Sarath Chandar

Biographie

Sarath Chandar est professeur associé au départment de génie informatique et génie logiciel de Polytechnique Montréal, où il dirige le laboratoire de recherche Chandar. Il est également membre académique principal à Mila – Institut québécois d’intelligence artificielle, et titulaire d'une chaire en IA Canada-CIFAR et d'une Chaire de recherche du Canada en apprentissage machine permanent.

Ses recherches portent sur l'apprentissage tout au long de la vie, l'apprentissage profond, l'optimisation, l'apprentissage par renforcement et le traitement du langage naturel. Pour promouvoir la recherche sur l'apprentissage tout au long de la vie, Sarath Chandar a créé la Conférence sur les agents d'apprentissage tout au long de la vie (CoLLAs) en 2022 et a présidé le programme en 2022 et en 2023. Il est titulaire d'un doctorat de l'Université de Montréal et d'une maîtrise en recherche de l'Indian Institute of Technology Madras.

Étudiants actuels

Ista Abbes

Maîtrise recherche - UdeM

Alex Aselstyne

Maîtrise recherche - Polytechnique

Davide Baldelli

Doctorat - Polytechnique

Co-superviseur⋅e :

Milan Bhan

Collaborateur·rice de recherche

Diego Cerda Mardini

Maîtrise recherche - McGill

Antoine Clavaud

Maîtrise recherche - Polytechnique

Naga Karthik Enamundram

Doctorat - Polytechnique

Superviseur⋅e principal⋅e :

Prashant Govindarajan

Doctorat - Polytechnique

Simon Guiroy

Doctorat - UdeM

Superviseur⋅e principal⋅e :

Doctorat - UdeM

David Heurtel--Depeiges

Doctorat - Polytechnique

Jerry Huang

Doctorat - UdeM

Saurav Jha

Postdoctorat - Polytechnique

Amir Ardalan Kalantari Dehaghi

Collaborateur·rice alumni

Lola Le Breton

Doctorat - Polytechnique

Aidan Li

Maîtrise recherche - UdeM

Co-superviseur⋅e :

Postdoctorat - UdeM

Doctorat - Polytechnique

Roshan Munirathinam Sankaran Balaji

Mohamed Amine Merzouk

Postdoctorat - Polytechnique

Superviseur⋅e principal⋅e :

Stagiaire de recherche - Polytechnique

Rayen Nacef

Stagiaire de recherche - Polytechnique

Hadi NekoeiQachkanloo

Doctorat - UdeM

Nilaksh Nilaksh

Doctorat - Polytechnique

Doctorat - UdeM

Collaborateur·rice de recherche - Polytechnique Montreal

Doctorat - UdeM

Postdoctorat

Visiteur de recherche indépendant

Shaipranesh Senthilkumar

Doctorat - Polytechnique

Nour Shaheen

Maîtrise recherche - Polytechnique

Arjun Vaithilingam Sudhakar

Achille Sowa Samo

Doctorat - Polytechnique

Megh Thakkar

Maîtrise recherche - UdeM

Doctorat - Polytechnique

Shawn Whitfield

Collaborateur·rice de recherche

Kowen Woo

Stagiaire de recherche - Polytechnique

Anabel XL

Postdoctorat - UdeM

Abdelrahman Zayed

Doctorat - Polytechnique

Xutong Zhao

Doctorat - Polytechnique

Artem Zholus

Doctorat - Polytechnique

NeoBERT: une nouvelle frontière pour les modèles de langage encodeurs open-source

Billets de blogue

A digital picture of Bert from Sesame street, wering black trench coat and sunglasses

3 mars 2025

par

Lola Le Breton

Quentin Fournier

Sarath Chandar

Lire l'article

1 octobre 2024

Comment expliquer l’IA et s’assurer que cette explication est vraie? Les modèles mesurables de fidélité vous indiquent comment y parvenir

par

Andrea Madsen

Siva Reddy

Sarath Chandar

Lire l'article

Publications

Conditionally Optimistic Exploration for Cooperative Deep Multi-Agent Reinforcement Learning

