2 citations · 2 across the 5 of their papers we have counts for
6 papers
Kernel-Based Differentiable Learning of Non-Parametric Directed Acyclic Graphical Models
Yurou Liang, Oleksandr Zadorozhnyi, Mathias Drton
Causal discovery amounts to learning a directed acyclic graph (DAG) that encodes a causal model. This model selection problem can be challenging due to its large combinatorial sear…
Moment inequalities for sums of weakly dependent random fields
Gilles Blanchard, Alexandra Carpentier, Oleksandr Zadorozhnyi
We derive both Azuma-Hoeffding and Burkholder-type inequalities for partial sums over a rectangular grid of dimension of a random field satisfying a weak dependency assumption…
Online nonparametric regression with Sobolev kernels
Oleksandr Zadorozhnyi, Pierre Gaillard, Sebastien Gerschinovitz +1
In this work we investigate the variation of the online kernelized ridge regression algorithm in the setting of dimensional adversarial nonparametric regression. We derive the…
Efficient Regularized Piecewise-Linear Regression Trees
Leonidas Lefakis, Oleksandr Zadorozhnyi, Gilles Blanchard
We present a detailed analysis of the class of regression decision tree algorithms which employ a regulized piecewise-linear node-splitting criterion and have regularized linear mo…
Restless dependent bandits with fading memory
Oleksandr Zadorozhnyi, Gilles Blanchard, Alexandra Carpentier
We study the stochastic multi-armed bandit problem in the case when the arm samples are dependent over time and generated from so-called weak $\cC$-mixing processes. We establish a…
Concentration of weakly dependent Banach-valued sums and applications to statistical learning methods
Gilles Blanchard, Oleksandr Zadorozhnyi
We obtain a Bernstein-type inequality for sums of Banach-valued random variables satisfying a weak dependence assumption of general type and under certain smoothness assumptions of…