1 citations · 1 across the 5 of their papers we have counts for
5 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…
On Rate-Optimal Partitioning Classification from Observable and from Privatised Data
Balázs Csanád Csáji, László Györfi, Ambrus Tamás +1
In this paper we revisit the classical method of partitioning classification and prove novel convergence rates under relaxed conditions, both for observable (non-privatised) and fo…
Resampled Confidence Regions with Exponential Shrinkage for the Regression Function of Binary Classification
Ambrus Tamás, Balázs Csanád Csáji
The regression function is one of the key objects of binary classification, since it not only determines a Bayes optimal classifier, hence, defines an optimal decision boundary, bu…
Recursive Estimation of Conditional Kernel Mean Embeddings
Ambrus Tamás, Balázs Csanád Csáji
Kernel mean embeddings, a widely used technique in machine learning, map probability distributions to elements of a reproducing kernel Hilbert space (RKHS). For supervised learning…
Exact Distribution-Free Hypothesis Tests for the Regression Function of Binary Classification via Conditional Kernel Mean Embeddings
Ambrus Tamás, Balázs Csanád Csáji
In this paper we suggest two statistical hypothesis tests for the regression function of binary classification based on conditional kernel mean embeddings. The regression function…