Sarath Chandar

Biography

Sarath Chandar is an assistant 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

Antoine Clavaud

Master's Research - Polytechnique Montréal

Louis Clouatre

PhD - Polytechnique Montréal

Principal supervisor :

Amal Zouaq

Jamal Colemal

Independent visiting researcher - no

Mathieu Duchesneau

PhD - Université de Montréal

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

Kshitij Gupta

Collaborating Alumni - Université de Montréal

Principal supervisor :

Irina Rish

Maryam Hashemzadeh

PhD - Université de Montréal

David Heurtel--Depeiges

PhD - Polytechnique Montréal

Jerry Huang

PhD - Université de Montréal

Khurram Javed

Independent visiting researcher - NA

Amir Ardalan Kalantari Dehaghi

Collaborating Alumni

Lola Le Breton

Master's Research - Polytechnique Montréal

andreas.madsen@mila.quebec

Andreas Madsen

PhD - Polytechnique Montréal

Co-supervisor :

Siva Reddy

PhD - Polytechnique Montréal

Hadi NekoeiQachkanloo

PhD - Université de Montréal

Darshan Patil

PhD - Université de Montréal

Gabriele Prato

PhD - Université de Montréal

Ali Rahimi-Kalahroudi

Master's Research - Université de Montréal

Janarthanan Rajendran

Collaborating Alumni - Université de Montréal

Co-supervisor :

Postdoctorate

Independent visiting researcher

Arjun Vaithilingam Sudhakar

Mohammad R. Samsami

Master's Research - Université de Montréal

PhD - Polytechnique Montréal

Co-supervisor :

Master's Research - Université de Montréal

PhD - Polytechnique Montréal

Nishanth Anand Vemgal

PhD - McGill University

Principal supervisor :

PhD - Polytechnique Montréal

Xutong Zhao

PhD - Polytechnique Montréal

Artem Zholus

PhD - Polytechnique Montréal

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

Blog Posts

October 1, 2024

Andrea Madsen

Siva Reddy

Sarath Chandar

Read the article

Publications

Combining Domain and Alignment Vectors to Achieve Better Knowledge-Safety Trade-offs in LLMs

Megh Thakkar

Yash More

Quentin Fournier

Matthew D Riemer

Pin-Yu Chen

Amal Zouaq

Payel Das

There is a growing interest in training domain-expert LLMs that excel in specific technical fields compared to their general-purpose instruc… (see more)tion-tuned counterparts. However, these expert models often experience a loss in their safety abilities in the process, making them capable of generating harmful content. As a solution, we introduce an efficient and effective merging-based alignment method called \textsc{MergeAlign} that interpolates the domain and alignment vectors, creating safer domain-specific models while preserving their utility. We apply \textsc{MergeAlign} on Llama3 variants that are experts in medicine and finance, obtaining substantial alignment improvements with minimal to no degradation on domain-specific benchmarks. We study the impact of model merging through model similarity metrics and contributions of individual models being merged. We hope our findings open new research avenues and inspire more efficient development of safe expert LLMs.

2024-11-11

ArXiv (preprint)

Crystal Design Amidst Noisy DFT Signals: A Reinforcement Learning Approach

Prashant Govindarajan

Mathieu Reymond

Santiago Miret

Mariano Phielipp

2024-11-03

NeurIPS.cc/2024/Workshop/AI4Mat (published)

Do Robot Snakes Dream like Electric Sheep? Investigating the Effects of Architectural Inductive Biases on Hallucination

Jerry Huang

Prasanna Parthasarathi

Mehdi Rezagholizadeh

Boxing Chen

The growth in prominence of large language models (LLMs) in everyday life can be largely attributed to their generative abilities, yet some … (see more)of this is also owed to the risks and costs associated with their use. On one front is their tendency to \textit{hallucinate} false or misleading information, limiting their reliability. On another is the increasing focus on the computational limitations associated with traditional self-attention based LLMs, which has brought about new alternatives, in particular recurrent models, meant to overcome them. Yet it remains uncommon to consider these two concerns simultaneously. Do changes in architecture exacerbate/alleviate existing concerns about hallucinations? Do they affect how and where they occur? Through an extensive evaluation, we study how these architecture-based inductive biases affect the propensity to hallucinate. While hallucination remains a general phenomenon not limited to specific architectures, the situations in which they occur and the ease with which specific types of hallucinations can be induced can significantly differ based on the model architecture. These findings highlight the need for better understanding both these problems in conjunction with each other, as well as consider how to design more universal techniques for handling hallucinations.

2024-10-22

ArXiv (preprint)

Toward Debugging Deep Reinforcement Learning Programs with RLExplorer

Rached Bouchoucha

Ahmed Haj Yahmed

Darshan Patil

Janarthanan Rajendran

Amin Nikanjam

Foutse Khomh

Deep reinforcement learning (DRL) has shown success in diverse domains such as robotics, computer games, and recommendation systems. However… (see more), like any other software system, DRL-based software systems are susceptible to faults that pose unique challenges for debugging and diagnosing. These faults often result in unexpected behavior without explicit failures and error messages, making debugging difficult and time-consuming. Therefore, automating the monitoring and diagnosis of DRL systems is crucial to alleviate the burden on developers. In this paper, we propose RLExplorer, the first fault diagnosis approach for DRL-based software systems. RLExplorer automatically monitors training traces and runs diagnosis routines based on properties of the DRL learning dynamics to detect the occurrence of DRL-specific faults. It then logs the results of these diagnoses as warnings that cover theoretical concepts, recommended practices, and potential solutions to the identified faults. We conducted two sets of evaluations to assess RLExplorer. Our first evaluation of faulty DRL samples from Stack Overflow revealed that our approach can effectively diagnose real faults in 83% of the cases. Our second evaluation of RLExplorer with 15 DRL experts/developers showed that (1) RLExplorer could identify 3.6 times more defects than manual debugging and (2) RLExplorer is easily integrated into DRL applications.

2024-10-06

ArXiv (preprint)

Balancing Context Length and Mixing Times for Reinforcement Learning at Scale

Matthew D Riemer

Khimya Khetarpal

Janarthanan Rajendran

2024-09-25

NeurIPS.cc/2024/Conference (poster)

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)

Context-Aware Assistant Selection for Improved Inference Acceleration with Large Language Models

Jerry Huang

Prasanna Parthasarathi

Mehdi Rezagholizadeh

2024-08-16

ArXiv (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)