Bengio. At ArXiv, 2020.

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Stabilizing the Lottery Ticket Hypothesis. Jonathan Frankle, Gintare Karolina Dziugaite, Daniel M. Roy, Michael Carbin. At Association for the Advancement of Artificial Intelligence (AAAI), 2020.

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Knowledge Hypergraphs: Prediction Beyond Binary Relations. Bahare Fatemi, Perouz Taslakian, David Vazquez, David Poole. At Workshop at the Association for the Advancement of Artificial Intelligence (AAAI), 2020.

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A realistic fish-habitat dataset to evaluate algorithms for underwater visual analysis. Alzayat Saleh, Issam H. Laradji, Dmitry A Konovalov, Michael Bradley, David Vazquez, Marcus Sheaves. At Nature Scientific Reports, 2020.

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Linear Mode Connectivity and the Lottery Ticket Hypothesis. Jonathan Frankle, Gintare Karolina Dziugaite, Daniel M. Roy, Michael Carbin. At Women in Machine Learning (WiML), 2019.

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Knowledge Hypergraphs: Prediction Beyond Binary Relations. Bahare Fatemi, Perouz Taslakian, David Vazquez, David Poole. At Women in Machine Learning (WiML), 2019.

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Class-Based Styling: Real-time Localized Style Transfer with Semantic Segmentation. Lironne Kurzman, David Vazquez, Issam H. Laradji. At Women in Machine Learning (WiML), 2019.

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Tackling Climate Change with Machine Learning. David Rolnick, Priya L. Donti, Lynn H. Kaack, Kelly Kochanski, Alexandre Lacoste, Kris Sankaran, Andrew Slavin Ross, Nikola Milojevic-Dupont, Natasha Jaques, Anna Waldman-Brown, Alexandra Luccioni, Tegan Maharaj, S. Karthik Mukkavilli, Konrad P. Kording, Carla Gomes, Andrew Y. Ng, Demis Hassabis, John C. Platt, Felix Creutzig, Jennifer Chayes, Yoshua Bengio, Evan D. Sherwin. At Workshop at the Neural Information Processing Systems (NeurIPS), 2019.

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Retrieving Signals in the Frequency Domain with Deep Complex Extractors. Chiheb Trabelsi, Olexa Bilaniuk, Ousmane Amadou Dia, Ying Zhang, Mirco Ravanelli, Jonathan Binas, Negar Rostamzadeh, Christopher Pal. At Workshop at the Neural Information Processing Systems (NeurIPS), 2019.

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Reducing Noise in GAN Training with Variance Reduced Extragradient. Tatjana Chavdarova, Gauthier Gidel, François Fleuret, Simon Lcoste-Julien. At Conference on Neural Information Processing Systems (NeurIPS), 2019.

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Real-Time Reinforcement Learning. Simon Ramstedt, Christopher Pal. At Conference on Neural Information Processing Systems (NeurIPS), 2019.

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Painless Stochastic Gradient: Interpolation, Line-Search, and Convergence Rates. Sharan Vaswani, Aaron Mishkin, Issam H. Laradji, Mark Schmidt, Gauthier Gidel, Simon Lcoste-Julien. At Conference on Neural Information Processing Systems (NeurIPS), 2019.

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On Adversarial Mixup Resynthesis. Christopher Beckham, Sina Honari, Vikas Verma, Alex Lamb, Farnoosh Ghadiri, R Devon Hjelm, Yoshua Bengio, Christopher Pal. At Conference on Neural Information Processing Systems (NeurIPS), 2019.

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Objects of violence: synthetic data for practical ML in human rights investigations. Lachlan Kermode, Jan Freyberg, Alican Akturk, Robert Trafford, Denis Kocetkov, Rafael Pardinas, Eyal Weizman, Julien Cornebise. At Workshop at the Neural Information Processing Systems (NeurIPS), 2019.

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Neural Multisensory Scene Inference. Jae Hyun Lim, Pedro O. Pinheiro, Negar Rostamzadeh, Christopher Pal, Sungjin Ahn. At Conference on Neural Information Processing Systems (NeurIPS), 2019.

