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

Shubham Agarwal

Postdoctorate - HEC Montréal

Principal supervisor :

Paul Barde

PhD - McGill University

Principal supervisor :

Derek Nowrouzezahrai

paul.b.barde@gmail.com

Master's Research - Université de Montréal

Chris Beckham

PhD - Polytechnique Montréal

Georges Belanger Albarran

Master's Research - Université de Montréal

Simon Chamorro

Master's Research - Polytechnique Montréal

Can (Sam) Chen

PhD - McGill University

Principal supervisor :

PhD - Université de Montréal

Principal supervisor :

Glen Berseth

Amine El-hattami

PhD - Polytechnique Montréal

Chris Emezue

Master's Research - Université de Montréal

Co-supervisor :

Master's Research - Polytechnique Montréal

Roger Girgis

PhD - Polytechnique Montréal

Anthony Gosselin

Master's Research - Polytechnique Montréal

PhD - Université de Montréal

Co-supervisor :

PhD - Université de Montréal

PhD - Université de Montréal

Master's Research - Université de Montréal

Olga Luo

PhD - Université de Montréal

Z Luo

PhD - Polytechnique Montréal

Aristides Milios

PhD - Université de Montréal

PhD - Université de Montréal

Alexandre Piché

PhD - Université de Montréal

Jonathan Pilault

PhD - Polytechnique Montréal

Mats Leon Richter

Postdoctorate - Université de Montréal

Juan Rodriguez

PhD - École de technologie suprérieure

Luke Rowe

PhD - Université de Montréal

Principal supervisor :

Julien Roy

PhD - Polytechnique Montréal

Co-supervisor :

PhD - Polytechnique Montréal

Principal supervisor :

PhD - McGill University

Principal supervisor :

PhD - Université de Montréal

Martin Weiss

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

Seq-VCR: Preventing Collapse in Intermediate Transformer Representations for Enhanced Reasoning

Md Rifat Arefin

Gopeshh Raaj Subbaraj

Nicolas Gontier

Yann LeCun

Irina Rish

Ravid Shwartz-Ziv

2024-11-04

ArXiv (preprint)

Beyond FVD: Enhanced Evaluation Metrics for Video Generation Quality

Ge Ya Luo

Gian Favero

Zhi Hao Luo

Alexia Jolicoeur-Martineau

The Fr\'echet Video Distance (FVD) is a widely adopted metric for evaluating video generation distribution quality. However, its effectivene… (see more)ss relies on critical assumptions. Our analysis reveals three significant limitations: (1) the non-Gaussianity of the Inflated 3D Convnet (I3D) feature space; (2) the insensitivity of I3D features to temporal distortions; (3) the impractical sample sizes required for reliable estimation. These findings undermine FVD's reliability and show that FVD falls short as a standalone metric for video generation evaluation. After extensive analysis of a wide range of metrics and backbone architectures, we propose JEDi, the JEPA Embedding Distance, based on features derived from a Joint Embedding Predictive Architecture, measured using Maximum Mean Discrepancy with polynomial kernel. Our experiments on multiple open-source datasets show clear evidence that it is a superior alternative to the widely used FVD metric, requiring only 16% of the samples to reach its steady value, while increasing alignment with human evaluation by 34%, on average.

2024-10-07

ArXiv (preprint)

Robust Guided Diffusion for Offline Black-Box Optimization

Can Chen

Christopher Beckham

Zixuan Liu

Xue (Steve) Liu

Offline black-box optimization aims to maximize a black-box function using an offline dataset of designs and their measured properties. Two … (see more)main approaches have emerged: the forward approach, which learns a mapping from input to its value, thereby acting as a proxy to guide optimization, and the inverse approach, which learns a mapping from value to input for conditional generation. (a) Although proxy-free~(classifier-free) diffusion shows promise in robustly modeling the inverse mapping, it lacks explicit guidance from proxies, essential for generating high-performance samples beyond the training distribution. Therefore, we propose \textit{proxy-enhanced sampling} which utilizes the explicit guidance from a trained proxy to bolster proxy-free diffusion with enhanced sampling control. (b) Yet, the trained proxy is susceptible to out-of-distribution issues. To address this, we devise the module \textit{diffusion-based proxy refinement}, which seamlessly integrates insights from proxy-free diffusion back into the proxy for refinement. To sum up, we propose \textit{\textbf{R}obust \textbf{G}uided \textbf{D}iffusion for Offline Black-box Optimization}~(\textbf{RGD}), combining the advantages of proxy~(explicit guidance) and proxy-free diffusion~(robustness) for effective conditional generation. RGD achieves state-of-the-art results on various design-bench tasks, underscoring its efficacy. Our code is at https://anonymous.4open.science/r/RGD-27A5/README.md.

