Laurent Charlin

research

∙ 06/02/2023

Improving the generalizability and robustness of large-scale traffic signal control

A number of deep reinforcement-learning (RL) approaches propose to contr...

0 Tianyu Shi, et al. ∙

research

∙ 05/30/2023

Joint Bayesian Inference of Graphical Structure and Parameters with a Single Generative Flow Network

Generative Flow Networks (GFlowNets), a class of generative models over ...

0 Tristan Deleu, et al. ∙

research

∙ 04/25/2023

Towards Compute-Optimal Transfer Learning

The field of transfer learning is undergoing a significant shift with th...

0 Massimo Caccia, et al. ∙

research

∙ 11/04/2022

Bayesian learning of Causal Structure and Mechanisms with GFlowNets and Variational Bayes

Bayesian causal structure learning aims to learn a posterior distributio...

0 Mizu Nishikawa-Toomey, et al. ∙

research

∙ 08/01/2022

Model-based graph reinforcement learning for inductive traffic signal control

Most reinforcement learning methods for adaptive-traffic-signal-control ...

0 François-Xavier Devailly, et al. ∙

research

∙ 07/10/2022

Scaling the Number of Tasks in Continual Learning

Standard gradient descent algorithms applied to sequences of tasks are k...

9 Timothée Lesort, et al. ∙

research

∙ 06/27/2022

Learning To Cut By Looking Ahead: Cutting Plane Selection via Imitation Learning

Cutting planes are essential for solving mixed-integer linear problems (...

2 Max B. Paulus, et al. ∙

research

∙ 05/28/2022

Task-Agnostic Continual Reinforcement Learning: In Praise of a Simple Baseline

We study task-agnostic continual reinforcement learning (TACRL) in which...

11 Massimo Caccia, et al. ∙

research

∙ 04/30/2022

Foundational Models for Continual Learning: An Empirical Study of Latent Replay

Rapid development of large-scale pre-training has resulted in foundation...

12 Oleksiy Ostapenko, et al. ∙

research

∙ 03/04/2022

The Machine Learning for Combinatorial Optimization Competition (ML4CO): Results and Insights

Combinatorial optimization is a well-established area in operations rese...

12 Maxime Gasse, et al. ∙

research

∙ 03/03/2022

A New Era: Intelligent Tutoring Systems Will Transform Online Learning for Millions

Despite artificial intelligence (AI) having transformed major aspects of...

5 François St-Hilaire, et al. ∙

research

∙ 11/15/2021

Continual Learning via Local Module Composition

Modularity is a compelling solution to continual learning (CL), the prob...

5 Oleksiy Ostapenko, et al. ∙

research

∙ 08/02/2021

Sequoia: A Software Framework to Unify Continual Learning Research

The field of Continual Learning (CL) seeks to develop algorithms that ac...

12 Fabrice Normandin, et al. ∙

research

∙ 06/09/2021

Pretraining Representations for Data-Efficient Reinforcement Learning

Data efficiency is a key challenge for deep reinforcement learning. We a...

0 Max Schwarzer, et al. ∙

research

∙ 04/15/2021

Comparative Study of Learning Outcomes for Online Learning Platforms

Personalization and active learning are key aspects to successful learni...

17 François St-Hilaire, et al. ∙

research

∙ 03/18/2021

Beyond Trivial Counterfactual Explanations with Diverse Valuable Explanations

Explainability for machine learning models has gained considerable atten...

26 Pau Rodríguez, et al. ∙

research

∙ 10/27/2020

Multi-XScience: A Large-scale Dataset for Extreme Multi-document Summarization of Scientific Articles

Multi-document summarization is a challenging task for which there exist...

0 Yao Lu, et al. ∙

research

∙ 09/14/2020

Synbols: Probing Learning Algorithms with Synthetic Datasets

Progress in the field of machine learning has been fueled by the introdu...

58 Alexandre Lacoste, et al. ∙

research

∙ 05/06/2020

A Large-Scale, Open-Domain, Mixed-Interface Dialogue-Based ITS for STEM

We present Korbit, a large-scale, open-domain, mixed-interface, dialogue...

1 Iulian Vlad Serban, et al. ∙

research

∙ 04/23/2020

Inference for travel time on transportation networks

Travel time is essential for making travel decisions in real-world trans...

0 Mohamad Elmasri, et al. ∙

research

∙ 03/12/2020

Online Fast Adaptation and Knowledge Accumulation: a New Approach to Continual Learning

Learning from non-stationary data remains a great challenge for machine ...

