Laurent Charlin

Core Academic Member

lcharlin@mila.quebec

Canada CIFAR AI Chair

Associate Professor, HEC Montréal, Department of Decision Sciences

Associate Professor, Université de Montréal, Department of Computer Science and Operations Research

Research Topics

AI for Science

Data Mining

Deep Learning

Generative Models

Graph Neural Networks

Information Retrieval

Natural Language Processing

Probabilistic Models

Recommender Systems

Reinforcement Learning

Representation Learning

Google Scholar

Biography

Laurent Charlin is the Interim Scientific Director of Mila – Quebec Artificial Intelligence Institute, a Canada CIFAR AI Chair, as well as an associate professor at HEC Montréal, the business school affiliated with Université de Montréal.

Charlin’s research focuses on developing novel machine learning models to aid in decision-making. Recent work has focused on learning from data that changes over time, and on applications in fields such as recommender systems and optimization.

He has a number of highly cited publications on dialogue systems (chatbots). He co-developed the Toronto Paper Matching System (TPMS), which has been widely used by computer science conferences for matching reviewers to papers. He has also given MOOCs, introductory talks and media interviews to contribute to knowledge transfer and improve AI literacy.

Current Students

Neda Adl

Master's Research - HEC Montréal

Github

Shubham Agarwal

Postdoctorate - HEC Montréal

Co-supervisor :

Dieng Awa

Master's Research - HEC Montréal

David Berger

PhD - Université de Montréal

Anirudh Buvanesh

PhD - Université de Montréal

Co-supervisor :

Aaron Courville

anirudb1102@gmail.com

Github

Félix Gauthier

Master's Research - HEC Montréal

Nicolas Goulet

PhD - HEC Montréal

Principal supervisor :

Eva Portelance

Google Scholar

Shubham Gupta

PhD - Université Laval

Principal supervisor :

Cem Subakan

Ben Hudson

PhD - Université de Montréal

Co-supervisor :

Emma Frejinger

ben.hudson@mila.quebec

Website

Github

Google Scholar

Mizu Nishikawa-Toomey

PhD - Université de Montréal

Co-supervisor :

PhD - Concordia University

Principal supervisor :

Collaborating Alumni - Université de Montréal

Google Scholar

Emiliano Penaloza

PhD - Université de Montréal

Website

Github

Gaurav Sahu

Postdoctorate - HEC Montréal

Co-supervisor :

PhD - Université de Montréal

Yipeng Zhang

PhD - Université de Montréal

Google Scholar

Publications

Learnable Explicit Density for Continuous Latent Space and Variational Inference

Chin-Wei Huang

Ahmed Touati

Laurent Dinh

Michal Drozdzal

Mohammad Havaei

Laurent Charlin

Aaron Courville

In this paper, we study two aspects of the variational autoencoder (VAE): the prior distribution over the latent variables and its correspon… (see more)ding posterior. First, we decompose the learning of VAEs into layerwise density estimation, and argue that having a flexible prior is beneficial to both sample generation and inference. Second, we analyze the family of inverse autoregressive flows (inverse AF) and show that with further improvement, inverse AF could be used as universal approximation to any complicated posterior. Our analysis results in a unified approach to parameterizing a VAE, without the need to restrict ourselves to use factorial Gaussians in the latent real space.

2017-10-06

ArXiv (preprint)

arxiv.org

A Sparse Probabilistic Model of User Preference Data

Matthew J. A. Smith

Laurent Charlin

Joelle Pineau

2017-04-11

Advances in Artificial Intelligence (published)

doi.org

A Hierarchical Latent Variable Encoder-Decoder Model for Generating Dialogues

Iulian V. Serban

Ryan Lowe

Sequential data often possesses hierarchical structures with complex dependencies between sub-sequences, such as found between the utterance… (see more)s in a dialogue. To model these dependencies in a generative framework, we propose a neural network-based generative architecture, with stochastic latent variables that span a variable number of time steps. We apply the proposed model to the task of dialogue response generation and compare it with other recent neural-network architectures. We evaluate the model performance through a human evaluation study. The experiments demonstrate that our model improves upon recently proposed models and that the latent variables facilitate both the generation of meaningful, long and diverse responses and maintaining dialogue state.

2017-02-12

Proceedings of the AAAI Conference on Artificial Intelligence (published)

doi.org

arxiv.org

Training End-to-End Dialogue Systems with the Ubuntu Dialogue Corpus

Ryan Thomas Lowe

Nissan Pow

Iulian V. Serban

Laurent Charlin

Chia-Wei Liu

Joelle Pineau

In this paper, we construct and train end-to-end neural network-based dialogue systems using an updated version of the recent Ubuntu Dialogu… (see more)e Corpus, a dataset containing almost 1 million multi-turn dialogues, with a total of over 7 million utterances and 100 million words. This dataset is interesting because of its size, long context lengths, and technical nature; thus, it can be used to train large models directly from data with minimal feature engineering, which can be both time consuming and expensive. We provide baselines in two different environments: one where models are trained to maximize the log-likelihood of a generated utterance conditioned on the context of the conversation, and one where models are trained to select the correct next response from a list of candidate responses. These are both evaluated on a recall task that we call Next Utterance Classification (NUC), as well as other generation-specific metrics. Finally, we provide a qualitative error analysis to help determine the most promising directions for future research on the Ubuntu Dialogue Corpus, and for end-to-end dialogue systems in general.

2017-01-20

Dialogue & Discourse (published)

doi.org

A Hierarchical Latent Variable Encoder-Decoder Model for Generating Dialogues

Iulian V. Serban

Ryan Lowe

2016-05-01