Christopher Pal

Distinguished Scientist

Low Data Learning

Leadership team

Chris Pal is an associate professor in the Department of computer and software engineering at the École Polytechnique of Montreal and a Distinguished Scientist at ServiceNow Research. Prior to arriving in Montreal, he was a professor in the Department of Computer Science at the University of Rochester. He has been a research scientist with the University of Massachusetts and has also been affiliated with the Interactive Visual Media Group and the Machine Learning and Applied Statistics groups at Microsoft Research. His research at Microsoft lead to three patents on image processing, computer vision and interactive multimedia. Chris earned his masters in Math and PhD from the University of Waterloo in Canada. His PhD research led to contributions applying probability models and optimization techniques to image, video and signal processing. Prior to his graduate studies Chris was with the multimedia research company Interval in Palo Alto, CA (Silicon Valley). As a result of his research at Interval he was awarded a patent on audio signal processing.

Interests

Machine Learning
Computer Vision
Natural Language Processing
Reinforcement Learning
Deep Learning

Publications

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

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.

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.

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

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

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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

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.

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.

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.

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.

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.

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

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

Deep Complex Separators. Christopher Pal, Negar Rostamzadeh, Ying Zhang, Olexa Bilaniuk, Chiheb Trabelsi. At Workshop at the Neural Information Processing Systems (NeurIPS), 2019.

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.

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.

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

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.

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

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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, 2017.