2 citations · 3 across the 3 of their papers we have counts for
3 papers
stat.ML2025
Near-optimal Delta-convex Estimation of Lipschitz Functions
Gábor Balázs
This paper presents a tractable algorithm for estimating an unknown Lipschitz function from noisy observations and establishes an upper bound on its convergence rate. The approach…
math.OC2016★ 1 cited
Max-affine estimators for convex stochastic programming
Gábor Balázs, András György, Csaba Szepesvári
In this paper, we consider two sequential decision making problems with a convexity structure, namely an energy storage optimization task and a multi-product assembly example. We f…
stat.ML2016★ 2 cited
Chaining Bounds for Empirical Risk Minimization
Gábor Balázs, András György, Csaba Szepesvári
This paper extends the standard chaining technique to prove excess risk upper bounds for empirical risk minimization with random design settings even if the magnitude of the noise…