2024-10-01

ArXiv (preprint)

Learning Action and Reasoning-Centric Image Editing from Videos and Simulation

Benno Krojer

Dheeraj Vattikonda

Luis Lara

Varun Jampani

Eva Portelance

Siva Reddy

2024-09-26

NeurIPS.cc/2024/Datasets_and_Benchmarks_Track (spotlight)

RepLiQA: A Question-Answering Dataset for Benchmarking LLMs on Unseen Reference Content

Joao Monteiro

Pierre-Andre Noel

Étienne Marcotte

Sai Rajeswar

Valentina Zantedeschi

David Vazquez

Nicolas Chapados

Perouz Taslakian

Large Language Models (LLMs) are trained on vast amounts of data, most of which is automatically scraped from the internet. This data includ… (see more)es encyclopedic documents that harbor a vast amount of general knowledge (*e.g.*, Wikipedia) but also potentially overlap with benchmark datasets used for evaluating LLMs. Consequently, evaluating models on test splits that might have leaked into the training set is prone to misleading conclusions. To foster sound evaluation of language models, we introduce a new test dataset named RepLiQA, suited for question-answering and topic retrieval tasks. RepLiQA is a collection of five splits of test sets, four of which have not been released to the internet or exposed to LLM APIs prior to this publication. Each sample in RepLiQA comprises (1) a reference document crafted by a human annotator and depicting an imaginary scenario (*e.g.*, a news article) absent from the internet; (2) a question about the document’s topic; (3) a ground-truth answer derived directly from the information in the document; and (4) the paragraph extracted from the reference document containing the answer. As such, accurate answers can only be generated if a model can find relevant content within the provided document. We run a large-scale benchmark comprising several state-of-the-art LLMs to uncover differences in performance across models of various types and sizes in a context-conditional language modeling setting. Released splits of RepLiQA can be found here: https://huggingface.co/datasets/ServiceNow/repliqa.

2024-09-26

NeurIPS.cc/2024/Datasets_and_Benchmarks_Track (poster)

CtRL-Sim: Reactive and Controllable Driving Agents with Offline Reinforcement Learning

Luke Rowe

Roger Girgis

Anthony Gosselin

Bruno Carrez

Florian Golemo

Felix Heide

Liam Paull

Evaluating autonomous vehicle stacks (AVs) in simulation typically involves replaying driving logs from real-world recorded traffic. However… (see more), agents replayed from offline data do not react to the actions of the AV, and their behaviour cannot be easily controlled to simulate counterfactual scenarios. Existing approaches have attempted to address these shortcomings by proposing methods that rely on heuristics or learned generative models of real-world data but these approaches either lack realism or necessitate costly iterative sampling procedures to control the generated behaviours. In this work, we take an alternative approach and propose CtRL-Sim, a method that leverages return-conditioned offline reinforcement learning within a physics-enhanced Nocturne simulator to efficiently generate reactive and controllable traffic agents. Specifically, we process real-world driving data through the Nocturne simulator to generate a diverse offline reinforcement learning dataset, annotated with various reward terms. With this dataset, we train a return-conditioned multi-agent behaviour model that allows for fine-grained manipulation of agent behaviours by modifying the desired returns for the various reward components. This capability enables the generation of a wide range of driving behaviours beyond the scope of the initial dataset, including those representing adversarial behaviours. We demonstrate that CtRL-Sim can efficiently generate diverse and realistic safety-critical scenarios while providing fine-grained control over agent behaviours. Further, we show that fine-tuning our model on simulated safety-critical scenarios generated by our model enhances this controllability.

