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pro vyhledávání: '"Chhor, Julien"'
We consider testing the goodness-of-fit of a distribution against alternatives separated in sup norm. We study the twin settings of Poisson-generated count data with a large number of categories and high-dimensional multinomials. In previous studies
Externí odkaz:
http://arxiv.org/abs/2409.08871
We study the problem of bivariate discrete or continuous probability density estimation under low-rank constraints.For discrete distributions, we assume that the two-dimensional array to estimate is a low-rank probability matrix. In the continuous ca
Externí odkaz:
http://arxiv.org/abs/2404.17209
Given a heterogeneous Gaussian sequence model with unknown mean $\theta \in \mathbb R^d$ and known covariance matrix $\Sigma = \operatorname{diag}(\sigma_1^2,\dots, \sigma_d^2)$, we study the signal detection problem against sparse alternatives, for
Externí odkaz:
http://arxiv.org/abs/2211.08580
In the nonparametric regression setting, we construct an estimator which is a continuous function interpolating the data points with high probability, while attaining minimax optimal rates under mean squared risk on the scale of H\"older classes adap
Externí odkaz:
http://arxiv.org/abs/2206.13347
Autor:
Chhor, Julien, Sentenac, Flore
Although robust learning and local differential privacy are both widely studied fields of research, combining the two settings is just starting to be explored. We consider the problem of estimating a discrete distribution in total variation from $n$
Externí odkaz:
http://arxiv.org/abs/2202.06825
Autor:
Chhor, Julien, Carpentier, Alexandra
We consider the goodness-of fit testing problem for H\"older smooth densities over $\mathbb{R}^d$: given $n$ iid observations with unknown density $p$ and given a known density $p_0$, we investigate how large $\rho$ should be to distinguish, with hig
Externí odkaz:
http://arxiv.org/abs/2109.04346
Akademický článek
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Autor:
Chhor, Julien, Carpentier, Alexandra
Publikováno v:
Mathematical Statistics & Learning; 2022, Vol. 5 Issue 1/2, p1-54, 54p