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Neocortical plasticity: an unsupervised cake but no free lunch. Eilif Muller, Philippe Beaudoin. At Workshop at the Neural Information Processing Systems (NeurIPS), 2019.

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Learning Reward Machines for Partially Observable Reinforcement Learning. Rodrigo Toro Icarte, Ethan Waldie, Toryn Q. Klassen, Richard Valenzano, Margarita P. Castro, Sheila A. McIlraith. At Conference on Neural Information Processing Systems (NeurIPS), 2019.

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Information-Theoretic Generalization Bounds for SGLD via Data-Dependent Estimates. Jeffrey Negrea, Mahdi Haghifam, Gintare Karolina Dziugaite, Ashish Khisti, Daniel M. Roy. At Conference on Neural Information Processing Systems (NeurIPS), 2019.

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CLOSURE: Assessing Systematic Generalization of CLEVR models. Dzmitry Bahdanau, Harm de Vries, Timothy J. O'Donnell, Philippe Beaudoin, Yoshua Bengio, Aaron Courville, Shikhar Murty. At Workshop at the Neural Information Processing Systems (NeurIPS), 2019.

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Adaptive Cross-Modal Few-shot Learning. Chen Xing, Negar Rostamzadeh, Boris N. Oreshkin, Pedro O. Pinheiro. At Conference on Neural Information Processing Systems (NeurIPS), 2019.

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Quantifying the Carbon Emissions of Machine Learning. Alexandre Lacoste, Alexandra Luccioni, victor schmidt, Thomas Dandres. At ArXiv, 2019.

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SEVN: A Sidewalk Simulation Environment for Visual Navigation. Martin Weiss, Simon Chamorro, Roger Girgis, Margaux Luck, Samira Ebrahimi Kahou, Joseph P. Cohen, Derek Nowrouzezahrai, Doina Precup, Florian Golemo, Christopher Pal. At Conference on Robot Learning (CoRL), 2019.

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Active Domain Randomization. Bhairav Mehta, Manfred Diaz, Florian Golemo, Christopher Pal, Liam Paull. At Conference on Robot Learning (CoRL), 2019.

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Physical Adversarial Textures that Fool Visual Object Tracking. Rey Reza Wiyatno, Anqi Xu. At International Conference on Computer Vision (ICCV), 2019.

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Fourier-CPPNs for Image Synthesis. Mattie Tesfaldet, Xavier Snelgrove, David Vazquez. At Workshop at the International Conference on Computer Vision (ICCV), 2019.

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Domain-Adaptive Single-view 3D Reconstruction. Pedro O. Pinheiro, Negar Rostamzadeh, Sungjin Ahn. At International Conference on Computer Vision (ICCV), 2019.

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Class-Based Styling: Real-time Localized Style Transfer with Semantic Segmentation. Lironne Kurzman, David Vazquez, Issam H. Laradji. At Workshop at the International Conference on Computer Vision (ICCV), 2019.

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Adaptive Masked Proxies for Few-Shot Segmentation. Mennatullah Siam, Boris N. Oreshkin, Martin Jagersand. At International Conference on Computer Vision (ICCV), 2019.

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Where are the Masks: Instance Segmentation with Image-level Supervision. Issam H. Laradji, David Vazquez, Mark Schmidt. At Britsh Machine Vision Conference (BMVC), 2019.

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Integrating Vision and Language in Social Networks for Identifying Visual Patterns of Personality Traits. Pau Rodriguez, Jordi Gonzalez, Josep M. Gonfaus, Xavier Roca. At International Journal of Social Science and Humanity (IJSSH), 2019.

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Extending the Spatial Scale of Land Use Regression Models for Ambient Ultrafine Particles using Satellite Images and Deep Convolutional Neural Networks. Kris Y. Hong, Pedro O. Pinheiro, Laura Minet, Marianne Hatzopoulou, Scott Weichenthal. At Journal of Environmental Science (JES), 2019.

