Sarath Chandar

Biographie

Sarath Chandar est professeur adjoint 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

Antoine Clavaud

Maîtrise recherche - Polytechnique

Louis Clouatre

Doctorat - Polytechnique

Superviseur⋅e principal⋅e :

Amal Zouaq

Jamal Colemal

Visiteur de recherche indépendant - no

Doctorat - UdeM

Naga Karthik Enamundram

Doctorat - Polytechnique

Superviseur⋅e principal⋅e :

Julien Cohen-Adad

emvnagakarthik@gmail.com

Prashant Govindarajan

Doctorat - Polytechnique

Simon Guiroy

Doctorat - UdeM

Superviseur⋅e principal⋅e :

Stagiaire de recherche - UdeM

Superviseur⋅e principal⋅e :

Liam Paull

Kshitij Gupta

Collaborateur·rice alumni - UdeM

Superviseur⋅e principal⋅e :

Doctorat - None

David Heurtel--Depeiges

Doctorat - Polytechnique

Jerry Huang

Maîtrise recherche - UdeM

Khurram Javed

Visiteur de recherche indépendant - NA

Amir Ardalan Kalantari Dehaghi

Collaborateur·rice alumni

Lola Le Breton

Maîtrise recherche - Polytechnique

andreas.madsen@mila.quebec

Andreas Madsen

Doctorat - Polytechnique

Co-superviseur⋅e :

Siva Reddy

Doctorat - Polytechnique

Postdoctorat - Polytechnique

Hadi NekoeiQachkanloo

Doctorat - UdeM

Doctorat - UdeM

Doctorat - UdeM

Ali Rahimi-Kalahroudi

Maîtrise recherche - UdeM

Janarthanan Rajendran

Postdoctorat - UdeM

Co-superviseur⋅e :

Doctorat - UdeM

Superviseur⋅e principal⋅e :

Postdoctorat

Visiteur de recherche indépendant

Arjun Vaithilingam Sudhakar

Mohammad R. Samsami

Maîtrise recherche - UdeM

Doctorat - Polytechnique

Co-superviseur⋅e :

Maîtrise recherche - UdeM

Doctorat - Polytechnique

Abdelrahman Zayed

Doctorat - Polytechnique

Xutong Zhao

Doctorat - Polytechnique

Artem Zholus

Doctorat - Polytechnique

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

Billets de blogue

1 octobre 2024

par

Andrea Madsen

Siva Reddy

Sarath Chandar

Lire l'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 (prépublication)

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

Jerry Huang

Prasanna Parthasarathi

Mehdi Rezagholizadeh

2024-08-16

ArXiv (prépublication)

Are self-explanations from Large Language Models faithful?

Andreas Madsen

Siva Reddy

2024-08-01

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

Exploring Quantization for Efficient Pre-Training of Transformer Language Models

Kamran Chitsaz

Quentin Fournier

Gonccalo Mordido

The increasing scale of Transformer models has led to an increase in their pre-training computational requirements. While quantization has p… (voir plus)roven to be effective after pre-training and during fine-tuning, applying quantization in Transformers during pre-training has remained largely unexplored at scale for language modeling. This study aims to explore the impact of quantization for efficient pre-training of Transformers, with a focus on linear layer components. By systematically applying straightforward linear quantization to weights, activations, gradients, and optimizer states, we assess its effects on model efficiency, stability, and performance during training. By offering a comprehensive recipe of effective quantization strategies to be applied during the pre-training of Transformers, we promote high training efficiency from scratch while retaining language modeling ability. Code is available at https://github.com/chandar-lab/EfficientLLMs.

2024-07-16

ArXiv (prépublication)

Should We Attend More or Less? Modulating Attention for Fairness

Abdelrahman Zayed

Goncalo Mordido

Samira Shabanian

2024-07-10

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

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 (publié)

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 … (voir plus)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… (voir plus)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 (publié)

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… (voir plus)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 (accepté)

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… (voir plus) 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 (prépublication)

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

2024-06-07

ArXiv (prépublication)

BindGPT: A Scalable Framework for 3D Molecular Design via Language Modeling and Reinforcement Learning

Artem Zholus

Maksim Kuznetsov

Roman Schutski

Shayakhmetov Rim

Daniil Polykovskiy

Alex Zhavoronkov

Generating novel active molecules for a given protein is an extremely challenging task for generative models that requires an understanding … (voir plus)of the complex physical interactions between the molecule and its environment. In this paper, we present a novel generative model, BindGPT which uses a conceptually simple but powerful approach to create 3D molecules within the protein's binding site. Our model produces molecular graphs and conformations jointly, eliminating the need for an extra graph reconstruction step. We pretrain BindGPT on a large-scale dataset and fine-tune it with reinforcement learning using scores from external simulation software. We demonstrate how a single pretrained language model can serve at the same time as a 3D molecular generative model, conformer generator conditioned on the molecular graph, and a pocket-conditioned 3D molecule generator. Notably, the model does not make any representational equivariance assumptions about the domain of generation. We show how such simple conceptual approach combined with pretraining and scaling can perform on par or better than the current best specialized diffusion models, language models, and graph neural networks while being two orders of magnitude cheaper to sample.

2024-06-06

ArXiv (prépublication)