Sarath Chandar

Biography

Sarath Chandar is an associate professor at Polytechnique Montreal's Department of Computer and Software Engineering, where he leads the Chandar Research Lab. He is also a Core Academic Member at Mila – Quebec Artificial Intelligence Institute and holds a Canada CIFAR AI Chair and the Canada Research Chair in Lifelong Machine Learning.

Chandar’s research interests include lifelong learning, deep learning, optimization, reinforcement learning and natural language processing. To promote research in lifelong learning, Chandar created the Conference on Lifelong Learning Agents (CoLLAs) in 2022, for which he served as program chair in 2022 and 2023.

He has a PhD from Université de Montréal and an MSc (By Research) from the Indian Institute of Technology Madras.

Current Students

Ista Abbes

Master's Research - Université de Montréal

Davide Baldelli

PhD - Polytechnique Montréal

Co-supervisor :

Master's Research - Polytechnique Montréal

Louis Clouatre

PhD - Polytechnique Montréal

Principal supervisor :

Amal Zouaq

Naga Karthik Enamundram

PhD - Polytechnique Montréal

Principal supervisor :

Julien Cohen-Adad

emvnagakarthik@gmail.com

Prashant Govindarajan

PhD - Polytechnique Montréal

Simon Guiroy

PhD - Université de Montréal

Principal supervisor :

Collaborating researcher - Université de Montréal

Principal supervisor :

Liam Paull

Maryam Hashemzadeh

PhD - Université de Montréal

David Heurtel--Depeiges

PhD - Polytechnique Montréal

Amir Ardalan Kalantari Dehaghi

Jerry Huang

PhD - Université de Montréal

Collaborating Alumni

Lola Le Breton

Master's Research - Polytechnique Montréal

andreas.madsen@mila.quebec

Ekaterina Lobacheva

Postdoctorate - Université de Montréal

PhD - Polytechnique Montréal

Co-supervisor :

Siva Reddy

PhD - Polytechnique Montréal

Master's Research - Polytechnique Montréal

Mohamed Amine Merzouk

Postdoctorate - Polytechnique Montréal

Principal supervisor :

Hadi NekoeiQachkanloo

PhD - Université de Montréal

Darshan Patil

PhD - Université de Montréal

Gabriele Prato

PhD - Université de Montréal

Janarthanan Rajendran

Collaborating Alumni - Université de Montréal

Co-supervisor :

Postdoctorate

Independent visiting researcher

Mohammad R. Samsami

Master's Research - Université de Montréal

Master's Research - Polytechnique Montréal

Arjun Vaithilingam Sudhakar

Megh Thakkar

Master's Research - Université de Montréal

PhD - Polytechnique Montréal

Abdelrahman Zayed

PhD - Polytechnique Montréal

Xutong Zhao

PhD - Polytechnique Montréal

Artem Zholus

PhD - Polytechnique Montréal

NeoBERT: A New Frontier for Open-Source Encoder Language Models

Blog Posts

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

March 3, 2025

Lola Le Breton

Quentin Fournier

Sarath Chandar

Read the article

October 1, 2024

How Do We Explain AI and Ensure the Explanation Is True? Faithfulness Measurable Models Tell You How

Andrea Madsen

Siva Reddy

Sarath Chandar

Read the article

Publications

Protein Language Models: Is Scaling Necessary?

Quentin Fournier

Robert M. Vernon

Almer van der Sloot

Benjamin Schulz

Christopher James Langmead

2024-09-23

bioRxiv (preprint)

Are self-explanations from Large Language Models faithful?

Andreas Madsen

Siva Reddy

2024-08-01

Findings of the Association for Computational Linguistics ACL 2024 (published)

Should We Attend More or Less? Modulating Attention for Fairness

Abdelrahman Zayed

Goncalo Mordido

Samira Shabanian

2024-07-10

colmweb.org/COLM/2024/Conference (accepted)

Lookbehind-SAM: k steps back, 1 step forward

Goncalo Mordido

Pranshu Malviya

Aristide Baratin

2024-07-08

Proceedings of the 41st International Conference on Machine Learning (published)

proceedings.mlr.press

A Reinforcement Learning Pipeline for Band Gap-directed Crystal Generation

Prashant Govindarajan

Mathieu Reymond

Santiago Miret

Antoine Clavaud

Mariano Phielipp

Property-driven AI-automated material discovery presents unique challenges owing to the complex nature of the chemical structural space and … (see more)computationally expensive simulations. For crystalline solids, the band gap is an important property for designing semiconductors and batteries. However, optimizing crystals for a target band gap is difficult and not well-explored. Reinforcement learning (RL) shows promise towards optimizing crystals, as it can freely explore the chemical space. However, it relies on regular band gap evaluations, which can only be accurately computed through expensive Density Functional Theory (DFT) simulations. In this study, we propose an active learning-inspired pipeline that combines RL and DFT simulations for optimizing crystal compositions given a target band gap. The pipeline includes an RL policy for predicting atom types and a band gap network that is fine-tuned with DFT data. Preliminary results indicate the need for furthering the state-of-the-art to address the inherent challenges of the problem.

