3 papers
eess.SY2026
Non-Asymptotic Confidence Regions for Separable Nonlinear Models
Szabolcs Szentpéteri, Péter Kovács, Balázs Csanád Csáji
This paper studies robust uncertainty quantification for separable nonlinear least squares (SNLLS) problems. We propose two identification methods, (i) Residual-Perturbed Gradient…
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…