Search Results for author: Albert Thomas

Found 6 papers, 1 papers with code

Model-based micro-data reinforcement learning: what are the crucial model properties and which model to choose?

1 code implementation ICLR 2021 Balázs Kégl, Gabriel Hurtado, Albert Thomas

We contribute to micro-data model-based reinforcement learning (MBRL) by rigorously comparing popular generative models using a fixed (random shooting) control agent.

Acrobot Model-based Reinforcement Learning

Refined bounds for randomized experimental design

no code implementations22 Dec 2020 Geovani Rizk, Igor Colin, Albert Thomas, Moez Draief

Experimental design is an approach for selecting samples among a given set so as to obtain the best estimator for a given criterion.

Experimental Design

Best Arm Identification in Graphical Bilinear Bandits

no code implementations14 Dec 2020 Geovani Rizk, Albert Thomas, Igor Colin, Rida Laraki, Yann Chevaleyre

We study the best arm identification problem in which the learner wants to find the graph allocation maximizing the sum of the bilinear rewards.

Parallel Contextual Bandits in Wireless Handover Optimization

no code implementations21 Jan 2019 Igor Colin, Albert Thomas, Moez Draief

As cellular networks become denser, a scalable and dynamic tuning of wireless base station parameters can only be achieved through automated optimization.

Multi-Armed Bandits

Mass Volume Curves and Anomaly Ranking

no code implementations3 May 2017 Stephan Clémençon, Albert Thomas

Here we formulate the issue of scoring anomalies as a M-estimation problem by means of a novel functional performance criterion, referred to as the Mass Volume curve (MV curve in short), whose optimal elements are strictly increasing transforms of the density almost everywhere on the support of the density.

Generalization Bounds

Calibration of One-Class SVM for MV set estimation

no code implementations30 Aug 2015 Albert Thomas, Vincent Feuillard, Alexandre Gramfort

Our approach makes it possible to tune the hyperparameters automatically and obtain nested set estimates.

Anomaly Detection

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