3 citations · 3 across the 3 of their papers we have counts for
3 papers
cs.LG2024
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…
eess.SY2024
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…
stat.ML2024★ 3 cited
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…