Chris Pal

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

Mai Ababneh

Collaborateur·rice de recherche - Formerly McGill (but ending)

Paul Barde

Collaborateur·rice de recherche - McGill

Superviseur⋅e principal⋅e :

Maîtrise recherche - UdeM

Chris Beckham

Doctorat - Polytechnique

Can (Sam) Chen

Collaborateur·rice alumni - McGill

Superviseur⋅e principal⋅e :

Xue (Steve) Liu

Léa Demeule

Doctorat - UdeM

Superviseur⋅e principal⋅e :

Doctorat - Polytechnique

Chris Emezue

Maîtrise recherche - UdeM

Co-superviseur⋅e :

Collaborateur·rice alumni - Polytechnique

Roger Girgis

Doctorat - Polytechnique

Florian Golemo

Postdoctorat - McGill

Maîtrise recherche - Polytechnique

Doctorat - UdeM

Co-superviseur⋅e :

Yousef Kotp

Maîtrise recherche - Concordia

Co-superviseur⋅e :

Maîtrise recherche - UdeM

Olga Luo

Doctorat - UdeM

Doctorat - UdeM

Joel Moniz

Doctorat - Polytechnique

Jonathan Pilault

Doctorat - Polytechnique

Juan Rodriguez

Doctorat - École de technologie suprérieure

Luke Rowe

Doctorat - UdeM

Superviseur⋅e principal⋅e :

Spécification directe du comportement par apprentissage par renforcement sous contrainte

Postdoctorat - HEC

Superviseur⋅e principal⋅e :

Doctorat - Polytechnique

Superviseur⋅e principal⋅e :

Doctorat - McGill

Superviseur⋅e principal⋅e :

Postdoctorat - Polytechnique

Co-superviseur⋅e :

Doctorat - UdeM

Billets de blogue

Direct Behavior Specification via Constrained Reinforcement Learning

31 août 2022

par

Julien Roy

Roger Girgis

Joshua Romoff

Pierre-Luc Bacon

Chris Pal

Lire l'article

Publications

BigDocs: An Open Dataset for Training Multimodal Models on Document and Code Tasks

Juan A. Rodriguez

Xiangru Jian

Siba Smarak Panigrahi

Tianyu Zhang

Aarash Feizi

Akshay Kalkunte Suresh

Amirhossein Abaskohi

Pierre-Andre Noel

Sanket Biswas … (voir 23 de plus)

Sara Shanian

Ying Zhang

Noah Bolger

Kurt MacDonald

Simon Fauvel

Sathwik Tejaswi Madhusudhan

Srinivas Sunkara

Joao Monteiro

Krishnamurthy Dj Dvijotham

Torsten Scholak

Nicolas Chapados

Sepideh Kharaghani

Sean Hughes

M. Özsu

Issam Hadj Laradji

Sai Rajeswar

2025-01-22

ICLR.cc/2025/Conference (poster)

CarbonSense: A Multimodal Dataset and Baseline for Carbon Flux Modelling

Matthew Fortier

Mats Leon Richter

Oliver Sonnentag

Terrestrial carbon fluxes provide vital information about our biosphere's health and its capacity to absorb anthropogenic CO…

2025-01-22

ICLR.cc/2025/Conference (poster)

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

Juan A. Rodriguez

Amirhossein Abaskohi

Mohammad Chegini

Alexandre Drouin

Perouz Taslakian

Valentina Zantedeschi

Alexandre Lacoste

David Vázquez

Nicolas Chapados

Sai Rajeswar

Issam Hadj Laradji

2025-01-22

ICLR.cc/2025/Conference (poster)

