79 citations · 519 across the 58 of their papers we have counts for
19 papers · 1 filter
Conformal Off-Policy Evaluation in Markov Decision Processes
Daniele Foffano, Alessio Russo, Alexandre Proutiere
Reinforcement Learning aims at identifying and evaluating efficient control policies from data. In many real-world applications, the learner is not allowed to experiment and cannot…
Best Policy Identification in Linear MDPs
Jerome Taupin, Yassir Jedra, Alexandre Proutiere
We investigate the problem of best policy identification in discounted linear Markov Decision Processes in the fixed confidence setting under a generative model. We first derive an…
Nearly Optimal Latent State Decoding in Block MDPs
Yassir Jedra, Junghyun Lee, Alexandre Proutière +1
We investigate the problems of model estimation and reward-free learning in episodic Block MDPs. In these MDPs, the decision maker has access to rich observations or contexts gener…
Learning Optimal Antenna Tilt Control Policies: A Contextual Linear Bandit Approach
Filippo Vannella, Alexandre Proutiere, Yassir Jedra +1
Controlling antenna tilts in cellular networks is imperative to reach an efficient trade-off between network coverage and capacity. In this paper, we devise algorithms learning opt…
Minimal Expected Regret in Linear Quadratic Control
Yassir Jedra, Alexandre Proutiere
We consider the problem of online learning in Linear Quadratic Control systems whose state transition and state-action transition matrices and may be initially unknown. We…
Online Learning of Optimally Diverse Rankings
Stefan Magureanu, Alexandre Proutiere, Marcus Isaksson +1
Search engines answer users' queries by listing relevant items (e.g. documents, songs, products, web pages, ...). These engines rely on algorithms that learn to rank items so as to…
Regret Analysis in Deterministic Reinforcement Learning
Damianos Tranos, Alexandre Proutiere
We consider Markov Decision Processes (MDPs) with deterministic transitions and study the problem of regret minimization, which is central to the analysis and design of optimal lea…
Off-policy Learning for Remote Electrical Tilt Optimization
Filippo Vannella, Jaeseong Jeong, Alexandre Proutiere
We address the problem of Remote Electrical Tilt (RET) optimization using off-policy Contextual Multi-Armed-Bandit (CMAB) techniques. The goal in RET optimization is to control the…
Predictive Bandits
Simon Lindståhl, Alexandre Proutiere, Andreas Johnsson
We introduce and study a new class of stochastic bandit problems, referred to as predictive bandits. In each round, the decision maker first decides whether to gather information a…
Distributed Online Optimization with Long-Term Constraints
Deming Yuan, Alexandre Proutiere, Guodong Shi
We consider distributed online convex optimization problems, where the distributed system consists of various computing units connected through a time-varying communication graph.…
An Optimal Algorithm for Multiplayer Multi-Armed Bandits
Alexandre Proutiere, Po-An Wang
The paper addresses the Multiplayer Multi-Armed Bandit (MMAB) problem, where decision makers or players collaborate to maximize their cumulative reward. When several players se…
Optimal Attacks on Reinforcement Learning Policies
Alessio Russo, Alexandre Proutiere
Control policies, trained using the Deep Reinforcement Learning, have been recently shown to be vulnerable to adversarial attacks introducing even very small perturbations to the p…
Distributed Online Linear Regression
Deming Yuan, Alexandre Proutiere, Guodong Shi
We study online linear regression problems in a distributed setting, where the data is spread over a network. In each round, each network node proposes a linear predictor, with the…
Exploration in Structured Reinforcement Learning
Jungseul Ok, Alexandre Proutiere, Damianos Tranos
We address reinforcement learning problems with finite state and action spaces where the underlying MDP has some known structure that could be potentially exploited to minimize the…
Cluster-Aided Mobility Predictions
Jaeseong Jeong, Mathieu Leconte, Alexandre Proutiere
Predicting the future location of users in wireless net- works has numerous applications, and can help service providers to improve the quality of service perceived by their client…
Unimodal Bandits without Smoothness
Richard Combes, Alexandre Proutiere
We consider stochastic bandit problems with a continuous set of arms and where the expected reward is a continuous and unimodal function of the arm. No further assumption is made r…
Unimodal Bandits: Regret Lower Bounds and Optimal Algorithms
Richard Combes, Alexandre Proutiere
We consider stochastic multi-armed bandits where the expected reward is a unimodal function over partially ordered arms. This important class of problems has been recently investig…
Lipschitz Bandits: Regret Lower Bounds and Optimal Algorithms
Stefan Magureanu, Richard Combes, Alexandre Proutiere
We consider stochastic multi-armed bandit problems where the expected reward is a Lipschitz function of the arm, and where the set of arms is either discrete or continuous. For dis…
Spectrum Bandit Optimization
Marc Lelarge, Alexandre Proutiere, M. Sadegh Talebi
We consider the problem of allocating radio channels to links in a wireless network. Links interact through interference, modelled as a conflict graph (i.e., two interfering links…