Sarath Chandar

istabrak.abbes@mila.quebec

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

Istabrak Abbes

Doctorat - UdeM

Alex Aselstyne

Maîtrise recherche - Polytechnique

alexander.aselstyne@mila.quebec

davide.baldelli@mila.quebec

Davide Baldelli

Doctorat - Polytechnique

Co-superviseur⋅e :

Amal Zouaq

diego.cerda-mardini@mila.quebec

Milan Bhan

Collaborateur·rice de recherche

milan.bhan@mila.quebec

Diego Cerda Mardini

Maîtrise recherche - McGill

Naga Karthik Enamundram

Doctorat - Polytechnique

Superviseur⋅e principal⋅e :

Julien Cohen-Adad

emvnagakarthik@gmail.com

prashant.govindarajan@mila.quebec

Prashant Govindarajan

Doctorat - Polytechnique

maryam.hashemzadeh@mila.quebec

Simon Guiroy

Doctorat - UdeM

Superviseur⋅e principal⋅e :

Doctorat - UdeM

david.heurtel-depeiges@mila.quebec

David Heurtel--Depeiges

Doctorat - Polytechnique

Jerry Huang

Doctorat - UdeM

jerry.huang@mila.quebec

Kanishk Jain

Doctorat - UdeM

Co-superviseur⋅e :

Ross Goroshin

kanishk.jain@mila.quebec

Saurav Jha

Postdoctorat - Polytechnique

saurav.jha@mila.quebec

Amir Ardalan Kalantari Dehaghi

Doctorat - UdeM

Doctorat - Polytechnique

lola.lebreton@mila.quebec

ekaterina.lobacheva@mila.quebec

Aidan Li

Maîtrise recherche - UdeM

Co-superviseur⋅e :

Postdoctorat - UdeM

Doctorat - Polytechnique

pranshu.malviya@mila.quebec

Roshan Munirathinam Sankaran Balaji

Collaborateur·rice de recherche - Polytechnique

roshan.munirathinam@mila.quebec

nilaksh.nilaksh@mila.quebec

Hadi NekoeiQachkanloo

Doctorat - UdeM

nekoeihe@mila.quebec

Nilaksh Nilaksh

Doctorat - Polytechnique

Doctorat - UdeM

darshan.patil@mila.quebec

Linda Peinthiere

Collaborateur·rice de recherche - Polytechnique Montreal

uphro_1@hotmail.com

mathieu.reymond@mila.quebec

Yann Pernot

Maîtrise recherche - Polytechnique

yann.pernot@mila.quebec

Mathieu Reymond

Collaborateur·rice alumni

Shaipranesh Senthilkumar

Doctorat - Polytechnique

shaipraneshgci@gmail.com

Arjun Vaithilingam Sudhakar

Nour Shaheen

Maîtrise recherche - Polytechnique

Superviseur⋅e principal⋅e :

Amine Mhedhbi

nour.shaheen@mila.quebec

Doctorat - Polytechnique

achille.sowa@mila.quebec

Anabel Tan

Postdoctorat - UdeM

anabel.tan@mila.quebec

Megh Thakkar

Maîtrise recherche - UdeM

megh.thakkar@mila.quebec

Doctorat - Polytechnique

arjun.vaithilingam-sudhakar@mila.quebec

Abdelrahman Zayed

Doctorat - Polytechnique

zayedabd@mila.quebec

Xutong Zhao

Doctorat - Polytechnique

xutong.zhao@mila.quebec

Artem Zholus

Doctorat - Polytechnique

artem.zholus@mila.quebec

Optimiser la conception CAO grâce aux LLM

Billets de blogue

19 décembre 2025

par

Prashant Govindarajan

Davide Baldelli

Quentin Fournier

Sarath Chandar

Lire l'article

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

3 mars 2025

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

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

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 self-supervision on large text corpora have become the default starting point for developing models f… (voir plus)or 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 pre-training 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.

2022-12-31

EMNLP (publié)

openreview.net

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

Ali Rahimi-Kalahroudi

Janarthanan Rajendran

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 -- exh… (voir plus)ibits model-based learning is effective adaptation to local changes in the environment, a particular form of adaptivity that is the focus of this work. In reinforcement learning, however, recent work has shown that modern deep model-based reinforcement-learning (MBRL) methods adapt poorly to local environment 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, we show that a conceptually simple variation of this traditional replay buffer is able to overcome this limitation. By removing only samples from the buffer from the local neighbourhood of the newly observed samples, deep world models can be built that maintain their accuracy across the state-space, while also being able to effectively adapt to local changes in the reward function. We demonstrate this by applying our replay-buffer variation to a deep version of the classical Dyna method, as well as to recent methods such as PlaNet and DreamerV2, demonstrating that deep model-based methods can adapt effectively as well to local changes in the environment.

