23 citations · 80 across the 17 of their papers we have counts for
14 papers · 1 filter
ClustRecNet: A Novel End-to-End Deep Learning Framework for Clustering Algorithm Recommendation
Mohammadreza Bakhtyari, Bogdan Mazoure, Renato Cordeiro de Amorim +2
Identifying an effective clustering algorithm for a given dataset remains a fundamental unsupervised learning issue. We introduce ClustRecNet, a novel end-to-end deep learning fram…
Value function estimation using conditional diffusion models for control
Bogdan Mazoure, Walter Talbott, Miguel Angel Bautista +3
A fairly reliable trend in deep reinforcement learning is that the performance scales with the number of parameters, provided a complimentary scaling in amount of training data. As…
Accelerating exploration and representation learning with offline pre-training
Bogdan Mazoure, Jake Bruce, Doina Precup +2
Sequential decision-making agents struggle with long horizon tasks, since solving them requires multi-step reasoning. Most reinforcement learning (RL) algorithms address this chall…
Contrastive Value Learning: Implicit Models for Simple Offline RL
Bogdan Mazoure, Benjamin Eysenbach, Ofir Nachum +1
Model-based reinforcement learning (RL) methods are appealing in the offline setting because they allow an agent to reason about the consequences of actions without interacting wit…
The Sandbox Environment for Generalizable Agent Research (SEGAR)
R Devon Hjelm, Bogdan Mazoure, Florian Golemo +5
A broad challenge of research on generalization for sequential decision-making tasks in interactive environments is designing benchmarks that clearly landmark progress. While there…
Improving Zero-shot Generalization in Offline Reinforcement Learning using Generalized Similarity Functions
Bogdan Mazoure, Ilya Kostrikov, Ofir Nachum +1
Reinforcement learning (RL) agents are widely used for solving complex sequential decision making tasks, but still exhibit difficulty in generalizing to scenarios not seen during t…
Cross-Trajectory Representation Learning for Zero-Shot Generalization in RL
Bogdan Mazoure, Ahmed M. Ahmed, Patrick MacAlpine +2
A highly desirable property of a reinforcement learning (RL) agent -- and a major difficulty for deep RL approaches -- is the ability to generalize policies learned on a few tasks…
Improving Long-Term Metrics in Recommendation Systems using Short-Horizon Reinforcement Learning
Bogdan Mazoure, Paul Mineiro, Pavithra Srinath +3
We study session-based recommendation scenarios where we want to recommend items to users during sequential interactions to improve their long-term utility. Optimizing a long-term…
A Theoretical Analysis of Catastrophic Forgetting through the NTK Overlap Matrix
Thang Doan, Mehdi Bennani, Bogdan Mazoure +2
Continual learning (CL) is a setting in which an agent has to learn from an incoming stream of data during its entire lifetime. Although major advances have been made in the field,…
Deep Reinforcement and InfoMax Learning
Bogdan Mazoure, Remi Tachet des Combes, Thang Doan +2
We begin with the hypothesis that a model-free agent whose representations are predictive of properties of future states (beyond expected rewards) will be more capable of solving a…
Representation of Reinforcement Learning Policies in Reproducing Kernel Hilbert Spaces
Bogdan Mazoure, Thang Doan, Tianyu Li +4
We propose a general framework for policy representation for reinforcement learning tasks. This framework involves finding a low-dimensional embedding of the policy on a reproducin…
Attraction-Repulsion Actor-Critic for Continuous Control Reinforcement Learning
Thang Doan, Bogdan Mazoure, Moloud Abdar +3
Continuous control tasks in reinforcement learning are important because they provide an important framework for learning in high-dimensional state spaces with deceptive rewards, w…
Leveraging exploration in off-policy algorithms via normalizing flows
Bogdan Mazoure, Thang Doan, Audrey Durand +2
The ability to discover approximately optimal policies in domains with sparse rewards is crucial to applying reinforcement learning (RL) in many real-world scenarios. Approaches su…
On-line Adaptative Curriculum Learning for GANs
Thang Doan, Joao Monteiro, Isabela Albuquerque +4
Generative Adversarial Networks (GANs) can successfully approximate a probability distribution and produce realistic samples. However, open questions such as sufficient convergence…