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The Impact of Preprocessing on Arabic-English Statistical and Neural Machine Translation. Mai Oudah, Amjad Almahairi, Nizar Habash. At Machine Translation Summit (MT Summit), 2019.

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Structure Learning for Neural Module Networks. Vardaan Pahuja, Jie Fu, Sarath Chandar, Christopher Pal. At Annual Meeting of the Association for Computational Linguistics (ACL), 2019.

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On the impressive performance of randomly weighted encoders in summarization tasks. Jonathan Pilault, Jaehong Park, Christopher Pal. At Annual Meeting of the Association for Computational Linguistics (ACL), 2019.

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Efficient Deep Gaussian Process Models for Variable-Sized Inputs. Issam H. Laradji, Mark Schmidt, Vladimir Pavlovic, Minyoung Kim. At International Joint Conference on Neural Networks (IJCNN), 2019.

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Searching for Markovian Subproblems to Address Partially Observable Reinforcement Learning. Rodrigo Toro Icarte, Ethan Waldie, Toryn Q. Klassen, Richard Valenzano, Margarita P. Castro, Sheila A. McIlraith. At Multi-disciplinary Conference on Reinforcement Learning and Decision Making (RLDM), 2019.

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Adversarial Computation of Optimal Transport Maps. Jacob Leygonie, Jennifer She, Amjad Almahairi, Sai Rajeswar Mudumba, Aaron Courville. At ArXiv, 2019.

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Context-Aware Visual Compatibility Prediction. Guillem Cucurull, Perouz Taslakian, David Vazquez. At Computer Vision and Pattern Recognition (CVPR), 2019.

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Mass spectra alignment using virtual lock-masses. Francis Brochu, Pier-Luc Plante, Alexandre Drouin, Dominic Gagnon, Dave Richard, Francine Durocher, Caroline Diorio, Mario Marchand, Jacques Corbeil, François Laviolette. At Nature Scientific Reports, 2019.

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Stochastic Neural Network with Kronecker Flow. Chin-Wei Huang, Ahmed Touati, Pascal Vincent, Gintare Karolina Dziugaite, Alexandre Lacoste, Aaron Courville. At Workshop at the International Conference on Machine Learning (ICML), 2019.

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Learning Global Variations in Outdoor PM_2.5 Concentrations with Satellite Images. Yukai (Kris) Hong, Pedro O. Pinheiro, Scott Weichenthal. At Workshop at the International Conference on Machine Learning (ICML), 2019.

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Information-Theoretic Generalization Bounds for SGLD via Data-Dependent Estimates. Jeffrey Negrea, Mahdi Haghifam, Gintare Karolina Dziugaite, Ashish Khisti, Daniel M. Roy. At Workshop at the International Conference on Machine Learning (ICML), 2019.

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Hierarchical Importance Weighted Autoencoders. Chin-Wei Huang, Kris Sankaran, Eeshan Dhekane, Alexandre Lacoste, Aaron Courville. At International Conference on Machine Learning (ICML), 2019.

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Investigating Trust Factors in Human-Robot Shared Control: Implicit Gender Bias Around Robot Voice. Alexander Wong, Anqi Xu, Gregory Dudek. At Conference on Computer and Robotic Vision (CRV), 2019.

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Pix2Shape: Towards Unsupervised Learning of 3D Scenes from Images using a View-based Representation. Sai Rajeswar Mudumba, Fahim Mannan, Florian Golemo, Jérôme Parent-Lévesque, David Vazquez, Derek Nowrouzezahrai, Aaron Courville. At International Journal in Computer Vision (IJCV), 2019.

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Towards Standardization of Data Licenses: The Montreal Data License. Misha Benjamin, Paul Gagnon, Negar Rostamzadeh, Christopher Pal, Yoshua Bengio, Alex Shee. At Workshop at the International Conference on Learning Representations (ICLR), 2019.

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Systematic Generalization: What Is Required and Can It Be Learned?. Dzmitry Bahdanau, Michael Noukhovitch, Thien Huu Nguyen, Harm de Vries, Aaron Courville, Shikhar Murty. At International Conference on Learning Representations (ICLR), 2019.