2022-12-08

DeepRL @ Neural Information Processing Systems (accepté)

proceedings.mlr.press

Deep Learning on a Healthy Data Diet: Finding Important Examples for Fairness

Abdelrahman Zayed

Goncalo Mordido

Hamid Palangi

Samira Shabanian

Data-driven predictive solutions predominant in commercial applications tend to suffer from biases and stereotypes, which raises equity conc… (voir plus)erns. Prediction models may discover, use, or amplify spurious correlations based on gender or other protected personal characteristics, thus discriminating against marginalized groups. Mitigating gender bias has become an important research focus in natural language processing (NLP) and is an area where annotated corpora are available. Data augmentation reduces gender bias by adding counterfactual examples to the training dataset. In this work, we show that some of the examples in the augmented dataset can be not important or even harmful for fairness. We hence propose a general method for pruning both the factual and counterfactual examples to maximize the model's fairness as measured by the demographic parity, equality of opportunity, and equality of odds. The fairness achieved by our method surpasses that of data augmentation on three text classification datasets, using no more than half of the examples in the augmented dataset. Our experiments are conducted using models of varying sizes and pre-training settings.

2022-11-19

ArXiv (prépublication)

Towards Evaluating Adaptivity of Model-Based Reinforcement Learning Methods

Yi Wan

Ali Rahimi-Kalahroudi

Janarthanan Rajendran

Ida Momennejad

Harm van Seijen

In recent years, a growing number of deep model-based reinforcement learning (RL) methods have been introduced. The interest in deep model-b… (voir plus)ased RL is not surprising, given its many potential benefits, such as higher sample efficiency and the potential for fast adaption to changes in the environment. However, we demonstrate, using an improved version of the recently introduced Local Change Adaptation (LoCA) setup, that well-known model-based methods such as PlaNet and DreamerV2 perform poorly in their ability to adapt to local environmental changes. Combined with prior work that made a similar observation about the other popular model-based method, MuZero, a trend appears to emerge, suggesting that current deep model-based methods have serious limitations. We dive deeper into the causes of this poor performance, by identifying elements that hurt adaptive behavior and linking these to underlying techniques frequently used in deep model-based RL. We empirically validate these insights in the case of linear function approximation by demonstrating that a modified version of linear Dyna achieves effective adaptation to local changes. Furthermore, we provide detailed insights into the challenges of building an adaptive nonlinear model-based method, by experimenting with a nonlinear version of Dyna.

2022-07-18

International Conference on Machine Learning (Accept for Short Presentation)

proceedings.mlr.press

PatchUp: A Feature-Space Block-Level Regularization Technique for Convolutional Neural Networks

Mojtaba Faramarzi

Mohammad Amini

Akilesh Badrinaaraayanan

Vikas Verma

Large capacity deep learning models are often prone to a high generalization gap when trained with a limited amount of labeled training data… (voir plus). A recent class of methods to address this problem uses various ways to construct a new training sample by mixing a pair (or more) of training samples. We propose PatchUp, a hidden state block-level regularization technique for Convolutional Neural Networks (CNNs), that is applied on selected contiguous blocks of feature maps from a random pair of samples. Our approach improves the robustness of CNN models against the manifold intrusion problem that may occur in other state-of-the-art mixing approaches. Moreover, since we are mixing the contiguous block of features in the hidden space, which has more dimensions than the input space, we obtain more diverse samples for training towards different dimensions. Our experiments on CIFAR10/100, SVHN, Tiny-ImageNet, and ImageNet using ResNet architectures including PreActResnet18/34, WRN-28-10, ResNet101/152 models show that PatchUp improves upon, or equals, the performance of current state-of-the-art regularizers for CNNs. We also show that PatchUp can provide a better generalization to deformed samples and is more robust against adversarial attacks.

2022-06-27

AAAI Conference on Artificial Intelligence (publié)

Local Structure Matters Most: Perturbation Study in NLU

Louis Clouatre

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.

2022-04-30

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

Memory Augmented Optimizers for Deep Learning

Paul-Aymeric McRae

Mahmoud Assran

Popular approaches for minimizing loss in data-driven learning often involve an abstraction or an explicit retention of the history of gradi… (voir plus)ents for efficient parameter updates. The aggregated history of gradients nudges the parameter updates in the right direction even when the gradients at any given step are not informative. Although the history of gradients summarized in meta-parameters or explicitly stored in memory has been shown effective in theory and practice, the question of whether

2022-04-24

International Conference on Learning Representations (Accept (Poster))

openreview.net

Improving Sample Efficiency of Value Based Models Using Attention and Vision Transformers

Amir Ardalan Kalantari

Mohammad Amini

Doina Precup

Much of recent Deep Reinforcement Learning success is owed to the neural architecture's potential to learn and use effective internal repres… (voir plus)entations of the world. While many current algorithms access a simulator to train with a large amount of data, in realistic settings, including while playing games that may be played against people, collecting experience can be quite costly. In this paper, we introduce a deep reinforcement learning architecture whose purpose is to increase sample efficiency without sacrificing performance. We design this architecture by incorporating advances achieved in recent years in the field of Natural Language Processing and Computer Vision. Specifically, we propose a visually attentive model that uses transformers to learn a self-attention mechanism on the feature maps of the state representation, while simultaneously optimizing return. We demonstrate empirically that this architecture improves sample complexity for several Atari environments, while also achieving better performance in some of the games.

