79 citations · 519 across the 58 of their papers we have counts for
7 papers · 1 filter
On the Sample Complexity of Representation Learning in Multi-task Bandits with Global and Local structure
Alessio Russo, Alexandre Proutiere
We investigate the sample complexity of learning the optimal arm for multi-task bandit problems. Arms consist of two components: one that is shared across tasks (that we call repre…
Self-Tuning Tube-based Model Predictive Control
Damianos Tranos, Alessio Russo, Alexandre Proutiere
We present Self-Tuning Tube-based Model Predictive Control (STT-MPC), an adaptive robust control algorithm for uncertain linear systems with additive disturbances based on the leas…
Tube-Based Zonotopic Data-Driven Predictive Control
Alessio Russo, Alexandre Proutiere
We present a novel tube-based data-driven predictive control method for linear systems affected by a bounded addictive disturbance. Our method leverages recent results in the reach…
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…
Measurement-based Admission Control in Sliced Networks: A Best Arm Identification Approach
Simon Lindståhl, Alexandre Proutiere, Andreas Johnsson
In sliced networks, the shared tenancy of slices requires adaptive admission control of data flows, based on measurements of network resources. In this paper, we investigate the de…
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…