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Reproducibility and Stability Analysis in Metric-Based Few-Shot Learning. Nathan Schucher, Denis Kocetkov, Laure Delisle, Thomas Boquet, Julien Cornebise. At Workshop at the International Conference on Learning Representations (ICLR), 2019.

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Quaternion Recurrent Neural Networks. Titouan Parcollet, Mirco Ravanelli, Mohamed Morchid, Georges Linarès, Chiheb Trabelsi, Renato De Mori, Yoshua Bengio. At International Conference on Learning Representations (ICLR), 2019.

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Probabilistic Planning with Sequential Monte Carlo Methods. Alexandre Piche, Valentin Thomas, Cyril Ibrahim, Yoshua Bengio, Christopher Pal. At International Conference on Learning Representations (ICLR), 2019.

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Planning with Latent SImulated Trajectories. Alexandre Piche, Valentin Thomas, Cyril Ibrahim, Julien Cornebise, Christopher Pal. At Workshop at the International Conference on Learning Representations (ICLR), 2019.

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On Difficulties of Probability Distillation. Chin-Wei Huang, Faruk Ahmed, Kundan Kumar, Alexandre Lacoste, Aaron Courville. At International Conference on Learning Representations (ICLR), 2019.

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Meta-Learning Framework with Applications to Zero-Shot Time Series Forecasting. Boris N. Oreshkin, Dmitri Carpov, Nicolas Chapados, Yoshua Bengio. At International Conference on Learning Representations (ICLR), 2019.

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BabyAI: A Platform to Study the Sample Efficiency of Grounded Language Learning. Maxime Chevalier-Boisvert, Dzmitry Bahdanau, Salem Lahlou, Lucas Willems, Chitwan Saharia, Thien Huu Nguyen, Yoshua Bengio. At International Conference on Learning Representations (ICLR), 2019.

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Adversarial Mixup Resynthesizers. Christopher Beckham, Sina Honari, Vikas Verma, Alex Lamb, Farnoosh Ghadiri, R Devon Hjelm, Yoshua Bengio, Christopher Pal. At Workshop at the International Conference on Learning Representations (ICLR), 2019.

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Adversarial Learning of General Transformations for Data Augmentation. Saypraseuth Mounsaveng, David Vazquez, Ismail Ben Ayed, Marco Pedersoli. At Workshop at the International Conference on Learning Representations (ICLR), 2019.

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Adaptive Masked Weight Imprinting for Few-Shot Segmentation. Mennatullah Siam, Boris N. Oreshkin. At Workshop at the International Conference on Learning Representations (ICLR), 2019.

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Adaptive Cross-Modal Few-shot Learning. Chen Xing, Negar Rostamzadeh, Boris N. Oreshkin, Pedro O. Pinheiro. At Workshop at the International Conference on Learning Representations (ICLR), 2019.

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Semantics Preserving Adversarial Learning. Ousmane Amadou Dia, Elnaz Barshan, Reza Babanezhad. At ArXiv, 2019.

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The Lottery Ticket Hypothesis at Scale. Jonathan Frankle, Gintare Karolina Dziugaite, Daniel M. Roy, Michael Carbin. At ArXiv, 2019.

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Improving Optimization Bounds using Machine Learning: Decision Diagrams meet Deep Reinforcement Learning. Quentin Cappart, Emmanuel Goutierre, David Bergman, Louis-Martin Rousseau. At Association for the Advancement of Artificial Intelligence (AAAI), 2019.

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Adversarial Framing for Image and Video Classification. Konrad Zolna, Michal Zajac, Negar Rostamzadeh, Pedro O. Pinheiro. At Student Abstract at the Association for the Advancement of Artificial Intelligence (AAAI), 2019.

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Hinted Networks. Joel Lamy Poirier, Anqi Xu. At ArXiv, 2018.

