Portrait of Laurent Charlin

Laurent Charlin

Core Academic Member
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

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

Master's Research - HEC Montréal
Postdoctorate - HEC Montréal
Co-supervisor :
Master's Research - HEC Montréal
PhD - Université de Montréal
PhD - Université de Montréal
Co-supervisor :
Master's Research - HEC Montréal
PhD - Université Laval
Principal supervisor :
PhD - Université de Montréal
Co-supervisor :
PhD - Université de Montréal
Co-supervisor :
PhD - Concordia University
Principal supervisor :
Collaborating Alumni - Université de Montréal
PhD - Université de Montréal
Postdoctorate - HEC Montréal
Co-supervisor :
PhD - Université de Montréal
PhD - Université de Montréal

Publications

IG-RL: Inductive Graph Reinforcement Learning for Massive-Scale Traffic Signal Control
François-Xavier Devailly
Denis Larocque
Scaling adaptive traffic signal control involves dealing with combinatorial state and action spaces. Multi-agent reinforcement learning atte… (see more)mpts to address this challenge by distributing control to specialized agents. However, specialization hinders generalization and transferability, and the computational graphs underlying neural-network architectures—dominating in the multi-agent setting—do not offer the flexibility to handle an arbitrary number of entities which changes both between road networks, and over time as vehicles traverse the network. We introduce Inductive Graph Reinforcement Learning (IG-RL) based on graph-convolutional networks which adapts to the structure of any road network, to learn detailed representations of traffic signal controllers and their surroundings. Our decentralized approach enables learning of a transferable-adaptive-traffic-signal-control policy. After being trained on an arbitrary set of road networks, our model can generalize to new road networks and traffic distributions, with no additional training and a constant number of parameters, enabling greater scalability compared to prior methods. Furthermore, our approach can exploit the granularity of available data by capturing the (dynamic) demand at both the lane level and the vehicle level. The proposed method is tested on both road networks and traffic settings never experienced during training. We compare IG-RL to multi-agent reinforcement learning and domain-specific baselines. In both synthetic road networks and in a larger experiment involving the control of the 3,971 traffic signals of Manhattan, we show that different instantiations of IG-RL outperform baselines.
Learning To Cut By Looking Ahead: Cutting Plane Selection via Imitation Learning
Max B. Paulus
Giulia Zarpellon
Andreas Krause
Chris J. Maddison
Cutting planes are essential for solving mixed-integer linear problems (MILPs), because they facilitate bound improvements on the optimal so… (see more)lution value. For selecting cuts, modern solvers rely on manually designed heuristics that are tuned to gauge the potential effectiveness of cuts. We show that a greedy selection rule explicitly looking ahead to select cuts that yield the best bound improvement delivers strong decisions for cut selection - but is too expensive to be deployed in practice. In response, we propose a new neural architecture (NeuralCut) for imitation learning on the lookahead expert. Our model outperforms standard baselines for cut selection on several synthetic MILP benchmarks. Experiments with a B&C solver for neural network verification further validate our approach, and exhibit the potential of learning methods in this setting.
A New Era: Intelligent Tutoring Systems Will Transform Online Learning for Millions
Francois St-Hilaire
Dung D. Vu
Antoine Frau
Nathan J. Burns
Farid Faraji
Joseph Potochny
Stephane Robert
Arnaud Roussel
Selene Zheng
Taylor Glazier
Junfel Vincent Romano
Robert Belfer
Muhammad Shayan
Ariella Smofsky
Tommy Delarosbil
Seulmin Ahn
Simon Eden-Walker
Kritika Sony
Ansona Onyi Ching
Sabina Elkins … (see 11 more)
A. Stepanyan
Adela Matajova
Victor Chen
Hossein Sahraei
Robert Larson
N. Markova
Andrew Barkett
Iulian V. Serban
Ekaterina Kochmar
COIL: A Deep Architecture for Column Generation
Behrouz Babaki
Sanjay Dominik Jena
