Portrait of Laurent Charlin

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
Interim Scientific Director, Leadership Team
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 :
Chris Pal
Website
Github
Google Scholar
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
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 :
Dhanya Sridhar
Website
Github
Google Scholar
Fırat Öncel
PhD - Concordia University
Principal supervisor :
Mirco Ravanelli
Website
Github
Google Scholar
Oleksiy Ostapenko
Collaborating Alumni - Université de Montréal
Google Scholar
Emiliano Penaloza
PhD - Université de Montréal
Website
Github
Gaurav Sahu
Postdoctorate - HEC Montréal
Co-supervisor :
Chris Pal
Website
Github
Google Scholar
Omar Salemohamed
PhD - Université de Montréal
Yipeng Zhang
PhD - Université de Montréal
Google Scholar

Publications

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