Zobrazeno 1 - 10
of 631
pro vyhledávání: '"62C20"'
Autor:
García, Jonathan, Petersen, Philipp
We prove that a classifier with a Barron-regular decision boundary can be approximated with a rate of high polynomial degree by ReLU neural networks with three hidden layers when a margin condition is assumed. In particular, for strong margin conditi
Externí odkaz:
http://arxiv.org/abs/2412.07312
We propose a general transfer learning framework for clustering given a main dataset and an auxiliary one about the same subjects. The two datasets may reflect similar but different latent grouping structures of the subjects. We propose an adaptive t
Externí odkaz:
http://arxiv.org/abs/2410.21263
We consider nonparametric estimation of the distribution function $F$ of squared sphere radii in the classical Wicksell problem. Under smoothness conditions on $F$ in a neighborhood of $x$, in \cite{21} it is shown that the Isotonic Inverse Estimator
Externí odkaz:
http://arxiv.org/abs/2410.14263
We study the effects of missingness on the estimation of population parameters. Moving beyond restrictive missing completely at random (MCAR) assumptions, we first formulate a missing data analogue of Huber's arbitrary $\epsilon$-contamination model.
Externí odkaz:
http://arxiv.org/abs/2410.10704
We study the problem of approximating and estimating classification functions that have their decision boundary in the $RBV^2$ space. Functions of $RBV^2$ type arise naturally as solutions of regularized neural network learning problems and neural ne
Externí odkaz:
http://arxiv.org/abs/2409.17991
We link conditional generative modelling to quantile regression. We propose a suitable loss function and derive minimax convergence rates for the associated risk under smoothness assumptions imposed on the conditional distribution. To establish the l
Externí odkaz:
http://arxiv.org/abs/2409.04231
Motivated by crowdsourcing, we consider a problem where we partially observe the correctness of the answers of $n$ experts on $d$ questions. In this paper, we assume that both the experts and the questions can be ordered, namely that the matrix $M$ c
Externí odkaz:
http://arxiv.org/abs/2408.15356
The minimax risk is often considered as a gold standard against which we can compare specific statistical procedures. Nevertheless, as has been observed recently in robust and heavy-tailed estimation problems, the inherent reduction of the (random) l
Externí odkaz:
http://arxiv.org/abs/2406.13447
Autor:
Maruyama, Yuzo, Matsuda, Takeru
This is a follow-up paper of Polson and Scott (2012, Bayesian Analysis), which claimed that the half-Cauchy prior is a sensible default prior for a scale parameter in hierarchical models. For estimation of a p-variate normal mean under the quadratic
Externí odkaz:
http://arxiv.org/abs/2406.08892
This paper studies federated learning for nonparametric regression in the context of distributed samples across different servers, each adhering to distinct differential privacy constraints. The setting we consider is heterogeneous, encompassing both
Externí odkaz:
http://arxiv.org/abs/2406.06755