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 :

Collaborateur·rice alumni - UdeM

Superviseur⋅e principal⋅e :

Irina Rish

Anshul Gupta

Stagiaire de recherche - 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

A responsible framework for applying artificial intelligence on medical images and signals at the point-of-care: the PACS-AI platform.

Pascal Thériault-Lauzier

Denis Cobin

Olivier Tastet

Élodie Labrecque Langlais

B. Taji

Guson Kang

A. Chong

Derek So

An Tang

J. W. Gichoya

Pierre-Luc Deziel

Julie G. Hussin

Samuel Kadoury

Robert Avram

2024-06-01

Canadian Journal of Cardiology (publié)

On the Costs and Benefits of Adopting Lifelong Learning for Software Analytics -- Empirical Study on Brown Build and Risk Prediction

Doriane Olewicki

Sarra Habchi

Mathieu Nayrolles

Mojtaba Faramarzi

Bram Adams

Nowadays, software analytics tools using machine learning (ML) models to, for example, predict the risk of a code change are well establishe… (voir plus)d. However, as the goals of a project shift over time, and developers and their habits change, the performance of said models tends to degrade (drift) over time. Current retraining practices typically require retraining a new model from scratch on a large updated dataset when performance decay is observed, thus incurring a computational cost; also there is no continuity between the models as the past model is discarded and ignored during the new model training. Even though the literature has taken interest in online learning approaches, those have rarely been integrated and evaluated in industrial environments. This paper evaluates the use of lifelong learning (LL) for industrial use cases at Ubisoft, evaluating both the performance and the required computational effort in comparison to the retraining-from-scratch approaches commonly used by the industry. LL is used to continuously build and maintain ML-based software analytics tools using an incremental learner that progressively updates the old model using new data. To avoid so-called"catastrophic forgetting"of important older data points, we adopt a replay buffer of older data, which still allows us to drastically reduce the size of the overall training dataset, and hence model training time.

2024-05-31

Proceedings of the 46th International Conference on Software Engineering: Software Engineering in Practice (publié)

Predicting the Impact of Model Expansion through the Minima Manifold: A Loss Landscape Perspective

Pranshu Malviya

Jerry Huang

Quentin Fournier

The optimal model for a given task is often challenging to determine, requiring training multiple models from scratch which becomes prohibit… (voir plus)ive as dataset and model sizes grow. A more efficient alternative is to reuse smaller pre-trained models by expanding them, however, this is not widely adopted as how this impacts training dynamics remains poorly understood. While prior works have introduced statistics to measure these effects, they remain flawed. To rectify this, we offer a new approach for understanding and quantifying the impact of expansion through the lens of the loss landscape, which has been shown to contain a manifold of linearly connected minima. Building on this new perspective, we propose a metric to study the impact of expansion by estimating the size of the manifold. Experimental results show a clear relationship between gains in performance and manifold size, enabling the comparison of candidate models and presenting a first step towards expanding models more reliably based on geometric properties of the loss landscape.

2024-05-24

ArXiv (prépublication)

Interpretability Needs a New Paradigm

Andreas Madsen

Himabindu Lakkaraju

Siva Reddy

2024-05-08

ArXiv (prépublication)

Sub-goal Distillation: A Method to Improve Small Language Agents

Maryam Hashemzadeh

Elias Stengel-Eskin

Marc-Alexandre Côté

While Large Language Models (LLMs) have demonstrated significant promise as agents in interactive tasks, their substantial computational req… (voir plus)uirements and restricted number of calls constrain their practical utility, especially in long-horizon interactive tasks such as decision-making or in scenarios involving continuous ongoing tasks. To address these constraints, we propose a method for transferring the performance of an LLM with billions of parameters to a much smaller language model (770M parameters). Our approach involves constructing a hierarchical agent comprising a planning module, which learns through Knowledge Distillation from an LLM to generate sub-goals, and an execution module, which learns to accomplish these sub-goals using elementary actions. In detail, we leverage an LLM to annotate an oracle path with a sequence of sub-goals towards completing a goal. Subsequently, we utilize this annotated data to fine-tune both the planning and execution modules. Importantly, neither module relies on real-time access to an LLM during inference, significantly reducing the overall cost associated with LLM interactions to a fixed cost. In ScienceWorld, a challenging and multi-task interactive text environment, our method surpasses standard imitation learning based solely on elementary actions by 16.7% (absolute). Our analysis highlights the efficiency of our approach compared to other LLM-based methods. Our code and annotated data for distillation can be found on GitHub.

