259 citations
- Centre de Recherche en Économie et StatistiqueFR18 papers
- Centre Inria de SaclayFR16 papers
- Centre National de la Recherche ScientifiqueFR14 papers
- Sorbonne UniversitéFR14 papers
- Laboratoire d'Informatique de GrenobleFR12 papers
- École Normale Supérieure Paris-SaclayFR11 papers
- ENSAE ParisFR11 papers
- École Nationale d'AdministrationSN9 papers
- Laboratoire d'Informatique, du Traitement de l'Information et des SystèmesFR8 papers
- École PolytechniqueFR7 papers
- Institut Systèmes Intelligents et de RobotiqueFR7 papers
- POLARIS: Évaluation et optimisation des performances des grandes infrastructuresFR6 papers
31 papers · 1 filter
On the Convergence of Approximate and Regularized Policy Iteration Schemes
Elena Smirnova, Elvis Dohmatob
Entropy regularized algorithms such as Soft Q-learning and Soft Actor-Critic, recently showed state-of-the-art performance on a number of challenging reinforcement learning (RL) ta…
Thompson Sampling in Non-Episodic Restless Bandits
Young Hun Jung, Marc Abeille, Ambuj Tewari
Restless bandit problems assume time-varying reward distributions of the arms, which adds flexibility to the model but makes the analysis more challenging. We study learning algori…
Learning from Bandit Feedback: An Overview of the State-of-the-art
Olivier Jeunen, Dmytro Mykhaylov, David Rohde +3
In machine learning we often try to optimise a decision rule that would have worked well over a historical dataset; this is the so called empirical risk minimisation principle. In…
Relaxed Softmax for learning from Positive and Unlabeled data
Ugo Tanielian, Flavian Vasile
In recent years, the softmax model and its fast approximations have become the de-facto loss functions for deep neural networks when dealing with multi-class prediction. This loss…
Ranking metrics on non-shuffled traffic
Alexandre Gilotte
Ranking metrics are a family of metrics largely used to evaluate recommender systems. However they typically suffer from the fact the reward is affected by the order in which recom…
How robust is MovieLens? A dataset analysis for recommender systems
Anne-Marie Tousch
Research publication requires public datasets. In recommender systems, some datasets are largely used to compare algorithms against a --supposedly-- common benchmark. Problem: for…