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Recurrent Transition Networks for Character Locomotion. Félix G. Harvey, Christopher Pal. At Conference and Exhibition on Computer Graphics and Interactive Techniques in Asia (SIGGRAPH Asia), 2018.

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Fashion-Gen: The Generative Fashion Dataset and Challenge. Negar Rostamzadeh, Seyedarian (Arian) Hosseini, Thomas Boquet, Wojciech Stokowiec, Ying Zhang, Christian Jauvin, Christopher Pal. At Women in Machine Learning (WiML), 2018.

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Unsupervised Depth Estimation, 3D Face Rotation and Replacement. Joel Ruben Antony Moniz, Christopher Beckham, Simon Rajotte, Sina Honari, Christopher Pal. At Conference on Neural Information Processing Systems (NeurIPS), 2018.

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Towards Text Generation with Adversarially Learned Neural Outlines. Sandeep Subramanian, Sai Rajeswar Mudumba, Alessandro Sordoni, Adam Trischler, Aaron Courville, Christopher Pal. At Conference on Neural Information Processing Systems (NeurIPS), 2018.

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Towards Deep Conversational Recommendations. Raymond Li, Samira Ebrahimi Kahou, Hannes Schulz, Vincent Michalski, Laurent Charlin, Christopher Pal. At Conference on Neural Information Processing Systems (NeurIPS), 2018.

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TADAM: Task dependent adaptive metric for improved few-shot learning. Boris N. Oreshkin, Pau Rodriguez, Alexandre Lacoste. At Conference on Neural Information Processing Systems (NeurIPS), 2018.

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Sparse Attentive Backtracking: Temporal Credit Assignment Through Reminding. Nan Rosemary Ke, Anirudh Goyal, Olexa Bilaniuk, Jonathan Binas, Christopher Pal, Yoshua Bengio, Michael C. Mozer. At Conference on Neural Information Processing Systems (NeurIPS), 2018.

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Reinforced Imitation in Heterogeneous Action Space. Konrad Zolna, Negar Rostamzadeh, Yoshua Bengio, Sungjin Ahn, Pedro O. Pinheiro. At Workshop at the Neural Information Processing Systems (NeurIPS), 2018.

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Improving Explorability in Variational Inference with Annealed Variational Objectives. Chin-Wei Huang, Shawn Tan, Alexandre Lacoste, Aaron Courville. At Conference on Neural Information Processing Systems (NeurIPS), 2018.

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Data-dependent PAC-Bayes priors via differential privacy. Gintare Karolina Dziugaite, Daniel M. Roy. At Conference on Neural Information Processing Systems (NeurIPS), 2018.

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Bayesian Model-Agnostic Meta-Learning. Taesup Kim, Jaesik Yoon, Sungwoong Kim, Yoshua Bengio, Sungjin Ahn. At Conference on Neural Information Processing Systems (NeurIPS), 2018.

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A large-scale crowd-sourced analysis of abuse against women journalists and politicians on Twitter. Laure Delisle, Alfredo Kalaitzis, Krzysztof Majewski, Archy de Berker, Milena Marin, Julien Cornebise. At Workshop at the Neural Information Processing Systems (NeurIPS), 2018.

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Adversarially-Trained Normalized Noisy-Feature Auto-Encoder for Text Generation. Xiang Zhang, Yann LeCun. At ArXiv, 2018.

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Interpretable genotype-to-phenotype classifiers with performance guarantees. Alexandre Drouin, Gaël Letarte, Frédéric Raymond, Mario Marchand, Jacques Corbeil, François Laviolette. At Nature Scientific Reports, 2018.

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Where are the Blobs: Counting by Localization with Point Supervision. Issam H. Laradji, Negar Rostamzadeh, Pedro O. Pinheiro, David Vazquez, Mark Schmidt. At European Conference on Computer Vision (ECCV), 2018.

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Quaternion Convolutional Neural Networks for End-to-End Automatic Speech Recognition. Titouan Parcollet, Ying Zhang, Mohamed Morchid, Chiheb Trabelsi, Georges Linarès, Renato De Mori, Yoshua Bengio. At Interspeech Conference, 2018.

