Portrait de Chris Pal

Chris Pal

Membre académique principal

christopher.pal@mila.quebec

Chaire en IA Canada-CIFAR

Professeur titulaire, Polytechnique Montréal, Département de génie informatique et de génie logiciel

Professeur associé, Université de Montréal, Département d'informatique et de recherche opérationnelle

Sujets de recherche

Apprentissage profond

Biographie

Christopher Pal est titulaire d'une chaire en IA Canada-CIFAR, professeur titulaire à Polytechnique Montréal et professeur adjoint au Département d'informatique et de recherche opérationnelle (DIRO) de l'Université de Montréal. Il est également chercheur émérite à ServiceNow Research. Il est engagé dans la recherche sur l'intelligence artificielle et l'apprentissage automatique depuis plus de 25 ans, publiant souvent des travaux sur les méthodes de modélisation du langage à grande échelle et les techniques de modélisation générative. Il a obtenu un doctorat en informatique à l'Université de Waterloo.

Étudiants actuels

Stagiaire de recherche - McGill

ababneh.mai@gmail.com

Shubham Agarwal

Postdoctorat - HEC

Superviseur⋅e principal⋅e :

Laurent Charlin

Collaborateur·rice de recherche - McGill

Superviseur⋅e principal⋅e :

Derek Nowrouzezahrai

paul.b.barde@gmail.com

Maîtrise recherche - UdeM

Doctorat - Polytechnique

Doctorat - McGill

Superviseur⋅e principal⋅e :

Xue (Steve) Liu

chencan421@gmail.com

Doctorat - UdeM

Superviseur⋅e principal⋅e :

Amine El-hattami

Doctorat - Polytechnique

Maîtrise recherche - UdeM

Co-superviseur⋅e :

Derek Nowrouzezahrai

Matthew Fortier

Collaborateur·rice alumni - Polytechnique

Doctorat - Polytechnique

Postdoctorat - McGill

Co-superviseur⋅e :

fgolemo@gmail.com

Anthony Gosselin

Maîtrise recherche - Polytechnique

Doctorat - UdeM

Co-superviseur⋅e :

Maîtrise recherche - Concordia

Co-superviseur⋅e :

Collaborateur·rice de recherche - UdeM

Maîtrise recherche - UdeM

Doctorat - UdeM

Doctorat - Polytechnique

Jonathan Pilault

Doctorat - Polytechnique

Doctorat - École de technologie suprérieure

Doctorat - UdeM

Superviseur⋅e principal⋅e :

Postdoctorat - HEC

Superviseur⋅e principal⋅e :

Laurent Charlin

Doctorat - Polytechnique

Superviseur⋅e principal⋅e :

Mattie Tesfaldet

Doctorat - McGill

Superviseur⋅e principal⋅e :

Derek Nowrouzezahrai

Doctorat - Polytechnique

Billets de blogue

Direct Behavior Specification via Constrained Reinforcement Learning

31 août 2022

Spécification directe du comportement par apprentissage par renforcement sous contrainte

par

Julien Roy

Joshua Romoff

Pierre-Luc Bacon

Chris Pal

Publications

Spaced Scheduling for Large Language Model Training

Amine El hattami

Nicolas Chapados

2025-06-09

TMLR (accepté)

Ctrl-Crash: Controllable Diffusion for Realistic Car Crashes

Anthony Gosselin

Ge Ya Luo

Luis Lara

Florian Golemo

Derek Nowrouzezahrai

Alexia Jolicoeur-Martineau

Video diffusion techniques have advanced significantly in recent years; however, they struggle to generate realistic imagery of car crashes … (voir plus)due to the scarcity of accident events in most driving datasets. Improving traffic safety requires realistic and controllable accident simulations. To tackle the problem, we propose Ctrl-Crash, a controllable car crash video generation model that conditions on signals such as bounding boxes, crash types, and an initial image frame. Our approach enables counterfactual scenario generation where minor variations in input can lead to dramatically different crash outcomes. To support fine-grained control at inference time, we leverage classifier-free guidance with independently tunable scales for each conditioning signal. Ctrl-Crash achieves state-of-the-art performance across quantitative video quality metrics (e.g., FVD and JEDi) and qualitative measurements based on a human-evaluation of physical realism and video quality compared to prior diffusion-based methods.

