107 citations
- SCOOL: Prise de décision séquentielle dans l'incertainFR13 papers
- Google DeepMind (United Kingdom)GB9 papers
- Centre de Recherche en Informatique, Signal et Automatique de LilleFR8 papers
- Centre National de la Recherche ScientifiqueFR8 papers
- Centre de Recherche en InformatiqueFR6 papers
- Meta (Israel)IL5 papers
- Centre Inria de l'Université de LilleFR3 papers
- Google (United States)US3 papers
- Otto-von-Guericke-Universität MagdeburgDE3 papers
- Chalmers University of TechnologySE2 papers
- Département d'InformatiqueFR2 papers
- École Centrale de LilleFR2 papers
41 papers
A single algorithm for both restless and rested rotting bandits
Julien Seznec, Pierre Ménard, Alessandro Lazaric +1
In many application domains (e.g., recommender systems, intelligent tutoring systems), the rewards associated to the actions tend to decrease over time. This decay is either caused…
Adaptive multi-fidelity optimization with fast learning rates
Come Fiegel, Victor Gabillon, Michal Valko
In multi-fidelity optimization, biased approximations of varying costs of the target function are available. This paper studies the problem of optimizing a locally smooth function…
Sample Complexity Bounds for Stochastic Shortest Path with a Generative Model
Jean Tarbouriech, Matteo Pirotta, Michal Valko +1
We study the sample complexity of learning an -optimal policy in the Stochastic Shortest Path (SSP) problem. We first derive sample complexity bounds when the learner has access…
Kriging and Gaussian Process Interpolation for Georeferenced Data Augmentation
Frédérick Fabre Ferber, Dominique Gay, Jean-Christophe Soulié +2
Data augmentation is a crucial step in the development of robust supervised learning models, especially when dealing with limited datasets. This study explores interpolation techni…
Marich: A Query-efficient Distributionally Equivalent Model Extraction Attack using Public Data
Pratik Karmakar, Debabrota Basu
We study design of black-box model extraction attacks that can send minimal number of queries from a publicly available dataset to a target ML model through a predictive API with a…
Entropy Regularized Reinforcement Learning with Cascading Networks
Riccardo Della Vecchia, Alena Shilova, Philippe Preux +1
Deep Reinforcement Learning (Deep RL) has had incredible achievements on high dimensional problems, yet its learning process remains unstable even on the simplest tasks. Deep RL us…