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Synbols: Probing Learning Algorithms with Synthetic Datasets. Alexandre Lacoste, Pau Rodriguez, Frédéric Branchaud-Charron, Parmida Atighhehchian, Massimo Caccia, Issam H. Laradji, Alexandre Drouin, Matt Craddock, Laurent Charlin, David Vazquez. At Montreal AI Symposium (MAIS), 2018.

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Maximal Jacobian-based Saliency Map Attack. Rey Reza Wiyatno, Anqi Xu. At Montreal AI Symposium (MAIS), 2018.

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Bayesian Model-Agnostic Meta-Learning. Taesup Kim, Jaesik Yoon, Ousmane Amadou Dia, Sungwoong Kim, Yoshua Bengio, Sungjin Ahn. At Montreal AI Symposium (MAIS), 2018.

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LTL and Beyond: Formal Languages for Reward Function Specification in Reinforcement Learning. Alberto Camacho, Rodrigo Toro Icarte, Toryn Q. Klassen, Richard Valenzano, Sheila A. McIlraith. At International Join Conference on Artificial Intelligence (IJCAI), 2018.

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Slanted Stixels: A way to represent steep streets. Daniel Hernandez, Lukas Schneider, Pau Cebrian, Antonio Espinosa, David Vazquez, Antonio M. Lopez, Uwe Franke, Marc Pollefeys, Juan C. Moure. At International Journal in Computer Vision (IJCV), 2018.

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Using Reward Machines for High-Level Task Specification and Decomposition in Reinforcement Learning. Rodrigo Toro Icarte, Toryn Q. Klassen, Richard Valenzano, Sheila A. McIlraith. At International Conference on Machine Learning (ICML), 2018.

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Teaching Multiple Tasks to an RL Agent using LTL. Rodrigo Toro Icarte, Toryn Q. Klassen, Richard Valenzano, Sheila A. McIlraith. At Autonomous Agents and Multiagent Systems (AAMAS), 2018.

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Neural Autoregressive Flows. Chin-Wei Huang, David Krueger, Alexandre Lacoste, Aaron Courville. At International Conference on Machine Learning (ICML), 2018.

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Hierarchical Adversarially Learned Inference. Ishmael Belghazi, Sai Rajeswar Mudumba, Olivier Mastropietro, Negar Rostamzadeh, Jovana Mitrovic, Aaron Courville. At Workshop at the International Conference on Machine Learning (ICML), 2018.

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Fashion-Gen: The Generative Fashion Dataset and Challenge. Negar Rostamzadeh, Seyedarian (Arian) Hosseini, Thomas Boquet, Wojciech Stokowiec, Ying Zhang, Christian Jauvin, Christopher Pal. At Workshop at the International Conference on Machine Learning (ICML), 2018.

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Learning Heuristics for the TSP by Policy Gradient. Michel Deudon, Pierre Cournut, Alexandre Lacoste, Yossiri Adulyasak, Louis-Martin Rousseau. At International Conference on the Integration of Constraint Programming, Artificial Intelligence, and Operations Research, 2018.

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Unsupervised Domain Adaptation with Similarity Learning. Pedro O. Pinheiro. At Computer Vision and Pattern Recognition (CVPR), 2018.

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Strong Baselines for Simple Question Answering over Knowledge Graphs with and without Neural Networks. Salman Mohammed, Peng Shi, Jimmy Li. At North American Chapter of the Association for Computational Linguistics (NAACL), 2018.

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Pay attention to the activations: a modular attention mechanism for fine-grained image recognition. Pau Rodriguez, Diego Velazquez, Guillem Cucurull, Josep M. Gonfaus, F. Xavier Roca, Jordi Gonzalez. At Transitions on Multimedia (IEEE TM), 2018.

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Advice-Based Exploration in Model-Based Reinforcement Learning. Rodrigo Toro Icarte, Toryn Q. Klassen, Richard Valenzano, Sheila A. McIlraith. At Canadian Conference on AI, 2018.

