Chris Pal

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

Mai Ababneh

Research Intern - McGill University

ababneh.mai@gmail.com

Shubham Agarwal

Postdoctorate - HEC Montréal

Principal supervisor :

Paul Barde

Collaborating researcher - McGill University

Principal supervisor :

Derek Nowrouzezahrai

paul.b.barde@gmail.com

Master's Research - Université de Montréal

Chris Beckham

PhD - Polytechnique Montréal

Can (Sam) Chen

PhD - McGill University

Principal supervisor :

PhD - Université de Montréal

Principal supervisor :

PhD - Polytechnique Montréal

Chris Emezue

Master's Research - Université de Montréal

Co-supervisor :

Collaborating Alumni - Polytechnique Montréal

Roger Girgis

PhD - Polytechnique Montréal

Florian Golemo

Postdoctorate - McGill University

Co-supervisor :

Master's Research - Polytechnique Montréal

PhD - Université de Montréal

Co-supervisor :

Yousef Kotp

Master's Research - Concordia University

Co-supervisor :

Collaborating researcher - Université de Montréal

Master's Research - Université de Montréal

Olga Luo

PhD - Université de Montréal

Joel Moniz

PhD - Polytechnique Montréal

Jonathan Pilault

PhD - Polytechnique Montréal

Juan Rodriguez

PhD - École de technologie suprérieure

Luke Rowe

PhD - Université de Montréal

Principal supervisor :

Gaurav Sahu

Postdoctorate - HEC Montréal

Principal supervisor :

PhD - Polytechnique Montréal

Principal supervisor :

PhD - McGill University

Principal supervisor :

PhD - Polytechnique Montréal

Direct Behavior Specification via Constrained Reinforcement Learning

Blog Posts

August 31, 2022

Julien Roy

Roger Girgis

Joshua Romoff

Pierre-Luc Bacon

Chris Pal

Read the article

Publications

Language Decision Transformers with Exponential Tilt for Interactive Text Environments

Nicolas Gontier

Pau Rodriguez

Issam Hadj Laradji

David Vazquez

2023-02-10

ArXiv (preprint)

ArK: Augmented Reality with Knowledge Emergent Infrastructure

Qiuyuan Huang

J. Park

Abhinav Gupta

Pan Lu

Paul N. Bennett

Ran Gong

Subhojit Som

Baolin Peng

Owais Khan Mohammed

Yejin Choi

Jianfeng Gao

Despite the growing adoption of mixed reality and interactive AI, it remains challenging to generate high-quality 2D/3D scenes in unseen env… (see more)ironments. Typically, an AI agent requires collecting extensive training data for every new task, which can be costly or impossible for many domains. In this study, we develop an infinite agent that learns to transfer knowledge memory from general foundation models (e.g., GPT4, DALLE) to novel domains or scenarios for scene understanding and generation in physical or virtual worlds. Central to our approach is the interactive emerging mechanism, dubbed Augmented Reality with Knowledge Emergent Infrastructure (ArK) , which leverages knowledge-memory to generate scenes in unseen physical worlds and virtual reality environments. The knowledge interactive emergent ability (Figure 1) is demonstrated through i) micro-action of cross-modality : in multi-modality models to collect a large amount of relevant knowledge-memory data for each interaction task (e.g., unseen scene understanding) from the physical reality; and ii) macro-behavior of reality-agnostic : in mix-reality environments to improve interactions that tailor to different characterized roles, target variables, collaborative information, and so on. We validate ArK’s effectiveness in scene generation and editing tasks and show that our ArK approach, combined with large foundation models, significantly improves the quality of generated 2D/3D scenes, highlighting its potential in applications such as metaverse and gaming simulation.

Block-State Transformers

Mahan Fathi

Jonathan Pilault

Pierre-Luc Bacon

Orhan Firat

Ross Goroshin

2023-01-01

NeurIPS (published)

Block-State Transformers

Jonathan Pilault

Mahan Fathi

Orhan Firat

Pierre-Luc Bacon

Ross Goroshin

Neural Causal Structure Discovery from Interventions

Nan Rosemary Ke

Olexa Bilaniuk

Anirudh Goyal

Stefan Bauer

Hugo Larochelle

Bernhard Schölkopf

Michael Curtis Mozer

Yoshua Bengio

Recent promising results have generated a surge of interest in continuous optimization methods for causal discovery from observational data.… (see more) However, there are theoretical limitations on the identiﬁability of underlying structures obtained solely from observational data. Interventional data, on the other hand, provides richer information about the underlying data-generating process. Nevertheless, extending and applying methods designed for observational data to include interventions is a challenging problem. To address this issue, we propose a general framework based on neural networks to develop models that incorporate both observational and interventional data. Notably, our method can handle the challenging and realistic scenario where the identity of the intervened upon variable is unknown. We evaluate our proposed approach in the context of graph recovery, both de novo and from a partially-known edge set. Our method achieves strong benchmark results on various structure learning tasks, including structure recovery of synthetic graphs as well as standard graphs from the Bayesian Network Repository

2023-01-01

Trans. Mach. Learn. Res. (published)

dblp.uni-trier.de

Towards Learning to Imitate from a Single Video Demonstration

Glen Berseth

Florian Golemo

Agents that can learn to imitate given video observation -- \emph{without direct access to state or action information} are more applicable … (see more)to learning in the natural world. However, formulating a reinforcement learning (RL) agent that facilitates this goal remains a significant challenge. We approach this challenge using contrastive training to learn a reward function comparing an agent's behaviour with a single demonstration. We use a Siamese recurrent neural network architecture to learn rewards in space and time between motion clips while training an RL policy to minimize this distance. Through experimentation, we also find that the inclusion of multi-task data and additional image encoding losses improve the temporal consistency of the learned rewards and, as a result, significantly improves policy learning. We demonstrate our approach on simulated humanoid, dog, and raptor agents in 2D and a quadruped and a humanoid in 3D. We show that our method outperforms current state-of-the-art techniques in these environments and can learn to imitate from a single video demonstration.

