11 citations · 21 across the 4 of their papers we have counts for
3 papers · 1 filter
Trading off rewards and errors in multi-armed bandits
Akram Erraqabi, Alessandro Lazaric, Michal Valko +2
In multi-armed bandits, the most-explored arms are the most informative, while reward maximization typically pulls only the best arm. We study the tradeoff between identifying arm…
Temporal Abstractions-Augmented Temporally Contrastive Learning: An Alternative to the Laplacian in RL
Akram Erraqabi, Marlos C. Machado, Mingde Zhao +4
In reinforcement learning, the graph Laplacian has proved to be a valuable tool in the task-agnostic setting, with applications ranging from skill discovery to reward shaping. Rece…
A3T: Adversarially Augmented Adversarial Training
Akram Erraqabi, Aristide Baratin, Yoshua Bengio +1
Recent research showed that deep neural networks are highly sensitive to so-called adversarial perturbations, which are tiny perturbations of the input data purposely designed to f…