4 papers
Stochastic Approximation in a Markovian Framework Revisited: Lipschitz Continuity of the Poisson Equation
Algo Carè, Balázs Csanád Csáji, Balázs Gerencsér +2
In this paper we revisit a fundamental technical issue within the theory of stochastic approximation (SA) in a Markovian framework, first proposed in the book by Djereveckii and Fr…
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…
Non-Asymptotic State-Space Identification of Closed-Loop Stochastic Linear Systems using Instrumental Variables
Szabolcs Szentpéteri, Balázs Csanád Csáji
The paper suggests a generalization of the Sign-Perturbed Sums (SPS) finite sample system identification method for the identification of closed-loop observable stochastic linear s…