2024-09-05

robot-learning.org/CoRL/2024/Conference (accepted)

Redesigning Information Markets in the Era of Language Models

Martin Weiss

Nasim Rahaman

Manuel Wüthrich

Yoshua Bengio

Li Erran Li

Bernhard Schölkopf

2024-07-10

colmweb.org/COLM/2024/Conference (accepted)

InsightBench: Evaluating Business Analytics Agents Through Multi-Step Insight Generation

Gaurav Sahu

Abhay Puri

Juan A. Rodriguez

Alexandre Drouin

Perouz Taslakian

Valentina Zantedeschi

Alexandre Lacoste

David Vazquez

Nicolas Chapados

Sai Rajeswar

Issam Hadj Laradji

2024-07-08

ArXiv (preprint)

Learning Action and Reasoning-Centric Image Editing from Videos and Simulations

Benno Krojer

Dheeraj Vattikonda

Luis Lara

Varun Jampani

Eva Portelance

Siva Reddy

An image editing model should be able to perform diverse edits, ranging from object replacement, changing attributes or style, to performing… (see more) actions or movement, which require many forms of reasoning. Current general instruction-guided editing models have significant shortcomings with action and reasoning-centric edits. Object, attribute or stylistic changes can be learned from visually static datasets. On the other hand, high-quality data for action and reasoning-centric edits is scarce and has to come from entirely different sources that cover e.g. physical dynamics, temporality and spatial reasoning. To this end, we meticulously curate the AURORA Dataset (Action-Reasoning-Object-Attribute), a collection of high-quality training data, human-annotated and curated from videos and simulation engines. We focus on a key aspect of quality training data: triplets (source image, prompt, target image) contain a single meaningful visual change described by the prompt, i.e., truly minimal changes between source and target images. To demonstrate the value of our dataset, we evaluate an AURORA-finetuned model on a new expert-curated benchmark (AURORA-Bench) covering 8 diverse editing tasks. Our model significantly outperforms previous editing models as judged by human raters. For automatic evaluations, we find important flaws in previous metrics and caution their use for semantically hard editing tasks. Instead, we propose a new automatic metric that focuses on discriminative understanding. We hope that our efforts : (1) curating a quality training dataset and an evaluation benchmark, (2) developing critical evaluations, and (3) releasing a state-of-the-art model, will fuel further progress on general image editing.

2024-07-03

ArXiv (preprint)

RepLiQA: A Question-Answering Dataset for Benchmarking LLMs on Unseen Reference Content

Joao Monteiro

Pierre-Andre Noel

Étienne Marcotte

Sai Rajeswar

Valentina Zantedeschi

David Vazquez

Nicolas Chapados

Perouz Taslakian

Large Language Models (LLMs) are trained on vast amounts of data, most of which is automatically scraped from the internet. This data includ… (see more)es encyclopedic documents that harbor a vast amount of general knowledge (e.g., Wikipedia) but also potentially overlap with benchmark datasets used for evaluating LLMs. Consequently, evaluating models on test splits that might have leaked into the training set is prone to misleading conclusions. To foster sound evaluation of language models, we introduce a new test dataset named RepLiQA, suited for question-answering and topic retrieval tasks. RepLiQA is a collection of five splits of test sets, four of which have not been released to the internet or exposed to LLM APIs prior to this publication. Each sample in RepLiQA comprises (1) a reference document crafted by a human annotator and depicting an imaginary scenario (e.g., a news article) absent from the internet; (2) a question about the document's topic; (3) a ground-truth answer derived directly from the information in the document; and (4) the paragraph extracted from the reference document containing the answer. As such, accurate answers can only be generated if a model can find relevant content within the provided document. We run a large-scale benchmark comprising several state-of-the-art LLMs to uncover differences in performance across models of various types and sizes in a context-conditional language modeling setting. Released splits of RepLiQA can be found here: https://huggingface.co/datasets/ServiceNow/repliqa.

2024-06-17

ArXiv (preprint)

Exploring validation metrics for offline model-based optimisation with diffusion models

Christopher Beckham

Alexandre Piché

David Vazquez

2024-06-13

TMLR (accepted)

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

Ge Ya Luo

Zhi Hao Luo

Anthony Gosselin

Alexia Jolicoeur-Martineau

With recent advances in video prediction, controllable video generation has been attracting more attention. Generating high fidelity videos … (see more)according to simple and flexible conditioning is of particular interest. To this end, we propose a controllable video generation model using pixel level renderings of 2D or 3D bounding boxes as conditioning. In addition, we also create a bounding box predictor that, given the initial and ending frames' bounding boxes, can predict up to 15 bounding boxes per frame for all the frames in a 25-frame clip. We perform experiments across 3 well-known AV video datasets: KITTI, Virtual-KITTI 2 and BDD100k.

2024-06-09

ArXiv (preprint)