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

Capture the Flag: Uncovering Data Insights with Large Language Models

Issam Hadj Laradji

Perouz Taslakian

Sai Rajeswar

Valentina Zantedeschi

Alexandre Lacoste

Nicolas Chapados

David Vazquez

Alexandre Drouin

2023-11-07

NeurIPS.cc/2023/Workshop/FMDM (published)

doi.org

Are Diffusion Models Vision-And-Language Reasoners?

Benno Krojer

Elinor Poole-Dayan

Vikram Voleti

Siva Reddy

Text-conditioned image generation models have recently shown immense qualitative success using denoising diffusion processes. However, unlik… (see more)e discriminative vision-and-language models, it is a non-trivial task to subject these diffusion-based generative models to automatic fine-grained quantitative evaluation of high-level phenomena such as compositionality. Towards this goal, we perform two innovations. First, we transform diffusion-based models (in our case, Stable Diffusion) for any image-text matching (ITM) task using a novel method called DiffusionITM. Second, we introduce the Generative-Discriminative Evaluation Benchmark (GDBench) benchmark with 7 complex vision-and-language tasks, bias evaluation and detailed analysis. We find that Stable Diffusion + DiffusionITM is competitive on many tasks and outperforms CLIP on compositional tasks like like CLEVR and Winoground. We further boost its compositional performance with a transfer setup by fine-tuning on MS-COCO while retaining generative capabilities. We also measure the stereotypical bias in diffusion models, and find that Stable Diffusion 2.1 is, for the most part, less biased than Stable Diffusion 1.5. Overall, our results point in an exciting direction bringing discriminative and generative model evaluation closer. We will release code and benchmark setup soon.

Block-State Transformers

Jonathan Pilault

Mahan Fathi

Orhan Firat

Pierre-Luc Bacon

Ross Goroshin

2023-09-21

NeurIPS.cc/2023/Conference (poster)

Parallel-mentoring for Offline Model-based Optimization

Can Chen

Christopher Beckham

Zixuan Liu

Xue (Steve) Liu

Parallel-mentoring for Offline Model-based Optimization

Can Chen

Christopher Beckham

Zixuan Liu

Xue (Steve) Liu

We study offline model-based optimization to maximize a black-box objective function with a static dataset of designs and scores. These desi… (see more)gns encompass a variety of domains, including materials, robots, DNA sequences, and proteins. A common approach trains a proxy on the static dataset and performs gradient ascent to obtain new designs. However, this often results in poor designs due to the proxy inaccuracies for out-of-distribution designs. Recent studies indicate that (a) gradient ascent with a mean ensemble of proxies generally outperforms simple gradient ascent, and (b) a trained proxy provides weak ranking supervision signals for design selection. Motivated by (a) and (b), we propose

2023-09-21

NeurIPS.cc/2023/Conference (poster)

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.

2023-09-10

TMLR (accepted)

Bridging the Gap Between Target Networks and Functional Regularization

Alexandre Piché

Valentin Thomas

Joseph Marino

Gian Maria Marconi

Rafael Pardinas

Mohammad Emtiyaz Khan

2023-09-06

TMLR (accepted)

doi.org

Goal-conditioned GFlowNets for Controllable Multi-Objective Molecular Design

Julien Roy

Pierre-Luc Bacon

Emmanuel Bengio

In recent years, in-silico molecular design has received much attention from the machine learning community. When designing a new compound f… (see more)or pharmaceutical applications, there are usually multiple properties of such molecules that need to be optimised: binding energy to the target, synthesizability, toxicity, EC50, and so on. While previous approaches have employed a scalarization scheme to turn the multi-objective problem into a preference-conditioned single objective, it has been established that this kind of reduction may produce solutions that tend to slide towards the extreme points of the objective space when presented with a problem that exhibits a concave Pareto front. In this work we experiment with an alternative formulation of goal-conditioned molecular generation to obtain a more controllable conditional model that can uniformly explore solutions along the entire Pareto front.

2023-06-23

ICML.cc/2023/Workshop/DeployableGenerativeAI (published)

doi.org

Block-State Transformers

Mahan Fathi

Jonathan Pilault

Pierre-Luc Bacon