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pro vyhledávání: '"A, Györfi"'
In this paper we revisit the classical method of partitioning classification and study its convergence rate under relaxed conditions, both for observable (non-privatised) and for privatised data. Let the feature vector $X$ take values in $\mathbb{R}^
Externí odkaz:
http://arxiv.org/abs/2312.14889
We study the problem of lossless feature selection for a $d$-dimensional feature vector $X=(X^{(1)},\dots ,X^{(d)})$ and label $Y$ for binary classification as well as nonparametric regression. For an index set $S\subset \{1,\dots ,d\}$, consider the
Externí odkaz:
http://arxiv.org/abs/2311.05033
We study the excess minimum risk in statistical inference, defined as the difference between the minimum expected loss in estimating a random variable from an observed feature vector and the minimum expected loss in estimating the same random variabl
Externí odkaz:
http://arxiv.org/abs/2307.16735
We study the problem nonparametric classification with repeated observations. Let $\bX$ be the $d$ dimensional feature vector and let $Y$ denote the label taking values in $\{1,\dots ,M\}$. In contrast to usual setup with large sample size $n$ and re
Externí odkaz:
http://arxiv.org/abs/2307.09896
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, for which the process is stationary and ergodic, and for any other in
Externí odkaz:
http://arxiv.org/abs/2304.04657
Autor:
Györfi, László, Kroll, Martin
We consider the problem of estimating a regression function from anonymized data in the framework of local differential privacy. We propose a novel partitioning estimate of the regression function, derive a rate of convergence for the excess predicti
Externí odkaz:
http://arxiv.org/abs/2206.00114
We study the problem of estimating the density $f(\boldsymbol x)$ of a random vector ${\boldsymbol X}$ in $\mathbb R^d$. For a spanning tree $T$ defined on the vertex set $\{1,\dots ,d\}$, the tree density $f_{T}$ is a product of bivariate conditiona
Externí odkaz:
http://arxiv.org/abs/2111.11971
Autor:
Györfi, László, Kroll, Martin
We revisit the classical problem of nonparametric density estimation but impose local differential privacy constraints. Under such constraints, the original multivariate data $X_1,\ldots,X_n \in \mathbb{R}^d$ cannot be directly observed, and all esti
Externí odkaz:
http://arxiv.org/abs/2107.12649
Autor:
Devroye, Luc, Györfi, László
We revisit the problem of the estimation of the differential entropy $H(f)$ of a random vector $X$ in $R^d$ with density $f$, assuming that $H(f)$ exists and is finite. In this note, we study the consistency of the popular nearest neighbor estimate $
Externí odkaz:
http://arxiv.org/abs/2102.12952
In this paper we revisit the classical problem of nonparametric regression, but impose local differential privacy constraints. Under such constraints, the raw data $(X_1,Y_1),\ldots,(X_n,Y_n)$, taking values in $\mathbb{R}^d \times \mathbb{R}$, canno
Externí odkaz:
http://arxiv.org/abs/2011.00216