2024-07-08

BOKU.ac.at/2024/AI4Mat (poster)

Language Model-In-The-Loop: Data Optimal Approach to Recommend Actions in Text Games

Arjun V Sudhakar

Prasanna Parthasarathi

Janarthanan Rajendran

Large Language Models (LLMs) have demonstrated superior performance in language understanding benchmarks. A recent use case for LLMs involve… (see more)s training decision-making agents over textual information. The existing approach leverages LLM's linguistic priors for action candidate recommendations in text games, i.e., to operate without environment-provided actions. However, adapting LLMs to specific games/tasks requires a massive amount of annotated human gameplay. Moreover, in the existing approach, the language model was kept frozen during an agent's training process, which limits learning from in-game knowledge about the world. Hence, we explore strategies to adapt the language model for candidate recommendation with in-game transition in an online learning fashion to mitigate reliance on human-annotated gameplays, which are costly to acquire. In this paper, we propose in-game transition selection methods to adapt the LLM in the loop, reducing the dependency on using human-annotated gameplays while improving performance and convergence. Our method demonstrates a 53% relative improvement in average game score over the previous state-of-the-art model, achieving more than twice the convergence rate in a full-annotated dataset setting. Furthermore, even with only 10% of human annotation, we surpassed the 100\% state-of-the-art performance benchmark.

2024-06-20

rl-conference.cc/RLC/2024/Workshop/TAFM (published)

Promoting Exploration in Memory-Augmented Adam using Critical Momenta

Pranshu Malviya

Goncalo Mordido

Aristide Baratin

Reza Babanezhad Harikandeh

Jerry Huang

Simon Lacoste-Julien

Razvan Pascanu

Adaptive gradient-based optimizers, particularly Adam, have left their mark in training large-scale deep learning models. The strength of su… (see more)ch optimizers is that they exhibit fast convergence while being more robust to hyperparameter choice. However, they often generalize worse than non-adaptive methods. Recent studies have tied this performance gap to flat minima selection: adaptive methods tend to find solutions in sharper basins of the loss landscape, which in turn hurts generalization. To overcome this issue, we propose a new memory-augmented version of Adam that promotes exploration towards flatter minima by using a buffer of critical momentum terms during training. Intuitively, the use of the buffer makes the optimizer overshoot outside the basin of attraction if it is not wide enough. We empirically show that our method improves the performance of several variants of Adam on standard supervised language modelling and image classification tasks.

2024-06-09

TMLR (accepted)

Why Don't Prompt-Based Fairness Metrics Correlate?

Abdelrahman Zayed

Goncalo Mordido

Ioana Baldini

The widespread use of large language models has brought up essential questions about the potential biases these models might learn. This led… (see more) to the development of several metrics aimed at evaluating and mitigating these biases. In this paper, we first demonstrate that prompt-based fairness metrics exhibit poor agreement, as measured by correlation, raising important questions about the reliability of fairness assessment using prompts. Then, we outline six relevant reasons why such a low correlation is observed across existing metrics. Based on these insights, we propose a method called Correlated Fairness Output (CAIRO) to enhance the correlation between fairness metrics. CAIRO augments the original prompts of a given fairness metric by using several pre-trained language models and then selects the combination of the augmented prompts that achieves the highest correlation across metrics. We show a significant improvement in Pearson correlation from 0.3 and 0.18 to 0.90 and 0.98 across metrics for gender and religion biases, respectively. Our code is available at https://github.com/chandar-lab/CAIRO.

2024-06-09

ArXiv (preprint)

Why Don't Prompt-Based Fairness Metrics Correlate?

Abdelrahman Zayed

Goncalo Mordido

Ioana Baldini

2024-06-09

ArXiv (preprint)

A Deep Dive into the Trade-Offs of Parameter-Efficient Preference Alignment Techniques

Megh Thakkar

Quentin Fournier

Matthew D Riemer

Pin-Yu Chen

Amal Zouaq

Payel Das

Large language models are first pre-trained on trillions of tokens and then instruction-tuned or aligned to specific preferences. While pre-… (see more)training remains out of reach for most researchers due to the compute required, fine-tuning has become affordable thanks to parameter-efficient methods such as LoRA and QLoRA. Alignment is known to be sensitive to the many factors involved, including the quantity and quality of data, the alignment method, and the adapter rank. However, there has not yet been an extensive study of their effect on downstream performance. To address this gap, we conduct an in-depth investigation of the impact of popular choices for three crucial axes: (i) the alignment dataset (HH-RLHF and BeaverTails), (ii) the alignment technique (SFT and DPO), and (iii) the model (LLaMA-1, Vicuna-v1.3, Mistral-7b, and Mistral-7b-Instruct). Our extensive setup spanning over 300 experiments reveals consistent trends and unexpected findings. We observe how more informative data helps with preference alignment, cases where supervised fine-tuning outperforms preference optimization, and how aligning to a distinct preference boosts performance on downstream tasks. Through our in-depth analyses, we put forward key guidelines to help researchers perform more effective parameter-efficient LLM alignment.

2024-06-07

ArXiv (preprint)

A Deep Dive into the Trade-Offs of Parameter-Efficient Preference Alignment Techniques

Megh Thakkar

Quentin Fournier

Matthew D Riemer

Pin-Yu Chen

Amal Zouaq

Payel Das