2024-05-04

ArXiv (prépublication)

Faithfulness Measurable Masked Language Models

Andreas Madsen

Siva Reddy

2024-05-01

ICML.cc/2024/Conference (spotlight)

Contrast-agnostic Spinal Cord Segmentation: A Comparative Study of ConvNets and Vision Transformers

Enamundram Naga Karthik

Sandrine Bédard

Jan Valošek

Julien Cohen-Adad

The cross-sectional area (CSA) of the spinal cord (SC) computed from its segmentation is a relevant clinical biomarker for the diagnosis and… (voir plus) monitoring of cord compression and atrophy. One key limitation of existing automatic methods is that their SC segmentations depend on the MRI contrast, resulting in different CSA across contrasts. Furthermore, these methods rely on CNNs, leaving a gap in the literature for exploring the performance of modern deep learning (DL) architectures. In this study, we extend our recent work \cite{Bdard2023TowardsCS} by evaluating the contrast-agnostic SC segmentation capabilities of different classes of DL architectures, namely, ConvNeXt, vision transformers (ViTs), and hierarchical ViTs. We compared 7 different DL models using the open-source \textit{Spine Generic} Database of healthy participants

2024-04-27

MIDL.io/2024/Short_Papers (accepté)

Towards Practical Tool Usage for Continually Learning LLMs

Jerry Huang

Prasanna Parthasarathi

Mehdi Rezagholizadeh

Large language models (LLMs) show an innate skill for solving language based tasks. But insights have suggested an inability to adjust for i… (voir plus)nformation or task-solving skills becoming outdated, as their knowledge, stored directly within their parameters, remains static in time. Tool use helps by offloading work to systems that the LLM can access through an interface, but LLMs that use them still must adapt to nonstationary environments for prolonged use, as new tools can emerge and existing tools can change. Nevertheless, tools require less specialized knowledge, therefore we hypothesize they are better suited for continual learning (CL) as they rely less on parametric memory for solving tasks and instead focus on learning when to apply pre-defined tools. To verify this, we develop a synthetic benchmark and follow this by aggregating existing NLP tasks to form a more realistic testing scenario. While we demonstrate scaling model size is not a solution, regardless of tool usage, continual learning techniques can enable tool LLMs to both adapt faster while forgetting less, highlighting their potential as continual learners.

2024-04-14

ArXiv (prépublication)

Intelligent Switching for Reset-Free RL

Darshan Patil

Janarthanan Rajendran

Glen Berseth

In the real world, the strong episode resetting mechanisms that are needed to train agents in simulation are unavailable. The \textit{resett… (voir plus)ing} assumption limits the potential of reinforcement learning in the real world, as providing resets to an agent usually requires the creation of additional handcrafted mechanisms or human interventions. Recent work aims to train agents (\textit{forward}) with learned resets by constructing a second (\textit{backward}) agent that returns the forward agent to the initial state. We find that the termination and timing of the transitions between these two agents are crucial for algorithm success. With this in mind, we create a new algorithm, Reset Free RL with Intelligently Switching Controller (RISC) which intelligently switches between the two agents based on the agent's confidence in achieving its current goal. Our new method achieves state-of-the-art performance on several challenging environments for reset-free RL.

2024-01-16

ICLR.cc/2024/Conference (poster)

Intelligent Switching for Reset-Free RL

Darshan Patil

Janarthanan Rajendran

Glen Berseth

2024-01-16

ICLR.cc/2024/Conference (poster)

Mastering Memory Tasks with World Models

Mohammad Reza Samsami

Artem Zholus

Janarthanan Rajendran

Current model-based reinforcement learning (MBRL) agents struggle with long-term dependencies. This limits their ability to effectively solv… (voir plus)e tasks involving extended time gaps between actions and outcomes, or tasks demanding the recalling of distant observations to inform current actions. To improve temporal coherence, we integrate a new family of state space models (SSMs) in world models of MBRL agents to present a new method, Recall to Imagine (R2I). This integration aims to enhance both long-term memory and long-horizon credit assignment. Through a diverse set of illustrative tasks, we systematically demonstrate that R2I not only establishes a new state-of-the-art for challenging memory and credit assignment RL tasks, such as BSuite and POPGym, but also showcases superhuman performance in the complex memory domain of Memory Maze. At the same time, it upholds comparable performance in classic RL tasks, such as Atari and DMC, suggesting the generality of our method. We also show that R2I is faster than the state-of-the-art MBRL method, DreamerV3, resulting in faster wall-time convergence.

2024-01-16

ICLR.cc/2024/Conference (présentation orale)

Learning Conditional Policies for Crystal Design Using Offline Reinforcement Learning

Prashant Govindarajan

Santiago Miret

Jarrid Rector-Brooks

Mariano Phielipp

Janarthanan Rajendran

Navigating through the exponentially large chemical space to search for desirable materials is an extremely challenging task in material dis… (voir plus)covery. Recent developments in generative and geometric deep learning have shown...

2024-01-01

Digital Discovery (publié)