Portrait of Sarath Chandar

Sarath Chandar

Core Academic Member
Canada CIFAR AI Chair
Associate Professor, Polytechnique Montréal, Department of Computer Engineering and Software Engineering
Adjunct Professor, Université de Montréal, Department of Computer Science and Operations Research
Indian Institute of Technology Madras
Research Topics
AI Alignment
Deep Learning
Explainable AI (XAI)
Foundation Models
Interpretability
Large Language Models (LLM)
Lifelong Learning
Medical Machine Learning
Multi-Agent Systems
Natural Language Processing
Online Learning
Optimization
Recurrent Neural Networks
Reinforcement Learning
Representation Learning
Transfer Learning
Trustworthy AI

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

PhD - Université de Montréal
Master's Research - Polytechnique Montréal
PhD - Polytechnique Montréal
Co-supervisor :
Collaborating researcher
Master's Research - McGill University
PhD - Université de Montréal
Principal supervisor :
Collaborating researcher
Principal supervisor :
Research Intern - Polytechnique Montréal
PhD - Université de Montréal
PhD - Université de Montréal
Co-supervisor :
Postdoctorate - Polytechnique Montréal
Master's Research - Polytechnique Montréal
PhD - Polytechnique Montréal
Master's Research - Université de Montréal
Co-supervisor :
PhD - Université de Montréal
PhD - Université de Montréal
Collaborating researcher - Polytechnique Montréal Montreal
Master's Research - Polytechnique Montréal
Master's Research - Polytechnique Montréal
Principal supervisor :
Master's Research - Université de Montréal
Co-supervisor :
PhD - Polytechnique Montréal
Postdoctorate - Université de Montréal
Master's Research - Université de Montréal
PhD - Polytechnique Montréal

Publications

A Deep Reinforcement Learning Chatbot (Short Version)
We present MILABOT: a deep reinforcement learning chatbot developed by the Montreal Institute for Learning Algorithms (MILA) for the Amazon … (see more)Alexa Prize competition. MILABOT is capable of conversing with humans on popular small talk topics through both speech and text. The system consists of an ensemble of natural language generation and retrieval models, including neural network and template-based models. By applying reinforcement learning to crowdsourced data and real-world user interactions, the system has been trained to select an appropriate response from the models in its ensemble. The system has been evaluated through A/B testing with real-world users, where it performed significantly better than other systems. The results highlight the potential of coupling ensemble systems with deep reinforcement learning as a fruitful path for developing real-world, open-domain conversational agents.
A Deep Reinforcement Learning Chatbot
We present MILABOT: a deep reinforcement learning chatbot developed by the Montreal Institute for Learning Algorithms (MILA) for the Amazon … (see more)Alexa Prize competition. MILABOT is capable of conversing with humans on popular small talk topics through both speech and text. The system consists of an ensemble of natural language generation and retrieval models, including template-based models, bag-of-words models, sequence-to-sequence neural network and latent variable neural network models. By applying reinforcement learning to crowdsourced data and real-world user interactions, the system has been trained to select an appropriate response from the models in its ensemble. The system has been evaluated through A/B testing with real-world users, where it performed significantly better than many competing systems. Due to its machine learning architecture, the system is likely to improve with additional data.
Hierarchical Memory Networks
Memory networks are neural networks with an explicit memory component that can be both read and written to by the network. The memory is oft… (see more)en addressed in a soft way using a softmax function, making end-to-end training with backpropagation possible. However, this is not computationally scalable for applications which require the network to read from extremely large memories. On the other hand, it is well known that hard attention mechanisms based on reinforcement learning are challenging to train successfully. In this paper, we explore a form of hierarchical memory network, which can be considered as a hybrid between hard and soft attention memory networks. The memory is organized in a hierarchical structure such that reading from it is done with less computation than soft attention over a flat memory, while also being easier to train than hard attention over a flat memory. Specifically, we propose to incorporate Maximum Inner Product Search (MIPS) in the training and inference procedures for our hierarchical memory network. We explore the use of various state-of-the art approximate MIPS techniques and report results on SimpleQuestions, a challenging large scale factoid question answering task.