ParetoFlow: Guided Flows in Multi-Objective Optimization

Ye Yuan

Can Chen

Xue (Steve) Liu

In offline multi-objective optimization (MOO), we leverage an offline dataset of designs and their associated labels to simultaneously minim… (voir plus)ize multiple objectives. This setting more closely mirrors complex real-world problems compared to single-objective optimization. Recent works mainly employ evolutionary algorithms and Bayesian optimization, with limited attention given to the generative modeling capabilities inherent in such data. In this study, we explore generative modeling in offline MOO through flow matching, noted for its effectiveness and efficiency. We introduce ParetoFlow, specifically designed to guide flow sampling to approximate the Pareto front. Traditional predictor (classifier) guidance is inadequate for this purpose because it models only a single objective. In response, we propose a multi-objective predictor guidance module that assigns each sample a weight vector, representing a weighted distribution across multiple objective predictions. A local filtering scheme is introduced to address non-convex Pareto fronts. These weights uniformly cover the entire objective space, effectively directing sample generation towards the Pareto front. Since distributions with similar weights tend to generate similar samples, we introduce a neighboring evolution module to foster knowledge sharing among neighboring distributions. This module generates offspring from these distributions, and selects the most promising one for the next iteration. Our method achieves state-of-the-art performance across various tasks.

2025-01-22

ICLR.cc/2025/Conference (poster)

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

Md Rifat Arefin

Nicolas Gontier

Ravid Shwartz-Ziv

2025-01-22

ICLR.cc/2025/Conference (poster)

Abstract: Würstchen - An Efficient Architecture for Large-scale Text-to-image Diffusion Models

Pablo Pernias

Dominic Rampas

Mats L. Richter

Marc Aubreville

2025-01-01

Bildverarbeitung für die Medizin (publié)

Alexia Jolicoeur-Martineau

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

Ge Ya Luo

Zhi Hao Luo

Anthony Gosselin

2025-01-01

Trans. Mach. Learn. Res. (publié)

LLMs can learn self-restraint through iterative self-reflection

2025-01-01

Trans. Mach. Learn. Res. (publié)

Scenario Dreamer: Vectorized Latent Diffusion for Generating Driving Simulation Environments

Felix Heide

2025-01-01

CVPR (publié)

arxiv.org

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 … (voir plus)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.

2024-12-20

TMLR (accepté)

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

Shubham Agarwal

Issam Hadj Laradji

Krishnamurthy Dj Dvijotham

Jason Stanley

Laurent Charlin

2024-12-15

ArXiv (prépublication)

arxiv.org

LLMs for Literature Review: Are we there yet?

Shubham Agarwal

Issam Hadj Laradji

Krishnamurthy Dj Dvijotham

Jason Stanley

Laurent Charlin

Literature reviews are an essential component of scientific research, but they remain time-intensive and challenging to write, especially du… (voir plus)e to the recent influx of research papers. This paper explores the zero-shot abilities of recent Large Language Models (LLMs) in assisting with the writing of literature reviews based on an abstract. We decompose the task into two components: 1. Retrieving related works given a query abstract, and 2. Writing a literature review based on the retrieved results. We analyze how effective LLMs are for both components. For retrieval, we introduce a novel two-step search strategy that first uses an LLM to extract meaningful keywords from the abstract of a paper and then retrieves potentially relevant papers by querying an external knowledge base. Additionally, we study a prompting-based re-ranking mechanism with attribution and show that re-ranking doubles the normalized recall compared to naive search methods, while providing insights into the LLM's decision-making process. In the generation phase, we propose a two-step approach that first outlines a plan for the review and then executes steps in the plan to generate the actual review. To evaluate different LLM-based literature review methods, we create test sets from arXiv papers using a protocol designed for rolling use with newly released LLMs to avoid test set contamination in zero-shot evaluations. We release this evaluation protocol to promote additional research and development in this regard. Our empirical results suggest that LLMs show promising potential for writing literature reviews when the task is decomposed into smaller components of retrieval and planning. Further, we demonstrate that our planning-based approach achieves higher-quality reviews by minimizing hallucinated references in the generated review by 18-26% compared to existing simpler LLM-based generation methods.

2024-12-15

ArXiv (prépublication)