no code implementations • 24 Feb 2024 • Yassir Jedra, William Réveillard, Stefan Stojanovic, Alexandre Proutiere
For policy evaluation and best policy identification, we show that our algorithms are nearly minimax optimal.
no code implementations • 26 Sep 2023 • Axel Grönland, Alessio Russo, Yassir Jedra, Bleron Klaiqi, Xavier Gelabert
In the Centralized-Radio Access Network (C-RAN) architecture, functions can be placed in the central or distributed locations.
no code implementations • 7 Sep 2023 • Ingvar Ziemann, Anastasios Tsiamis, Bruce Lee, Yassir Jedra, Nikolai Matni, George J. Pappas
This tutorial serves as an introduction to recently developed non-asymptotic methods in the theory of -- mainly linear -- system identification.
no code implementations • 7 Jun 2023 • Yassir Jedra, Sean Mann, Charlotte Park, Devavrat Shah
Instead of treating this observation bias as a disadvantage, as is typically the case, the goal is to exploit the shared information between the bias and the outcome of interest to improve predictions.
1 code implementation • 17 Aug 2022 • Yassir Jedra, Junghyun Lee, Alexandre Proutière, Se-Young Yun
We investigate the problems of model estimation and reward-free learning in episodic Block MDPs.
no code implementations • 11 Aug 2022 • 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.
no code implementations • 6 Jan 2022 • Filippo Vannella, Alexandre Proutiere, Yassir Jedra, Jaeseong Jeong
In this paper, we devise algorithms learning optimal tilt control policies from existing data (in the so-called passive learning setting) or from data actively generated by the algorithms (the active learning setting).
no code implementations • 29 Sep 2021 • Yassir Jedra, Alexandre Proutiere
Quantifying the impact of such a constantly-varying control policy on the performance of these estimates and on the regret constitutes one of the technical challenges tackled in this paper.
no code implementations • NeurIPS 2020 • Yassir Jedra, Alexandre Proutiere
We study the problem of best-arm identification with fixed confidence in stochastic linear bandits.
no code implementations • 17 Mar 2020 • Yassir Jedra, Alexandre Proutiere
We present a new finite-time analysis of the estimation error of the Ordinary Least Squares (OLS) estimator for stable linear time-invariant systems.
no code implementations • 25 Mar 2019 • Yassir Jedra, Alexandre Proutiere
For controlled systems, our lower bounds are not as explicit as in the case of uncontrolled systems, but could well provide interesting insights into the design of control policy with minimal sample complexity.