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

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Google Scholar

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

Shubham Agarwal

Postdoctorat - HEC

Superviseur⋅e principal⋅e :

Laurent Charlin

shubham.agarwal@mila.quebec

Site web

Github

Google Scholar

Paul Barde

Doctorat - McGill

Superviseur⋅e principal⋅e :

Doctorat - Polytechnique

christopher.beckham@mila.quebec

Georges Belanger Albarran

Maîtrise recherche - UdeM

georges.belangeralbarran@mila.quebec

Simon Chamorro

Maîtrise recherche - Polytechnique

chamorrs@mila.quebec

Google Scholar

Can (Sam) Chen

Doctorat - McGill

Superviseur⋅e principal⋅e :

Doctorat - Polytechnique

elhattaa@mila.quebec

Chris Emezue

Maîtrise recherche - UdeM

Co-superviseur⋅e :

Derek Nowrouzezahrai

chris.emezue@mila.quebec

Maîtrise recherche - Polytechnique

matthew.fortier@mila.quebec

Github

Google Scholar

Roger Girgis

Doctorat - Polytechnique

girgisro@mila.quebec

Anthony Gosselin

Maîtrise recherche - Polytechnique

anthony.gosselin@mila.quebec

Doctorat - UdeM

Co-superviseur⋅e :

Sarath Chandar Anbil Parthipan

Doctorat - UdeM

Baudchon Hugo Baudchon

Collaborateur·rice de recherche - Université de Montréal

Superviseur⋅e principal⋅e :

Étienne Laliberté

hugo.baudchon@mila.quebec

Doctorat - UdeM

Collaborateur·rice de recherche

michelle.lin@mila.quebec

Site web

Github

Google Scholar

Z Luo

Doctorat - Polytechnique

Doctorat - UdeM

Doctorat - UdeM

Doctorat - UdeM

Doctorat - Polytechnique

Postdoctorat - UdeM

mats-leon.richter@mila.quebec

Github

Juan Rodriguez

Doctorat - École de technologie suprérieure

juan.rodriguez@mila.quebec

Site web

Github

Google Scholar

Luke Rowe

Doctorat - UdeM

Superviseur⋅e principal⋅e :

Liam Paull

luke.rowe@mila.quebec

Github

Google Scholar

Julien Roy

Doctorat - Polytechnique

Co-superviseur⋅e :

Doctorat

Superviseur⋅e principal⋅e :

Amal Zouaq

aditya.sharma@mila.quebec

Doctorat - McGill

Superviseur⋅e principal⋅e :

Derek Nowrouzezahrai

mattie.tesfaldet@mila.quebec

Doctorat - UdeM

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

Roger Girgis

Joshua Romoff

Pierre-Luc Bacon

Chris Pal

Lire l'article

Publications

Controllable Image Generation via Collage Representations

Arantxa Casanova

Marlene Careil

Adriana Romero Soriano

Chris Pal

Jakob Verbeek

Michal Drozdzal

2023-04-26

ArXiv (prépublication)

doi.org

openreview.net

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

Christopher Beckham

Chris Pal

In this work we theoretically show that conservative objective models (COMs) for offline model-based optimisation (MBO) are a special kind o… (voir plus)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 (prépublication)

doi.org

arxiv.org

Visual Question Answering From Another Perspective: CLEVR Mental Rotation Tests

Christopher Beckham

Martin Weiss

Florian Golemo

Sina Honari

Derek Nowrouzezahrai

Chris Pal

2023-04-01

Pattern Recognition (publié)

doi.org

openreview.net

Proactive Contact Tracing

Prateek Gupta

Tegan Maharaj

Martin Weiss

Nasim Rahaman

Hannah Alsdurf

Nanor Minoyan

Soren Harnois-Leblanc

Joanna Merckx

andrew williams

Victor Schmidt

Pierre-Luc St-Charles

Akshay Patel

Yang Zhang

David Buckeridge

Chris Pal

Bernhard Schölkopf

Yoshua Bengio

2023-03-13

PLOS Digital Health (publié)

doi.org

Score-based Diffusion Models in Function Space

Jae Hyun Lim

Nikola B. Kovachki

R. Baptista

Christopher Beckham

Kamyar Azizzadenesheli

Jean Kossaifi

Vikram Voleti

Jiaming Song

Karsten Kreis

Jan Kautz

Chris Pal

Arash Vahdat

Animashree Anandkumar

2023-02-14

ArXiv (prépublication)

doi.org

arxiv.org

Language Decision Transformers with Exponential Tilt for Interactive Text Environments

Nicolas Gontier

Pau Rodriguez

Issam Hadj Laradji

David Vazquez

Chris Pal

2023-02-10

ArXiv (preprint)

openreview.net

ArK: Augmented Reality with Knowledge Emergent Infrastructure

Qiuyuan Huang

J. Park

Abhinav Gupta

Pan Lu

Paul N. Bennett

Ran Gong

Subhojit Som

Baolin Peng

Owais Khan Mohammed

Chris Pal

Yejin Choi

Jianfeng Gao

Despite the growing adoption of mixed reality and interactive AI, it remains challenging to generate high-quality 2D/3D scenes in unseen env… (voir plus)ironments. Typically, an AI agent requires collecting extensive training data for every new task, which can be costly or 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 physical or virtual worlds. Central to our approach is the interactive emerging mechanism, dubbed Augmented Reality with Knowledge Emergent Infrastructure (ArK) , which leverages knowledge-memory to generate scenes in unseen physical worlds and virtual reality environments. The knowledge interactive emergent ability (Figure 1) is demonstrated through 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 ArK’s effectiveness in scene generation and editing tasks and show that our ArK approach, combined with large foundation models, significantly improves the quality of generated 2D/3D scenes, highlighting its potential in applications such as metaverse and gaming simulation.