2025-05-30

ArXiv (prépublication)

Rendering-Aware Reinforcement Learning for Vector Graphics Generation

Juan A. Rodriguez

Haotian Zhang

Abhay Puri

Aarash Feizi

Rishav Pramanik

Pascal Wichmann

Arnab Mondal

Mohammad Reza Samsami

Rabiul Awal

Perouz Taslakian

Spandana Gella

Sai Rajeswar

David Vazquez

Marco Pedersoli

Scalable Vector Graphics (SVG) offer a powerful format for representing visual designs as interpretable code. Recent advances in vision-lang… (voir plus)uage models (VLMs) have enabled high-quality SVG generation by framing the problem as a code generation task and leveraging large-scale pretraining. VLMs are particularly suitable for this task as they capture both global semantics and fine-grained visual patterns, while transferring knowledge across vision, natural language, and code domains. However, existing VLM approaches often struggle to produce faithful and efficient SVGs because they never observe the rendered images during training. Although differentiable rendering for autoregressive SVG code generation remains unavailable, rendered outputs can still be compared to original inputs, enabling evaluative feedback suitable for reinforcement learning (RL). We introduce RLRF(Reinforcement Learning from Rendering Feedback), an RL method that enhances SVG generation in autoregressive VLMs by leveraging feedback from rendered SVG outputs. Given an input image, the model generates SVG roll-outs that are rendered and compared to the original image to compute a reward. This visual fidelity feedback guides the model toward producing more accurate, efficient, and semantically coherent SVGs. RLRF significantly outperforms supervised fine-tuning, addressing common failure modes and enabling precise, high-quality SVG generation with strong structural understanding and generalization.

2025-05-27

ArXiv (prépublication)

Ctrl-V: Higher Fidelity Autonomous Vehicle Video Generation with Bounding-Box Controlled Object Motion

Ge Ya Luo

Zhi Hao Luo

Anthony Gosselin

Alexia Jolicoeur-Martineau

2025-05-10

TMLR (accepté)

Distilling semantically aware orders for autoregressive image generation

Rishav Pramanik

Antoine Poupon

Juan A. Rodriguez

Masih Aminbeidokhti

David Vazquez

Zhaozheng Yin

Marco Pedersoli

2025-04-23

ArXiv (prépublication)

Distilling semantically aware orders for autoregressive image generation

Rishav Pramanik

Antoine Poupon

Juan A. Rodriguez

Masih Aminbeidokhti

David Vazquez

Zhaozheng Yin

Marco Pedersoli

2025-04-23

ArXiv (prépublication)

StarVector: Generating Scalable Vector Graphics Code from Images and Text

Juan A. Rodriguez

Abhay Puri

Shubham Agarwal

Issam Hadj Laradji

Pau Rodriguez

Sai Rajeswar

David Vazquez

Marco Pedersoli

2025-04-11

Proceedings of the AAAI Conference on Artificial Intelligence (publié)

AgentAda: Skill-Adaptive Data Analytics for Tailored Insight Discovery

Amirhossein Abaskohi

Amrutha Varshini Ramesh

Shailesh Nanisetty

Chirag Goel

David Vazquez

Spandana Gella

Giuseppe Carenini

Issam Hadj Laradji

2025-04-10

ArXiv (prépublication)

AgentAda: Skill-Adaptive Data Analytics for Tailored Insight Discovery

Amirhossein Abaskohi

Amrutha Varshini Ramesh

Shailesh Nanisetty

Chirag Goel

David Vazquez

Spandana Gella

Giuseppe Carenini

Issam Hadj Laradji

2025-04-01

arXiv (publié)

LitLLMs, LLMs for Literature Review: Are we there yet?

Shubham Agarwal

Gaurav Sahu

Abhay Puri

Issam Hadj Laradji

Krishnamurthy Dj Dvijotham

Jason Stanley

Laurent Charlin

2025-04-01

TMLR (accepté)

Scenario Dreamer: Vectorized Latent Diffusion for Generating Driving Simulation Environments

Luke Rowe

Roger Girgis

Anthony Gosselin

Felix Heide

2025-03-28

ArXiv (prépublication)

Scenario Dreamer: Vectorized Latent Diffusion for Generating Driving Simulation Environments

Luke Rowe

Roger Girgis

Anthony Gosselin

Felix Heide

2025-03-28

ArXiv (prépublication)