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

Can Chen

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

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.

2024-03-29

ArXiv (preprint)

Language Models Can Reduce Asymmetry in Information Markets

Nasim Rahaman

Martin Weiss

Manuel Wüthrich

Yoshua Bengio

Erran L. Li

Bernhard Schölkopf

2024-03-21

ArXiv (preprint)

Multi-Resolution Continuous Normalizing Flows

Vikram Voleti

Chris Finlay

Adam M. Oberman

2024-03-21

Annals of Mathematics and Artificial Intelligence (published)

IntentGPT: Few-shot Intent Discovery with Large Language Models

Juan A. Rodriguez

Nicholas Botzer

David Vazquez

Marco Pedersoli

Issam Hadj Laradji

2024-03-11

ICLR.cc/2024/Workshop/LLMAgents (poster)

Self-evaluation and self-prompting to improve the reliability of LLMs

Alexandre Piché

Aristides Milios

Dzmitry Bahdanau

In order to safely deploy Large Language Models (LLMs), they must be capable of dynamically adapting their behavior based on their level of … (see more)knowledge and uncertainty associated with specific topics. This adaptive behavior, which we refer to as self-restraint, is non-trivial to teach since it depends on the internal knowledge of an LLM. By default, LLMs are trained to maximize the next token likelihood which does not teach the model to modulate its answer based on its level of uncertainty. In order to learn self-restraint, we devise a simple objective that can encourage the model to produce generation that the model is confident in. To optimize this objective, we introduce ReSearch, an iterative search algorithm based on self-evaluation and self-prompting. Our method results in fewer hallucinations overall, both for known and unknown topics, as the model learns to selectively restrain itself. In addition, our method elegantly incorporates the ability to decline, when the model assesses that it cannot provide a response without a high proportion of hallucination.

2024-03-04

ICLR.cc/2024/Workshop/SeT_LLM (published)

Self-evaluation and self-prompting to improve the reliability of LLMs

Alexandre Piché

Aristides Milios

Dzmitry Bahdanau

2024-03-04

ICLR.cc/2024/Workshop/SeT_LLM (published)

Reinforcement Learning for Blind Stair Climbing with Legged and Wheeled-Legged Robots

Simon Chamorro

Victor Klemm

Miguel I. Valls

Roland Siegwart

2024-02-09

ArXiv (preprint)

LitLLM: A Toolkit for Scientific Literature Review

Shubham Agarwal

Issam Hadj Laradji

Laurent Charlin

Conducting literature reviews for scientific papers is essential for understanding research, its limitations, and building on existing work.… (see more) It is a tedious task which makes an automatic literature review generator appealing. Unfortunately, many existing works that generate such reviews using Large Language Models (LLMs) have significant limitations. They tend to hallucinate-generate non-actual information-and ignore the latest research they have not been trained on. To address these limitations, we propose a toolkit that operates on Retrieval Augmented Generation (RAG) principles, specialized prompting and instructing techniques with the help of LLMs. Our system first initiates a web search to retrieve relevant papers by summarizing user-provided abstracts into keywords using an off-the-shelf LLM. Authors can enhance the search by supplementing it with relevant papers or keywords, contributing to a tailored retrieval process. Second, the system re-ranks the retrieved papers based on the user-provided abstract. Finally, the related work section is generated based on the re-ranked results and the abstract. There is a substantial reduction in time and effort for literature review compared to traditional methods, establishing our toolkit as an efficient alternative. Our open-source toolkit is accessible at https://github.com/shubhamagarwal92/LitLLM and Huggingface space (https://huggingface.co/spaces/shubhamagarwal92/LitLLM) with the video demo at https://youtu.be/E2ggOZBAFw0.

2024-02-02

ArXiv (preprint)

Würstchen: An Efficient Architecture for Large-Scale Text-to-Image Diffusion Models

Pablo Pernias

Dominic Rampas

Mats Leon Richter

Marc Aubreville

2024-01-16

ICLR.cc/2024/Conference (oral)

StarVector: Generating Scalable Vector Graphics Code from Images

Juan A. Rodriguez

Shubham Agarwal

Issam Hadj Laradji

Pau Rodriguez

David Vazquez

Marco Pedersoli

Scalable Vector Graphics (SVGs) have become integral in modern image rendering applications due to their infinite scalability in resolution,… (see more) versatile usability, and editing capabilities. SVGs are particularly popular in the fields of web development and graphic design. Existing approaches for SVG modeling using deep learning often struggle with generating complex SVGs and are restricted to simpler ones that require extensive processing and simplification. This paper introduces StarVector, a multimodal SVG generation model that effectively integrates Code Generation Large Language Models (CodeLLMs) and vision models. Our approach utilizes a CLIP image encoder to extract visual representations from pixel-based images, which are then transformed into visual tokens via an adapter module. These visual tokens are pre-pended to the SVG token embeddings, and the sequence is modeled by the StarCoder model using next-token prediction, effectively learning to align the visual and code tokens. This enables StarVector to generate unrestricted SVGs that accurately represent pixel images. To evaluate StarVector's performance, we present SVG-Bench, a comprehensive benchmark for evaluating SVG methods across multiple datasets and relevant metrics. Within this benchmark, we introduce novel datasets including SVG-Stack, a large-scale dataset of real-world SVG examples, and use it to pre-train StarVector as a large foundation model for SVGs. Our results demonstrate significant enhancements in visual quality and complexity handling over current methods, marking a notable advancement in SVG generation technology. Code and models: https://github.com/joanrod/star-vector

2023-12-17

ArXiv (preprint)

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)

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.

2023-09-21

NeurIPS.cc/2023/Conference (poster)