Chris Pal

aditya.sharma@mila.quebec

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

Abhinav Gupta

PhD - Université de Montréal

PhD

Principal supervisor :

Amal Zouaq

PhD - Université de Montréal

picheale@mila.quebec

anthony.gosselin@mila.quebec

Amine El-hattami

PhD - Polytechnique Montréal

elhattaa@mila.quebec

Abdou Mousas

PhD - Université de Montréal

mousasab@mila.quebec

Anthony Gosselin

Master's Research - Polytechnique Montréal

PhD - McGill University

Principal supervisor :

Master's Research - Université de Montréal

Co-supervisor :

Derek Nowrouzezahrai

chris.emezue@mila.quebec

PhD - Polytechnique Montréal

christopher.beckham@mila.quebec

Ge Ya (olga Luo) Luo

PhD - Université de Montréal

xugeya@mila.quebec

Georges Belanger Albarran

Master's Research - Université de Montréal

georges.belangeralbarran@mila.quebec

Baudchon Hugo Baudchon

Collaborating researcher - Université de Montréal

Principal supervisor :

Étienne Laliberté

hugo.baudchon@mila.quebec

juan.rodriguez@mila.quebec

Jae Hyun Lim

PhD - Université de Montréal

PhD - Polytechnique Montréal

PhD - École de technologie suprérieure

Website

Julien Roy

PhD - Polytechnique Montréal

Co-supervisor :

Luke Rowe

PhD - Université de Montréal

Principal supervisor :

Liam Paull

luke.rowe@mila.quebec

mats-leon.richter@mila.quebec

Martin Weiss

PhD - Polytechnique Montréal

Postdoctorate - Université de Montréal

matthew.fortier@mila.quebec

Matthew Fortier

Master's Research - Polytechnique Montréal

mattie.tesfaldet@mila.quebec

Mattie Tesfaldet

PhD - McGill University

Principal supervisor :

Derek Nowrouzezahrai

Collaborating researcher

michelle.lin@mila.quebec

Website

shubham.agarwal@mila.quebec

Paul Barde

PhD - McGill University

Principal supervisor :

PhD - Polytechnique Montréal

girgisro@mila.quebec

Shubham Agarwal

Postdoctorate - HEC Montréal

Principal supervisor :

Laurent Charlin

Master's Research - Polytechnique Montréal

chamorrs@mila.quebec

Sarath Chandar Anbil Parthipan

Simon Guiroy

PhD - Université de Montréal

Co-supervisor :

PhD - Université de Montréal

voletivi@mila.quebec

Z Luo

PhD - Polytechnique Montréal

luozhiha@mila.quebec

Blog Posts

August 31, 2022

Direct Behavior Specification via Constrained Reinforcement Learning

Julien Roy

Roger Girgis

Joshua Romoff

Pierre-Luc Bacon

Chris Pal

Read the article

Publications

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

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)

Parallel-mentoring for Offline Model-based Optimization

Can Chen

Christopher Beckham

Zixuan Liu

Xue (Steve) Liu

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

Rafael Pardinas

Gian Maria Marconi

Mohammad Emtiyaz Khan

2023-09-06

TMLR (accepted)

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)

Improving Generalization in Task-oriented Dialogues with Workflows and Action Plans

Stefania Raimondo

Xiaotian Liu

David Vazquez

Hector. Palacios

2023-06-02

ArXiv (preprint)

arxiv.org

ArK: Augmented Reality with Knowledge Interactive Emergent Ability

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 agents, it remains challenging for these systems to generate high quality 2… (see more)D/3D scenes in unseen environments. The common practice requires deploying an AI agent to collect large amounts of data for model training for every new task. This process is costly, or even 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 the physical or virtual world. The heart of our approach is an emerging mechanism, dubbed Augmented Reality with Knowledge Inference Interaction (ArK), which leverages knowledge-memory to generate scenes in unseen physical world and virtual reality environments. The knowledge interactive emergent ability (Figure 1) is demonstrated as the observation learns 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 the effectiveness of ArK on the scene generation and editing tasks. We show that our ArK approach, combined with large foundation models, significantly improves the quality of generated 2D/3D scenes, compared to baselines, demonstrating the potential benefit of incorporating ArK in generative AI for applications such as metaverse and gaming simulation.

2023-05-01

ArXiv (preprint)

arxiv.org

Controllable Image Generation via Collage Representations

Arantxa Casanova

Marlene Careil

Adriana Romero Soriano

Jakob Verbeek

Michal Drozdzal

2023-04-26

ArXiv (preprint)

Conservative objective models are a special kind of contrastive divergence-based energy model

Christopher Beckham

In this work we theoretically show that conservative objective models (COMs) for offline model-based optimisation (MBO) are a special kind o… (see more)f contrastive divergence-based energy model, one where the energy function represents both the unconditional probability of the input and the conditional probability of the reward variable. While the initial formulation only samples modes from its learned distribution, we propose a simple fix that replaces its gradient ascent sampler with a Langevin MCMC sampler. This gives rise to a special probabilistic model where the probability of sampling an input is proportional to its predicted reward. Lastly, we show that better samples can be obtained if the model is decoupled so that the unconditional and conditional probabilities are modelled separately.

2023-04-07

ArXiv (preprint)

arxiv.org

Visual Question Answering From Another Perspective: CLEVR Mental Rotation Tests

Christopher Beckham

Martin Weiss

Florian Golemo

Sina Honari

Derek Nowrouzezahrai

2023-04-01

Pattern Recognition (published)