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Deep Complex Networks. Chiheb Trabelsi, Olexa Bilaniuk, Ying Zhang, Dmitriy Serdyuk, Sandeep Subramanian, João Felipe Santos, Soroush Mehri, Negar Rostamzadeh, Yoshua Bengio, Christopher Pal. At International Conference on Learning Representations (ICLR), 2018.

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Class-Based Styling: Real-time Localized Style Transfer with Semantic Segmentation. Lironne Kurzman, David Vazquez, Issam H. Laradji. At Workshop at the International Conference on Learning Representations (ICLR), 2018.

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Learning to Remove Rain in Traffic Surveillance by Using Synthetic Data. Chris H. Bahnsen, David Vazquez, Antonio M. Lopez, Thomas B. Moeslun. At International Conference on Computer Vision Theory and Applications (VISIGRAPP), 2018.

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Deep learning for electromyographic hand gesture signal classification using transfer learning. Ulysse Cote-Allard, Cheikh Latyr Fall, Alexandre Drouin, Alexandre Campeau-Lecours, Clement Gosselin, Kyrre Glette, François Laviolette, Benoit Gosselin. At Transactions on Neural Systems and Rehabilitation Engineering (IEEE NSRE), 2018.

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How One Company Made Its Analytics Investment Pay Off. Jose A. Murillo, Yuri Levin, Mikhail Nediak, Ivan Sergienko. At Harvard Business Review (HBR), 2018.

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Disentangling the independently controllable factors of variation by interacting with the world. Valentin Thomas, Emmanuel Bengio, William Fedus, Jules Pondard, Philippe Beaudoin, Hugo Larochelle, Joelle Pineau, Doina Precup, Yoshua Bengio. At Workshop at the Neural Information Processing Systems (NeurIPS), 2017.

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Deep Prior. Alexandre Lacoste, Thomas Boquet, Negar Rostamzadeh, Boris N. Oreshkin, Wonchang Chung, David Krueger. At Workshop at the Neural Information Processing Systems (NeurIPS), 2017.

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Bayesian Hypernetworks. David Krueger, Chin-Wei Huang, Riashat Islam, Ryan Turner, Alexandre Lacoste, Aaron Courville. At Workshop at the Neural Information Processing Systems (NeurIPS), 2017.

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Underwater Multi-Robot Convoying using Visual Tracking by Detection. Florian Shkurti, Wei-Di Chang, Peter Henderson, Md Jahidul Islam, Juan Camilo Gamboa Higuera, Jimmy Li, Travis Manderson, Anqi Xu, Gregory Dudek, Junaed Sattar. At International Conference on Intelligent Robots and Systems (IROS), 2017.

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Efficient Multi-Robot Coverage of a Known Environment. Nare Karapetyan, Kelly Benson, Chris McKinney, Perouz Taslakian, Ioannis Rekleitis. At International Conference on Intelligent Robots and Systems (IROS), 2017.

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Accurate Supervised and Semi-Supervised Machine Reading for Long Documents. Daniel Hewlett, Llion Jones, Alexandre Lacoste, Izzeddin Gur. At Conference on Empirical Methods in Natural Language Processing (EMNLP), 2017.

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Independently Controllable Factors. Valentin Thomas, Jules Pondard, Emmanuel Bengio, Marc Sarfati, Philippe Beaudoin, Marie-Jean Meurs, Joelle Pineau, Doina Precup, Yoshua Bengio. At ArXiv, 2017.

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Continuous Yao Graphs. Luis Barba, Prosenjit Bose, Jean-Lou De Carufel, Mirela Damian, Rolf Fagerberg, André van Renssen, Perouz Taslakian, Sander Verdonschot. At Journal on Computational Geometry (CG), 2017.

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Coarse-to-Fine Question Answering for Long Documents. Eunsol Choi, Daniel Hewlett, Jakob Uszkoreit, Illia Polosukhin, Alexandre Lacoste, Jonathan Berant. At Annual Meeting of the Association for Computational Linguistics (ACL), 2016.

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