. Column generation is a popular method to solve large-scale linear programs with an exponential number of variables. Several important appl… (see more)ications, such as the vehicle routing problem, rely on this technique in order to be solved. However, in practice, column generation methods suffer from slow convergence (i.e. they require too many iterations). Stabilization techniques, which carefully select the column to add at each iteration, are commonly used to improve convergence. In this work, we frame the problem of selecting which columns to add as one of sequential decision-making. We propose a neural column generation architecture that iteratively selects columns to be added to the problem. Our architecture is inspired by stabilization techniques and predicts the optimal duals, which are then used to select the columns to add. We proposed architecture, trained using imitation learning. Exemplified on the Vehicle Routing Problem, we show that several machine learning models yield good performance in predicting the optimal duals and that our architecture outperforms them as well as a popular state-of-the-art stabilization technique. Further, the architecture approach can generalize to instances larger than those observed during training.
Continual Learning with Foundation Models: An Empirical Study of Latent Replay
Oleksiy Ostapenko
Timothee LESORT
Pau Rodriguez
Md Rifat Arefin
Arthur Douillard
Scaling the Number of Tasks in Continual Learning
Timothee LESORT
Oleksiy Ostapenko
Diganta Misra
Md Rifat Arefin
Pau Rodriguez
Task-Agnostic Continual Reinforcement Learning: In Praise of a Simple Baseline
Massimo Caccia
Jonas Mueller
Taesup Kim
Rasool Fakoor
We study task-agnostic continual reinforcement learning (TACRL) in which standard RL challenges are compounded with partial observability st… (see more)emming from task agnosticism, as well as additional difficulties of continual learning (CL), i.e., learning on a non-stationary sequence of tasks. Here we compare TACRL methods with their soft upper bounds prescribed by previous literature: multi-task learning (MTL) methods which do not have to deal with non-stationary data distributions, as well as task-aware methods, which are allowed to operate under full observability . We consider a previously unexplored and straightforward baseline for TACRL, replay-based recurrent RL (3RL), in which we augment an RL algorithm with recurrent mechanisms to address partial observability and experience replay mechanisms to address catastrophic forgetting in CL. Studying empirical performance in a sequence of RL tasks, we find surprising occurrences of 3RL matching and overcoming the MTL and task-aware soft upper bounds. We lay out hypotheses that could explain this inflection point of continual and task-agnostic learning research. Our hypotheses are empirically tested in continuous control tasks via a large-scale study of the popular multi-task and continual learning benchmark Meta-World. By analyzing different training statistics including gradient conflict, we find evidence that 3RL’s outperformance stems from its ability to quickly infer how new tasks relate with the previous ones, enabling forward transfer.
Neural Column Generation for Capacitated Vehicle Routing
Behrouz Babaki
Sanjay Dominik Jena
The column generation technique is essential for solving linear programs with an exponential number of variables. Many important application… (see more)s such as the vehicle routing problem (VRP) now require it. However, in practice, getting column generation to converge is challenging. It often ends up adding too many columns. In this work, we frame the problem of selecting which columns to add as one of sequential decision-making. We propose a neural column generation architecture that iteratively selects columns to be added to the problem. The architecture, inspired by stabilization techniques, first predicts the optimal duals. These predictions are then used to obtain the columns to add. We show using VRP instances that in this setting several machine learning models yield good performance on the task and that our proposed architecture learned using imitation learning outperforms a modern stabilization technique.
Beyond Trivial Counterfactual Explanations with Diverse Valuable Explanations
Pau Rodriguez
Massimo Caccia
Alexandre Lacoste
Lee Zamparo
Issam Hadj Laradji
David Vazquez
