Christopher Pal
Distinguished Scientist
Low Data Learning
Leadership team
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
Workflow discovery in low data regimes.
Workshop at the International Conference on Machine Learning (ICML),
2023.
Multilingual Code Retrieval Without Paired Data: A New Benchmark and Experiments.
Workshop at the International Conference on Learning Representations (ICLR),
2023.
Workflow discovery in low data regimes.
Transactions on Machine Learning Research,
2023.
Attention-based Neural Cellular Automata.
Conference on Neural Information Processing Systems (NeurIPS),
2022.
Implicit Offline Reinforcement Learning via Supervised Learning.
Workshop at the Neural Information Processing Systems (NeurIPS),
2022.
Masked Conditional Video Diffusion for Prediction, Generation, and Interpolation.
Conference on Neural Information Processing Systems (NeurIPS),
2022.
Neural Attentive CIrcuits.
Conference on Neural Information Processing Systems (NeurIPS),
2022.
Does entity abstraction help generative Transformers reason? .
Transactions on Machine Learning Research,
2022.
Overcoming challenges in leveraging GANs for few-shot data augmentation.
Workshop at the Conference on Lifelong Learning Agents (CoLLAs),
2022.
Beyond Target Networks: Improving Deep Q-learning with Functional Regularization.
Transactions on Machine Learning Research,
2022.
Direct Behavior Specification via Constrained Reinforcement Learning.
International Conference on Machine Learning (ICML),
2022.
A Probabilistic Perspective on Reinforcement Learning via Supervised Learning.
Workshop at the International Conference on Learning Representations (ICLR),
2022.
Latent Variable Sequential Set Transformers for Joint Multi-Agent Motion Prediction.
International Conference on Learning Representations (ICLR),
2022.
Learning to Guide and to Be Guided in the Architect-Builder Problem.
International Conference on Learning Representations (ICLR),
2022.
Visual Question Answering From Another Perspective: CLEVR Mental Rotation Tests.
Pattern Recognition (PR),
2022.
Systematic Evaluation of Causal Discovery in Visual Model Based Reinforcement Learning.
Conference on Neural Information Processing Systems (NeurIPS),
2021.
DuoRAT: Towards Simpler Text-to-SQL Models.
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.
International Conference on Learning Representations (ICLR),
2021.
Predicting Infectiousness for Proactive Contact Tracing.
International Conference on Learning Representations (ICLR),
2021.
Reinforcement Learning with Random Delays.
International Conference on Learning Representations (ICLR),
2021.
Adversarial Soft Advantage Fitting: Imitation Learning without Policy Optimization.
Conference on Neural Information Processing Systems (NeurIPS),
2020.
Measuring Systematic Generalization in Neural Proof Generation with Transformers.
Conference on Neural Information Processing Systems (NeurIPS),
2020.
Promoting Coordination through Policy Regularization in Multi-Agent Deep Reinforcement Learning.
Conference on Neural Information Processing Systems (NeurIPS),
2020.
On Extractive and Abstractive Neural Document Summarization with Transformer Language Models.
Conference on Empirical Methods in Natural Language Processing (EMNLP),
2020.
AR-DAE: Towards Unbiased Neural Entropy Gradient Estimation.
International Conference on Machine Learning (ICML),
2020.
A Meta-Transfer Objective for Learning to Disentangle Causal Mechanisms.
International Conference on Learning Representations (ICLR),
2020.
Finding and Visualizing Weaknesses of Deep Reinforcement Learning Agents.
International Conference on Learning Representations (ICLR),
2020.
Reinforced Active Learning for Image Segmentation.
International Conference on Learning Representations (ICLR),
2020.
Neural Multisensory Scene Inference.
Conference on Neural Information Processing Systems (NeurIPS),
2019.
On Adversarial Mixup Resynthesis.
Conference on Neural Information Processing Systems (NeurIPS),
2019.
Real-Time Reinforcement Learning.
Conference on Neural Information Processing Systems (NeurIPS),
2019.
Retrieving Signals in the Frequency Domain with Deep Complex Extractors.
Workshop at the Neural Information Processing Systems (NeurIPS),
2019.
Active Domain Randomization.
Conference on Robot Learning (CoRL),
2019.
SEVN: A Sidewalk Simulation Environment for Visual Navigation.
Conference on Robot Learning (CoRL),
2019.
On the impressive performance of randomly weighted encoders in summarization tasks.
Annual Meeting of the Association for Computational Linguistics (ACL),
2019.
Structure Learning for Neural Module Networks.
Annual Meeting of the Association for Computational Linguistics (ACL),
2019.
Adversarial Mixup Resynthesizers.
Workshop at the International Conference on Learning Representations (ICLR),
2019.
Planning with Latent SImulated Trajectories.
Workshop at the International Conference on Learning Representations (ICLR),
2019.
Probabilistic Planning with Sequential Monte Carlo Methods.
International Conference on Learning Representations (ICLR),
2019.
Towards Standardization of Data Licenses: The Montreal Data License.
Workshop at the International Conference on Learning Representations (ICLR),
2019.
Recurrent Transition Networks for Character Locomotion.
Conference and Exhibition on Computer Graphics and Interactive Techniques in Asia (SIGGRAPH Asia),
2018.
Fashion-Gen: The Generative Fashion Dataset and Challenge.
Women in Machine Learning (WiML),
2018.
Sparse Attentive Backtracking: Temporal Credit Assignment Through Reminding.
Conference on Neural Information Processing Systems (NeurIPS),
2018.
Towards Deep Conversational Recommendations.
Conference on Neural Information Processing Systems (NeurIPS),
2018.
Towards Text Generation with Adversarially Learned Neural Outlines.
Conference on Neural Information Processing Systems (NeurIPS),
2018.
Unsupervised Depth Estimation, 3D Face Rotation and Replacement.
Conference on Neural Information Processing Systems (NeurIPS),
2018.
Fashion-Gen: The Generative Fashion Dataset and Challenge.
Workshop at the International Conference on Machine Learning (ICML),
2018.
Deep Complex Networks.
International Conference on Learning Representations (ICLR),
2018.