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

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StarCoder: may the source be with you!. Raymond Li, Loubna Ben Allal, Yangtian Zi, Niklas Muennighoff, Denis Kocetkov, Chenghao Mou, Marc Marone, Christopher Akiki, Jia Li, Jenny Chim, Qian Liu, Evgenii Zheltonozhskii, Terry Yue Zhuo, Thomas Wang, Olivier Dehaene, Mishig Davaadorj, Joel Lamy Poirier, João Monteiro, Oleh Shliazhko, Nicolas Gontier, Nicholas Meade, Armel Zebaze, Ming-Ho Yee, Logesh Kumar Umapathi, Jian Zhu, Benjamin Lipkin, Muhtasham Oblokulov, Zhiruo Wang, Rudra Murthy, Jason Stillerman, Siva Sankalp Patel, Dmitry Abulkhanov, Marco Zocca, Manan Dey, Zhihan Zhang, Nour Fahmy, Urvashi Bhattacharyya, Wenhao Yu, Swayam Singh, Sasha Luccioni, Paulo Villegas, Maxim Kunakov, Fedor Zhdanov, Manuel Romero, Tony Lee, Nadav Timor, Jennifer Ding, Claire Schlesinger, Hailey Schoelkopf, Jan Ebert, Tri Dao, Mayank Mishra, Alex Gu, 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. At ArXiv,  2023.

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Explaining explainable AI. Nicolas Chapados, Richard Zuroff. At Book Chapter,  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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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 2023, 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,  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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Leveraging Human Preferences to Master Poetry. Rafael Pardinas, Gabriel Huang, David Vazquez, Alexandre Piche. At AAAI Workshops,  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, 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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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,  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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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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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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Discrete Learning Of DAGs 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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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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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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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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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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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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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, Md Rifat Arefin, 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, Md Rifat Arefin, 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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Layered gradient accumulation and modular pipeline parallelism: fast and efficient training of large language models. Joel Lamy Poirier. At ArXiv,  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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TopiOCQA: Open-domain Conversational Question Answering with Topic Switching. Vaibhav Adlakha, Shehzaad Dhuliawala, Kaheer Suleman, Harm de Vries, Siva Reddy. At Annual Meeting 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 Lacoste-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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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 ACM Computing Surveys,  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 Lacoste-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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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, andrew williams. 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 Lacoste-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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Using Harms and Benefits to Ground Practical AI Fairness Assessments in Finance. Lachlan McCalman, Daniel Steinberg. At ACM Conference on Fairness, Accountability, and Transparency (FAccT),  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 Lacoste-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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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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Graph-Density Aware Losses for Novel Compositions in Scene Graph Generation. Boris Knyazev, Harm de Vries, Cătălina Cangea, Graham 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,  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 Lacoste-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 Lacoste-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 Lacoste-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, Md Rifat Arefin, 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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Optimal PAC-Bayes bounds require distribution- and data-dependent priors. Kyle Hsu, Waseem Gharbieh, Daniel M. Roy, Gintare Karolina Dziugaite. At Women in Machine Learning (WiML),  2019.

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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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Towards generalization guarantees for SGD: Data-dependent PAC-Bayes priors. Gabriel Arpino, Daniel M. Roy, Gintare Karolina Dziugaite. At Workshop at the Neural Information Processing Systems (NeurIPS),  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 Lacoste-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 Lacoste-Julien. At Conference on Neural Information Processing Systems (NeurIPS),  2019.

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Optimal PAC-Bayes bounds require distribution- and data-dependent priors. Kyle Hsu, Waseem Gharbieh, Daniel M. Roy, Gintare Karolina Dziugaite. At Workshop at the 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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Deep Complex Separators. Christopher Pal, Negar Rostamzadeh, Ying Zhang, Olexa Bilaniuk, Chiheb Trabelsi. At Workshop at the 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, Shikhar Murty, Philippe Beaudoin, Yoshua Bengio, Aaron Courville. At Workshop at the Neural Information Processing Systems (NeurIPS),  2019.

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Adversarial Functionality-Preserving Training in the Malware Domain. Ousmane Amadou Dia. 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,  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,  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,  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,  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, Shikhar Murty, Michael Noukhovitch, Thien Huu Nguyen, Harm de Vries, Aaron Courville. 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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An escape-time analysis of SGD. Mufan Li, Gintare Karolina Dziugaite, Philippe Casgrain. 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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Tighter Nonvacuous PAC-Bayes Bounds: Beyond Stability of SGD. Gabriel Arpino, Gintare Karolina Dziugaite, Daniel M. Roy. At Workshop at the 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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