Xutong Zhao

Yangchen Pan

Chenjun Xiao

Efficient exploration is critical in cooperative deep Multi-Agent Reinforcement Learning (MARL). In this work, we propose an exploration met… (voir plus)hod that effectively encourages cooperative exploration based on the idea of sequential action-computation scheme. The high-level intuition is that to perform optimism-based exploration, agents would explore cooperative strategies if each agent's optimism estimate captures a structured dependency relationship with other agents. Assuming agents compute actions following a sequential order at \textit{each environment timestep}, we provide a perspective to view MARL as tree search iterations by considering agents as nodes at different depths of the search tree. Inspired by the theoretically justified tree search algorithm UCT (Upper Confidence bounds applied to Trees), we develop a method called Conditionally Optimistic Exploration (COE). COE augments each agent's state-action value estimate with an action-conditioned optimistic bonus derived from the visitation count of the global state and joint actions of preceding agents. COE is performed during training and disabled at deployment, making it compatible with any value decomposition method for centralized training with decentralized execution. Experiments across various cooperative MARL benchmarks show that COE outperforms current state-of-the-art exploration methods on hard-exploration tasks.

2023-05-08

auai.org/UAI/2023/Conference (publié)

Behavioral Cloning for Crystal Design

Prashant Govindarajan

Santiago Miret

Jarrid Rector-Brooks

Mariano Phielipp

Solid-state materials, which are made up of periodic 3D crystal structures, are particularly useful for a variety of real-world applications… (voir plus) such as batteries, fuel cells and catalytic materials. Designing solid-state materials, especially in a robust and automated fashion, remains an ongoing challenge. To further the automated design of crystalline materials, we propose a method to learn to design valid crystal structures given a crystal skeleton. By incorporating Euclidean equivariance into a policy network, we portray the problem of designing new crystals as a sequential prediction task suited for imitation learning. At each step, given an incomplete graph of a crystal skeleton, an agent assigns an element to a specific node. We adopt a behavioral cloning strategy to train the policy network on data consisting of curated trajectories generated from known crystals.

2023-03-17

ICLR.cc/2023/Workshop/ML4Materials (poster)

Dealing With Non-stationarity in Decentralized Cooperative Multi-Agent Deep Reinforcement Learning via Multi-Timescale Learning

Hadi Nekoei

Akilesh Badrinaaraayanan

Amit Sinha

Mohammad Amin Amini

Aditya Mahajan

2023-02-06

ArXiv (prépublication)

An Empirical Investigation of the Role of Pre-training in Lifelong Learning

Sanket Vaibhav Mehta

Darshan Patil

Emma Strubell

The lifelong learning paradigm in machine learning is an attractive alternative to the more prominent isolated learning scheme not only due … (voir plus)to its resemblance to biological learning, but also its potential to reduce energy waste by obviating excessive model re-training. A key challenge to this paradigm is the phenomenon of catastrophic forgetting. With the increasing popularity and success of pre-trained models in machine learning, we pose the question: What role does pre-training play in lifelong learning, specifically with respect to catastrophic forgetting? We investigate existing methods in the context of large, pre-trained models and evaluate their performance on a variety of text and image classification tasks, including a large-scale study using a novel dataset of 15 diverse NLP tasks. Across all settings, we observe that generic pre-training implicitly alleviates the effects of catastrophic forgetting when learning multiple tasks sequentially compared to randomly initialized models. We then further investigate why pre-training alleviates forgetting in this setting. We study this phenomenon by analyzing the loss landscape, finding that pre-trained weights appear to ease forgetting by leading to wider minima. Based on this insight, we propose jointly optimizing for current task loss and loss basin sharpness in order to explicitly encourage wider basins during sequential fine-tuning. We show that this optimization approach leads to performance comparable to the state-of-the-art in task-sequential continual learning across multiple settings, without retaining a memory that scales in size with the number of tasks.

Replay Buffer with Local Forgetting for Adapting to Local Environment Changes in Deep Model-Based Reinforcement Learning

Ali Rahimi-Kalahroudi

Ida Momennejad

Harm van Seijen

2023-01-01

CoLLAs (publié)

proceedings.mlr.press

Self-Influence Guided Data Reweighting for Language Model Pre-training

Megh Thakkar

Tolga Bolukbasi

Sriram Ganapathy

Shikhar Vashishth

Partha Talukdar

Language Models (LMs) pre-trained with selfsupervision on large text corpora have become the default starting point for developing models fo… (voir plus)r various NLP tasks. Once the pre-training corpus has been assembled, all data samples in the corpus are treated with equal importance during LM pre-training. However, due to varying levels of relevance and quality of data, equal importance to all the data samples may not be the optimal choice. While data reweighting has been explored in the context of task-specific supervised learning and LM fine-tuning, model-driven reweighting for pretraining data has not been explored. We fill this important gap and propose PRESENCE, a method for jointly reweighting samples by leveraging self-influence (SI) scores as an indicator of sample importance and pre-training. PRESENCE promotes novelty and stability for model pre-training. Through extensive analysis spanning multiple model sizes, datasets, and tasks, we present PRESENCE as an important first step in the research direction of sample reweighting for pre-training language models.

