3 citations · 8 across the 7 of their papers we have counts for
7 papers
Data-Driven Upper Confidence Bounds with Near-Optimal Regret for Heavy-Tailed Bandits
Ambrus Tamás, Szabolcs Szentpéteri, Balázs Csanád Csáji
Stochastic multi-armed bandits (MABs) provide a fundamental reinforcement learning model to study sequential decision making in uncertain environments. The upper confidence bounds…
Finite-Sample Identification of Linear Regression Models with Residual-Permuted Sums
Szabolcs Szentpéteri, Balázs Csanád Csáji
This letter studies a distribution-free, finite-sample data perturbation (DP) method, the Residual-Permuted Sums (RPS), which is an alternative of the Sign-Perturbed Sums (SPS) alg…
Signed-Perturbed Sums Estimation of ARX Systems: Exact Coverage and Strong Consistency (Extended Version)
Algo Carè, Erik Weyer, Balázs Cs. Csáji +1
Sign-Perturbed Sums (SPS) is a system identification method that constructs confidence regions for the unknown system parameters. In this paper, we study SPS for ARX systems, and e…
Sample Complexity of the Sign-Perturbed Sums Identification Method: Scalar Case
Szabolcs Szentpéteri, Balázs Csanád Csáji
Sign-Perturbed Sum (SPS) is a powerful finite-sample system identification algorithm which can construct confidence regions for the true data generating system with exact coverage…
Improving Kernel-Based Nonasymptotic Simultaneous Confidence Bands
Balázs Csanád Csáji, Bálint Horváth
The paper studies the problem of constructing nonparametric simultaneous confidence bands with nonasymptotic and distribition-free guarantees. The target function is assumed to be…
Robust Independence Tests with Finite Sample Guarantees for Synchronous Stochastic Linear Systems
Ambrus Tamás, Dániel Ágoston Bálint, Balázs Csanád Csáji
The paper introduces robust independence tests with non-asymptotically guaranteed significance levels for stochastic linear time-invariant systems, assuming that the observed outpu…