2023-01-01

(publié)

www.semanticscholar.org

Block-State Transformers

Mahan Fathi

Jonathan Pilault

Pierre-Luc Bacon

Chris Pal

Orhan Firat

Ross Goroshin

2023-01-01

NeurIPS (publié)

arxiv.org

Towards Learning to Imitate from a Single Video Demonstration

Glen Berseth

Florian Golemo

Chris Pal

Agents that can learn to imitate given video observation -- \emph{without direct access to state or action information} are more applicable … (voir plus)to learning in the natural world. However, formulating a reinforcement learning (RL) agent that facilitates this goal remains a significant challenge. We approach this challenge using contrastive training to learn a reward function comparing an agent's behaviour with a single demonstration. We use a Siamese recurrent neural network architecture to learn rewards in space and time between motion clips while training an RL policy to minimize this distance. Through experimentation, we also find that the inclusion of multi-task data and additional image encoding losses improve the temporal consistency of the learned rewards and, as a result, significantly improves policy learning. We demonstrate our approach on simulated humanoid, dog, and raptor agents in 2D and a quadruped and a humanoid in 3D. We show that our method outperforms current state-of-the-art techniques in these environments and can learn to imitate from a single video demonstration.

arxiv.org

Workflow Discovery from Dialogues in the Low Data Regime

Amine El hattami

Stefania Raimondo

Issam Hadj Laradji

David Vazquez

Pau Rodriguez

Chris Pal

Text-based dialogues are now widely used to solve real-world problems. In cases where solution strategies are already known, they can someti… (voir plus)mes be codified into workflows and used to guide humans or artificial agents through the task of helping clients. We introduce a new problem formulation that we call Workflow Discovery (WD) in which we are interested in the situation where a formal workflow may not yet exist. Still, we wish to discover the set of actions that have been taken to resolve a particular problem. We also examine a sequence-to-sequence (Seq2Seq) approach for this novel task. We present experiments where we extract workflows from dialogues in the Action-Based Conversations Dataset (ABCD). Since the ABCD dialogues follow known workflows to guide agents, we can evaluate our ability to extract such workflows using ground truth sequences of actions. We propose and evaluate an approach that conditions models on the set of possible actions, and we show that using this strategy, we can improve WD performance. Our conditioning approach also improves zero-shot and few-shot WD performance when transferring learned models to unseen domains within and across datasets. Further, on ABCD a modified variant of our Seq2Seq method achieves state-of-the-art performance on related but different problems of Action State Tracking (AST) and Cascading Dialogue Success (CDS) across many evaluation metrics.

2023-01-01

Trans. Mach. Learn. Res. (publié)

doi.org

openreview.net

Implicit Offline Reinforcement Learning via Supervised Learning

Alexandre Piché

Rafael Pardinas

David Vazquez

Igor Mordatch

Chris Pal

Offline Reinforcement Learning (RL) via Supervised Learning is a simple and effective way to learn robotic skills from a dataset of varied b… (voir plus)ehaviors. It is as simple as supervised learning and Behavior Cloning (BC) but takes advantage of the return information. On BC tasks, implicit models have been shown to match or outperform explicit ones. Despite the benefits of using implicit models to learn robotic skills via BC, Offline RL via Supervised Learning algorithms have been limited to explicit models. We show how implicit models leverage return information and match or outperform explicit algorithms to acquire robotic skills from fixed datasets. Furthermore, we show how closely related our implicit methods are to other popular RL via Supervised Learning algorithms.

2022-12-09

NeurIPS.cc/2022/Workshop/DeepRL (inconnu)

doi.org

openreview.net

SMPL-IK: Learned Morphology-Aware Inverse Kinematics for AI Driven Artistic Workflows

Vikram Voleti

Boris Oreshkin

Florent Bocquelet

Félix Harvey

Louis-Simon Ménard

Chris Pal

2022-11-22

SIGGRAPH Asia 2022 Technical Communications (publié)

doi.org

arxiv.org

Le traitement du langage naturel à l'ère de l'IA générative

Boussole des politiques en IA

Vie étudiante et ressources

Chris Pal

Biographie

Étudiants actuels

Billets de blogue

Publications

Le traitement du langage naturel à l'ère de l'IA générative

Boussole des politiques en IA

Vie étudiante et ressources

Mots-clés populaires:

Chris Pal

Biographie

Étudiants actuels

Billets de blogue

Publications