2023-01-01

EMNLP (publié)

Post-hoc Interpretability for Neural NLP: A Survey

Andreas Madsen

Siva Reddy

2022-12-23

ACM Computing Surveys (publié)

Replay Buffer With Local Forgetting for Adaptive Deep Model-Based Reinforcement Learning

Ali Rahimi-Kalahroudi

Ida Momennejad

Harm van Seijen

One of the key behavioral characteristics used in neuroscience to determine whether the subject of study—be it a rodent or a human—exhib… (voir plus)its model-based learning is effective adaptation to local changes in the environment. In reinforcement learning, however, recent work has shown that modern deep model-based reinforcement-learning (MBRL) methods adapt poorly to such changes. An explanation for this mismatch is that MBRL methods are typically designed with sample-efﬁciency on a single task in mind and the requirements for effective adaptation are substantially higher, both in terms of the learned world model and the planning routine. One particularly challenging requirement is that the learned world model has to be sufﬁciently accurate throughout relevant parts of the state-space. This is challenging for deep-learning-based world models due to catastrophic forgetting. And while a replay buffer can mitigate the effects of catastrophic forgetting, the traditional ﬁrst-in-ﬁrst-out replay buffer precludes effective adaptation due to maintaining stale data. In this work

2022-12-09

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

Improving Meta-Learning Generalization with Activation-Based Early-Stopping

Simon Guiroy

Chris Pal

Goncalo Mordido

2022-11-28

Proceedings of The 1st Conference on Lifelong Learning Agents (publié)

PatchBlender: A Motion Prior for Video Transformers

Gabriele Prato

Yale Song

(Rex) Devon Hjelm

Neel Joshi

2022-11-11

ArXiv (prépublication)

Segmentation of Multiple Sclerosis Lesions across Hospitals: Learn Continually or Train from Scratch?

Enamundram Naga Karthik

Anne Kerbrat

Pierre Labauge

Tobias Granberg

Jason F. Talbott

Daniel S Reich

Massimo Filippi

Rohit Bakshi

Virginie Callot

Julien Cohen-Adad

Segmentation of Multiple Sclerosis (MS) lesions is a challenging problem. Several deep-learning-based methods have been proposed in recent y… (voir plus)ears. However, most methods tend to be static, that is, a single model trained on a large, specialized dataset, which does not generalize well. Instead, the model should learn across datasets arriving sequentially from different hospitals by building upon the characteristics of lesions in a continual manner. In this regard, we explore experience replay, a well-known continual learning method, in the context of MS lesion segmentation across multi-contrast data from 8 different hospitals. Our experiments show that replay is able to achieve positive backward transfer and reduce catastrophic forgetting compared to sequential fine-tuning. Furthermore, replay outperforms the multi-domain training, thereby emerging as a promising solution for the segmentation of MS lesions. The code is available at this link: https://github.com/naga-karthik/continual-learning-ms

2022-10-27

ArXiv (prépublication)

Local Structure Matters Most: Perturbation Study in NLU

Louis Clouâtre

Prasanna Parthasarathi

Amal Zouaq

Recent research analyzing the sensitivity of natural language understanding models to word-order perturbations has shown that neural models … (voir plus)are surprisingly insensitive to the order of words.In this paper, we investigate this phenomenon by developing order-altering perturbations on the order of words, subwords, and characters to analyze their effect on neural models’ performance on language understanding tasks.We experiment with measuring the impact of perturbations to the local neighborhood of characters and global position of characters in the perturbed texts and observe that perturbation functions found in prior literature only affect the global ordering while the local ordering remains relatively unperturbed.We empirically show that neural models, invariant of their inductive biases, pretraining scheme, or the choice of tokenization, mostly rely on the local structure of text to build understanding and make limited use of the global structure.

2022-05-01

Findings of the Association for Computational Linguistics: ACL 2022 (publié)