Workflow Discovery from Dialogues in the Low Data Regime

Amine El hattami

Stefania Raimondo

Issam Hadj Laradji

David Vazquez

Pau Rodriguez

Text-based dialogues are now widely used to solve real-world problems. In cases where solution strategies are already known, they can someti… (see more)mes be codified into workflows and used to guide humans or artificial agents through the task of helping clients. We introduce a new problem formulation that we call Workflow Discovery (WD) in which we are interested in the situation where a formal workflow may not yet exist. Still, we wish to discover the set of actions that have been taken to resolve a particular problem. We also examine a sequence-to-sequence (Seq2Seq) approach for this novel task. We present experiments where we extract workflows from dialogues in the Action-Based Conversations Dataset (ABCD). Since the ABCD dialogues follow known workflows to guide agents, we can evaluate our ability to extract such workflows using ground truth sequences of actions. We propose and evaluate an approach that conditions models on the set of possible actions, and we show that using this strategy, we can improve WD performance. Our conditioning approach also improves zero-shot and few-shot WD performance when transferring learned models to unseen domains within and across datasets. Further, on ABCD a modified variant of our Seq2Seq method achieves state-of-the-art performance on related but different problems of Action State Tracking (AST) and Cascading Dialogue Success (CDS) across many evaluation metrics.

2023-01-01

Trans. Mach. Learn. Res. (published)

Implicit Offline Reinforcement Learning via Supervised Learning

Alexandre Piché

Rafael Pardinas

David Vazquez

Igor Mordatch

Offline Reinforcement Learning (RL) via Supervised Learning is a simple and effective way to learn robotic skills from a dataset of varied b… (see more)ehaviors. It is as simple as supervised learning and Behavior Cloning (BC) but takes advantage of the return information. On BC tasks, implicit models have been shown to match or outperform explicit ones. Despite the benefits of using implicit models to learn robotic skills via BC, Offline RL via Supervised Learning algorithms have been limited to explicit models. We show how implicit models leverage return information and match or outperform explicit algorithms to acquire robotic skills from fixed datasets. Furthermore, we show how closely related our implicit methods are to other popular RL via Supervised Learning algorithms.

2022-12-09

NeurIPS.cc/2022/Workshop/DeepRL (unknown)

Score-based Denoising Diffusion with Non-Isotropic Gaussian Noise Models

Vikram Voleti

Adam M. Oberman

Generative models based on denoising diffusion techniques have led to an unprecedented increase in the quality and diversity of imagery that… (see more) is now possible to create with neural generative models. However, most contemporary state-of-the-art methods are derived from a standard isotropic Gaussian formulation. In this work we examine the situation where non-isotropic Gaussian distributions are used. We present the key mathematical derivations for creating denoising diffusion models using an underlying non-isotropic Gaussian noise model. We also provide initial experiments with the CIFAR10 dataset to help verify empirically that this more general modelling approach can also yield high-quality samples.

2022-11-29

NeurIPS.cc/2022/Workshop/SBM (poster)

Improving Meta-Learning Generalization with Activation-Based Early-Stopping

Simon Guiroy

Goncalo Mordido

Sarath Chandar

2022-11-28

Proceedings of The 1st Conference on Lifelong Learning Agents (published)

Receptive Field Refinement for Convolutional Neural Networks Reliably Improves Predictive Performance

Mats Leon Richter

Minimal changes to neural architectures (e.g. changing a single hyperparameter in a key layer), can lead to significant gains in predictive … (see more)performance in Convolutional Neural Networks (CNNs). In this work, we present a new approach to receptive field analysis that can yield these types of theoretical and empirical performance gains across twenty well-known CNN architectures examined in our experiments. By further developing and formalizing the analysis of receptive field expansion in convolutional neural networks, we can predict unproductive layers in an automated manner before ever training a model. This allows us to optimize the parameter-efficiency of a given architecture at low cost. Our method is computationally simple and can be done in an automated manner or even manually with minimal effort for most common architectures. We demonstrate the effectiveness of this approach by increasing parameter efficiency across past and current top-performing CNN-architectures. Specifically, our approach is able to improve ImageNet1K performance across a wide range of well-known, state-of-the-art (SOTA) model classes, including: VGG Nets, MobileNetV1, MobileNetV3, NASNet A (mobile), MnasNet, EfficientNet, and ConvNeXt - leading to a new SOTA result for each model class.

2022-11-26

ArXiv (preprint)

SMPL-IK: Learned Morphology-Aware Inverse Kinematics for AI Driven Artistic Workflows

Vikram Voleti

Boris Oreshkin

Florent Bocquelet

Félix Harvey

Louis-Simon Ménard

2022-11-22

SIGGRAPH Asia 2022 Technical Communications (published)