11 citations · 47 across the 8 of their papers we have counts for
8 papers
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…
Pliable rejection sampling
Akram Erraqabi, Michal Valko, Alexandra Carpentier +1
Rejection sampling is a technique for sampling from difficult distributions. However, its use is limited due to a high rejection rate. Common adaptive rejection sampling methods ei…
Controlled Sparsity via Constrained Optimization or: How I Learned to Stop Tuning Penalties and Love Constraints
Jose Gallego-Posada, Juan Ramirez, Akram Erraqabi +2
The performance of trained neural networks is robust to harsh levels of pruning. Coupled with the ever-growing size of deep learning models, this observation has motivated extensiv…
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…
Image Segmentation by Iterative Inference from Conditional Score Estimation
Adriana Romero, Michal Drozdzal, Akram Erraqabi +2
Inspired by the combination of feedforward and iterative computations in the virtual cortex, and taking advantage of the ability of denoising autoencoders to estimate the score of…