Explainability for machine learning models has gained considerable attention within the research community given the importance of deploying… (see more) more reliable machine-learning systems. In computer vision applications, generative counterfactual methods indicate how to perturb a model’s input to change its prediction, providing details about the model’s decision-making. Current methods tend to generate trivial counterfactuals about a model’s decisions, as they often suggest to exaggerate or remove the presence of the attribute being classified. For the machine learning practitioner, these types of counterfactuals offer little value, since they provide no new information about undesired model or data biases. In this work, we identify the problem of trivial counterfactual generation and we propose DiVE to alleviate it. DiVE learns a perturbation in a disentangled latent space that is constrained using a diversity-enforcing loss to uncover multiple valuable explanations about the model’s prediction. Further, we introduce a mechanism to prevent the model from producing trivial explanations. Experiments on CelebA and Synbols demonstrate that our model improves the success rate of producing high-quality valuable explanations when compared to previous state-of-the-art methods. Code is available at https://github.com/ElementAI/beyond-trivial-explanations.
Sequoia: A Software Framework to Unify Continual Learning Research
Fabrice Normandin
Florian Golemo
Oleksiy Ostapenko
Pau Rodriguez
Matthew D Riemer
J. Hurtado
Lucas Cecchi
Dominic Zhao
Ryan Lindeborg
Timothee LESORT
David Vazquez
Massimo Caccia
The field of Continual Learning (CL) seeks to develop algorithms that accumulate knowledge and skills over time through interaction with non… (see more)-stationary environments. In practice, a plethora of evaluation procedures (settings) and algorithmic solutions (methods) exist, each with their own potentially disjoint set of assumptions. This variety makes measuring progress in CL difficult. We propose a taxonomy of settings, where each setting is described as a set of assumptions. A tree-shaped hierarchy emerges from this view, where more general settings become the parents of those with more restrictive assumptions. This makes it possible to use inheritance to share and reuse research, as developing a method for a given setting also makes it directly applicable onto any of its children. We instantiate this idea as a publicly available software framework called Sequoia, which features a wide variety of settings from both the Continual Supervised Learning (CSL) and Continual Reinforcement Learning (CRL) domains. Sequoia also includes a growing suite of methods which are easy to extend and customize, in addition to more specialized methods from external libraries. We hope that this new paradigm and its first implementation can help unify and accelerate research in CL. You can help us grow the tree by visiting (this GitHub URL).
Comparative Study of Learning Outcomes for Online Learning Platforms
Francois St-Hilaire
Nathan J. Burns
Robert Belfer
Muhammad Shayan
Ariella Smofsky
Dung D. Vu
Antoine Frau
Joseph Potochny
Farid Faraji
Vincent Pavero
Neroli Ko
Ansona Onyi Ching
Sabina Elkins
A. Stepanyan
Adela Matajova
Iulian V. Serban
Ekaterina Kochmar
Continual Learning via Local Module Composition
Oleksiy Ostapenko
Pau Rodriguez
Massimo Caccia
Modularity is a compelling solution to continual learning (CL), the problem of modeling sequences of related tasks. Learning and then compos… (see more)ing modules to solve different tasks provides an abstraction to address the principal challenges of CL including catastrophic forgetting, backward and forward transfer across tasks, and sub-linear model growth. We introduce local module composition (LMC), an approach to modular CL where each module is provided a local structural component that estimates a module's relevance to the input. Dynamic module composition is performed layer-wise based on local relevance scores. We demonstrate that agnosticity to task identities (IDs) arises from (local) structural learning that is module-specific as opposed to the task- and/or model-specific as in previous works, making LMC applicable to more CL settings compared to previous works. In addition, LMC also tracks statistics about the input distribution and adds new modules when outlier samples are detected. In the first set of experiments, LMC performs favorably compared to existing methods on the recent Continual Transfer-learning Benchmark without requiring task identities. In another study, we show that the locality of structural learning allows LMC to interpolate to related but unseen tasks (OOD), as well as to compose modular networks trained independently on different task sequences into a third modular network without any fine-tuning. Finally, in search for limitations of LMC we study it on more challenging sequences of 30 and 100 tasks, demonstrating that local module selection becomes much more challenging in presence of a large number of candidate modules. In this setting best performing LMC spawns much fewer modules compared to an oracle based baseline, however, it reaches a lower overall accuracy. The codebase is available under https://github.com/oleksost/LMC.