26 Massimo Caccia, et al. ∙

research

∙ 03/06/2020

IG-RL: Inductive Graph Reinforcement Learning for Massive-Scale Traffic Signal Control

Scaling adaptive traffic-signal control involves dealing with combinator...

21 François-Xavier Devailly, et al. ∙

research

∙ 08/11/2019

Online Continual Learning with Maximally Interfered Retrieval

Continual learning, the setting where a learning agent is faced with a n...

4 Rahaf Aljundi, et al. ∙

research

∙ 06/04/2019

Exact Combinatorial Optimization with Graph Convolutional Neural Networks

Combinatorial optimization problems are typically tackled by the branch-...

25 Maxime Gasse, et al. ∙

research

∙ 06/03/2019

Continual Learning of New Sound Classes using Generative Replay

Continual learning consists in incrementally training a model on a seque...

0 Zhepei Wang, et al. ∙

research

∙ 02/25/2019

Session-based Social Recommendation via Dynamic Graph Attention Networks

Online communities such as Facebook and Twitter are enormously popular a...

0 Weiping Song, et al. ∙

research

∙ 12/18/2018

Towards Deep Conversational Recommendations

There has been growing interest in using neural networks and deep learni...

10 Raymond Li, et al. ∙

research

∙ 11/06/2018

Language GANs Falling Short

Generating high-quality text with sufficient diversity is essential for ...

1 Massimo Caccia, et al. ∙

research

∙ 08/20/2018

The Deconfounded Recommender: A Causal Inference Approach to Recommendation

The goal of a recommender system is to show its users items that they wi...

6 Yixin Wang, et al. ∙

research

∙ 06/12/2018

Focused Hierarchical RNNs for Conditional Sequence Processing

Recurrent Neural Networks (RNNs) with attention mechanisms have obtained...

0 Nan Rosemary Ke, et al. ∙

research

∙ 11/07/2017

Sparse Attentive Backtracking: Long-Range Credit Assignment in Recurrent Networks

A major drawback of backpropagation through time (BPTT) is the difficult...

0 Nan Rosemary Ke, et al. ∙

research

∙ 10/06/2017

Learnable Explicit Density for Continuous Latent Space and Variational Inference

In this paper, we study two aspects of the variational autoencoder (VAE)...

0 Chin-Wei Huang, et al. ∙

research

∙ 11/18/2016

Generative Deep Neural Networks for Dialogue: A Short Review

Researchers have recently started investigating deep neural networks for...

0 Iulian Vlad Serban, et al. ∙

research

∙ 05/19/2016

A Hierarchical Latent Variable Encoder-Decoder Model for Generating Dialogues

Sequential data often possesses a hierarchical structure with complex de...

0 Iulian Vlad Serban, et al. ∙

research

∙ 05/18/2016

On the Evaluation of Dialogue Systems with Next Utterance Classification

An open challenge in constructing dialogue systems is developing methods...

0 Ryan Lowe, et al. ∙

research

∙ 03/25/2016

How NOT To Evaluate Your Dialogue System: An Empirical Study of Unsupervised Evaluation Metrics for Dialogue Response Generation

We investigate evaluation metrics for dialogue response generation syste...

0 Chia-Wei Liu, et al. ∙

research

∙ 12/17/2015

A Survey of Available Corpora for Building Data-Driven Dialogue Systems

During the past decade, several areas of speech and language understandi...

0 Iulian Vlad Serban, et al. ∙

research

∙ 10/23/2015

Modeling User Exposure in Recommendation

Collaborative filtering analyzes user preferences for items (e.g., books...

0 Dawen Liang, et al. ∙

research

∙ 09/15/2015

Dynamic Poisson Factorization

Models for recommender systems use latent factors to explain the prefere...

0 Laurent Charlin, et al. ∙

research

∙ 11/10/2014

Deep Exponential Families

We describe deep exponential families (DEFs), a class of latent variable...

0 Rajesh Ranganath, et al. ∙

research

∙ 06/18/2012

Active Learning for Matching Problems

Effective learning of user preferences is critical to easing user burden...

0 Laurent Charlin, et al. ∙

research

∙ 06/13/2012

Hierarchical POMDP Controller Optimization by Likelihood Maximization

Planning can often be simpli ed by decomposing the task into smaller tas...

0 Marc Toussaint, et al. ∙

research

∙ 02/14/2012

A Framework for Optimizing Paper Matching

At the heart of many scientific conferences is the problem of matching s...

0 Laurent Charlin, et al. ∙

Laurent Charlin

Featured Co-authors