Portrait of Chris Pal

Chris Pal

Core Academic Member

christopher.pal@mila.quebec

Canada CIFAR AI Chair

Full Professor, Polytechnique Montréal, Department of Computer Engineering and Software Engineering

Assistant Professor, Université de Montréal, Department of Computer Science and Operations Research

Research Topics

Deep Learning

Biography

Christopher Pal is a Canada CIFAR AI Chair, full professor at Polytechnique Montréal and adjunct professor in the Department of Computer Science and Operations Research (DIRO) at Université de Montréal. He is also a Distinguished Scientist at ServiceNow Research.

Pal has been involved in AI and machine learning research for over twenty-five years and has published extensively on large-scale language modelling methods and generative modelling techniques. He has a PhD in computer science from the University of Waterloo.

Current Students

Collaborating researcher - Formerly McGill University (but ending)

Collaborating researcher - McGill University

Principal supervisor :

Derek Nowrouzezahrai

Master's Research - Université de Montréal

Collaborating Alumni - McGill University

Principal supervisor :

Xue (Steve) Liu

PhD - Université de Montréal

Principal supervisor :

Amine El-hattami

PhD - Polytechnique Montréal

Master's Research - Université de Montréal

Co-supervisor :

Derek Nowrouzezahrai

PhD - Polytechnique Montréal

PhD - Université de Montréal

Co-supervisor :

Master's Research - Concordia University

Co-supervisor :

Olivier Lessard

PhD - Polytechnique Montréal

Co-supervisor :

Master's Research - Université de Montréal

PhD - Université de Montréal

Aristides Milios

PhD - Université de Montréal

PhD - Polytechnique Montréal

PhD - École de technologie suprérieure

PhD - Université de Montréal

Principal supervisor :

Postdoctorate - HEC Montréal

Principal supervisor :

Laurent Charlin

PhD - Polytechnique Montréal

Principal supervisor :

Mattie Tesfaldet

Collaborating researcher - McGill University

Principal supervisor :

Derek Nowrouzezahrai

Postdoctorate - Polytechnique Montréal

Co-supervisor :

Hugo Larochelle

PhD - Université de Montréal

Collaborating researcher

Blog Posts

Direct Behavior Specification via Constrained Reinforcement Learning

August 31, 2022

Direct Behavior Specification via Constrained Reinforcement Learning

by

Julien Roy

Joshua Romoff

Pierre-Luc Bacon

Chris Pal

Read the article

Publications

Towards Deep Conversational Recommendations

Raymond Li

Samira Ebrahimi Kahou

Hannes Schulz

Vincent Michalski

Laurent Charlin

There has been growing interest in using neural networks and deep learning techniques to create dialogue systems. Conversational recommendat… (see more)ion is an interesting setting for the scientific exploration of dialogue with natural language as the associated discourse involves goal-driven dialogue that often transforms naturally into more free-form chat. This paper provides two contributions. First, until now there has been no publicly available large-scale dataset consisting of real-world dialogues centered around recommendations. To address this issue and to facilitate our exploration here, we have collected ReDial, a dataset consisting of over 10,000 conversations centered around the theme of providing movie recommendations. We make this data available to the community for further research. Second, we use this dataset to explore multiple facets of conversational recommendations. In particular we explore new neural architectures, mechanisms, and methods suitable for composing conversational recommendation systems. Our dataset allows us to systematically probe model sub-components addressing different parts of the overall problem domain ranging from: sentiment analysis and cold-start recommendation generation to detailed aspects of how natural language is used in this setting in the real world. We combine such sub-components into a full-blown dialogue system and examine its behavior.

2017-12-31

Advances in Neural Information Processing Systems 31 (NeurIPS 2018) (published)

Towards Text Generation with Adversarially Learned Neural Outlines.

Sandeep Subramanian

Sai Rajeswar

Alessandro Sordoni

Adam Trischler

Aaron C. Courville

2017-12-31

Advances in Neural Information Processing Systems 31 (NeurIPS 2018) (published)

dblp.uni-trier.de

Actual: Actor-Critic Under Adversarial Learning

Nan Rosemary Ke

R Devon Hjelm

Generative Adversarial Networks (GANs) are a powerful framework for deep generative modeling. Posed as a two-player minimax problem, GANs ar… (see more)e typically trained end-to-end on real-valued data and can be used to train a generator of high-dimensional and realistic images. However, a major limitation of GANs is that training relies on passing gradients from the discriminator through the generator via back-propagation. This makes it fundamentally difficult to train GANs with discrete data, as generation in this case typically involves a non-differentiable function. These difficulties extend to the reinforcement learning setting when the action space is composed of discrete decisions. We address these issues by reframing the GAN framework so that the generator is no longer trained using gradients through the discriminator, but is instead trained using a learned critic in the actor-critic framework with a Temporal Difference (TD) objective. This is a natural fit for sequence modeling and we use it to achieve improvements on language modeling tasks over the standard Teacher-Forcing methods.

2017-11-12

ArXiv (preprint)

Sparse Attentive Backtracking: Long-Range Credit Assignment in Recurrent Networks

Nan Rosemary Ke

Laurent Charlin

A major drawback of backpropagation through time (BPTT) is the difficulty of learning long-term dependencies, coming from having to propagat… (see more)e credit information backwards through every single step of the forward computation. This makes BPTT both computationally impractical and biologically implausible. For this reason, full backpropagation through time is rarely used on long sequences, and truncated backpropagation through time is used as a heuristic. However, this usually leads to biased estimates of the gradient in which longer term dependencies are ignored. Addressing this issue, we propose an alternative algorithm, Sparse Attentive Backtracking, which might also be related to principles used by brains to learn long-term dependencies. Sparse Attentive Backtracking learns an attention mechanism over the hidden states of the past and selectively backpropagates through paths with high attention weights. This allows the model to learn long term dependencies while only backtracking for a small number of time steps, not just from the recent past but also from attended relevant past states.

2017-11-06

ArXiv (preprint)

Brain Tumor Segmentation with Deep Neural Networks

Mohammad Havaei

David Warde-Farley

Aaron Courville

Pierre-Marc Jodoin

Hugo Larochelle

2016-05-20

Medical Image Analysis (unknown)