2022-01-31

arXiv (prépublication)

Detecting Languages Unintelligible to Multilingual Models through Local Structure Probes

Louis Clouatre

Amal Zouaq

Providing better language tools for low-resource and endangered languages is imperative for equitable growth. Recent progress with massively… (voir plus) multilingual pretrained models has proven surprisingly effective at performing zero-shot transfer to a wide variety of languages. However, this transfer is not universal, with many languages not currently understood by multilingual approaches. It is estimated that only 72 languages possess a "small set of labeled datasets" on which we could test a model's performance, the vast majority of languages not having the resources available to simply evaluate performances on. In this work, we attempt to clarify which languages do and do not currently benefit from such transfer. To that end, we develop a general approach that requires only unlabelled text to detect which languages are not well understood by a cross-lingual model. Our approach is derived from the hypothesis that if a model's understanding is insensitive to perturbations to text in a language, it is likely to have a limited understanding of that language. We construct a cross-lingual sentence similarity task to evaluate our approach empirically on 350, primarily low-resource, languages.

2021-12-31

EMNLP (Findings) (publié)

IIRC: Incremental Implicitly-Refined Classification

Mohamed Abdelsalam

Mojtaba Faramarzi

Shagun Sodhani

We introduce the "Incremental Implicitly-Refined Classi-fication (IIRC)" setup, an extension to the class incremental learning setup where t… (voir plus)he incoming batches of classes have two granularity levels. i.e., each sample could have a high-level (coarse) label like "bear" and a low-level (fine) label like "polar bear". Only one label is provided at a time, and the model has to figure out the other label if it has already learnfed it. This setup is more aligned with real-life scenarios, where a learner usually interacts with the same family of entities multiple times, discovers more granularity about them, while still trying not to forget previous knowledge. Moreover, this setup enables evaluating models for some important lifelong learning challenges that cannot be easily addressed under the existing setups. These challenges can be motivated by the example "if a model was trained on the class bear in one task and on polar bear in another task, will it forget the concept of bear, will it rightfully infer that a polar bear is still a bear? and will it wrongfully associate the label of polar bear to other breeds of bear?". We develop a standardized benchmark that enables evaluating models on the IIRC setup. We evaluate several state-of-the-art lifelong learning algorithms and highlight their strengths and limitations. For example, distillation-based methods perform relatively well but are prone to incorrectly predicting too many labels per image. We hope that the proposed setup, along with the benchmark, would provide a meaningful problem setting to the practitioners

2021-12-31

IEEE/CVF Conference on Computer Vision and Pattern Recognition (inconnu)

Scaling Laws for the Few-Shot Adaptation of Pre-trained Image Classifiers

Empirical science of neural scaling laws is a rapidly growing area of significant importance to the future of machine learning, particularly… (voir plus) in the light of recent breakthroughs achieved by large-scale pre-trained models such as GPT-3, CLIP and DALL-e. Accurately predicting the neural network performance with increasing resources such as data, compute and model size provides a more comprehensive evaluation of different approaches across multiple scales, as opposed to traditional point-wise comparisons of fixed-size models on fixed-size benchmarks, and, most importantly, allows for focus on the best-scaling, and thus most promising in the future, approaches. In this work, we consider a challenging problem of few-shot learning in image classification, especially when the target data distribution in the few-shot phase is different from the source, training, data distribution, in a sense that it includes new image classes not encountered during training. Our current main goal is to investigate how the amount of pre-training data affects the few-shot generalization performance of standard image classifiers. Our key observations are that (1) such performance improvements are well-approximated by power laws (linear log-log plots) as the training set size increases, (2) this applies to both cases of target data coming from either the same or from a different domain (i.e., new classes) as the training data, and (3) few-shot performance on new classes converges at a faster rate than the standard classification performance on previously seen classes. Our findings shed new light on the relationship between scale and generalization.

2021-10-12

arXiv (prépublication)

openreview.net

A Survey of Data Augmentation Approaches for NLP

Steven Y. Feng

Varun Gangal

Jason Wei

Soroush Vosoughi

Teruko Mitamura

Eduard Hovy

Data augmentation has recently seen increased interest in NLP due to more work in low-resource domains, new tasks, and the popularity of lar… (voir plus)ge-scale neural networks that require large amounts of training data. Despite this recent upsurge, this area is still relatively underexplored, perhaps due to the challenges posed by the discrete nature of language data. In this paper, we present a comprehensive and unifying survey of data augmentation for NLP by summarizing the literature in a structured manner. We first introduce and motivate data augmentation for NLP, and then discuss major methodologically representative approaches. Next, we highlight techniques that are used for popular NLP applications and tasks. We conclude by outlining current challenges and directions for future research. Overall, our paper aims to clarify the landscape of existing literature in data augmentation for NLP and motivate additional work in this area. We also present a GitHub repository with a paper list that will be continuously updated at https://github.com/styfeng/DataAug4NLP

2021-07-31

Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021 (publié)