3 papers
stat.ML2026
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…
math.ST2024
Distribution-free tests for lossless feature selection in classification and regression
László Györfi, Tamás Linder, Harro Walk
We study the problem of lossless feature selection for a -dimensional feature vector and label for binary classification as well as nonparametri…
math.PR2024
On the strong stability of ergodic iterations
László Györfi, Attila Lovas, Miklós Rásonyi
We revisit processes generated by iterated random functions driven by a stationary and ergodic sequence. Such a process is called strongly stable if a random initialization exists,…