Zobrazeno 1 - 10
of 2 097
pro vyhledávání: '"62G08"'
Autor:
Ou, Weigutian, Bölcskei, Helmut
Covering numbers of families of (deep) ReLU networks have been used to characterize their approximation-theoretic performance, upper-bound the prediction error they incur in nonparametric regression, and quantify their classification capacity. These
Externí odkaz:
http://arxiv.org/abs/2410.06378
Autor:
Girard, Stéphane, Pakzad, Cambyse
We propose an extreme dimension reduction method extending the Extreme-PLS approach to the case where the covariate lies in a possibly infinite-dimensional Hilbert space. The ideas are partly borrowed from both Partial Least-Squares and Sliced Invers
Externí odkaz:
http://arxiv.org/abs/2410.05517
Autor:
Luo, Hengrui, Li, Meng
Tree-based methods are powerful nonparametric techniques in statistics and machine learning. However, their effectiveness, particularly in finite-sample settings, is not fully understood. Recent applications have revealed their surprising ability to
Externí odkaz:
http://arxiv.org/abs/2410.02623
Autor:
Maturo, Fabrizio, Porreca, Annamaria
The positioning of this research falls within the scalar-on-function classification literature, a field of significant interest across various domains, particularly in statistics, mathematics, and computer science. This study introduces an advanced m
Externí odkaz:
http://arxiv.org/abs/2409.17804
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
Building on recent studies of large-dimensional kernel regression, particularly those involving inner product kernels on the sphere $\mathbb{S}^{d}$, we investigate the Pinsker bound for inner product kernel regression in such settings. Specifically,
Externí odkaz:
http://arxiv.org/abs/2409.00915
Autor:
Maturo, Fabrizio, Porreca, Annamaria
The advent of big data has raised significant challenges in analysing high-dimensional datasets across various domains such as medicine, ecology, and economics. Functional Data Analysis (FDA) has proven to be a robust framework for addressing these c
Externí odkaz:
http://arxiv.org/abs/2408.12288
Autor:
Genest, Christian, Ouimet, Frédéric
This paper introduces a local linear smoother for regression surfaces on the simplex. The estimator solves a least-squares regression problem weighted by a locally adaptive Dirichlet kernel, ensuring excellent boundary properties. Asymptotic results
Externí odkaz:
http://arxiv.org/abs/2408.07209
We proposed the tensor-input tree (TT) method for scalar-on-tensor and tensor-on-tensor regression problems. We first address scalar-on-tensor problem by proposing scalar-output regression tree models whose input variable are tensors (i.e., multi-way
Externí odkaz:
http://arxiv.org/abs/2408.01926
Autor:
Petersen, Alexander
Estimation of the mean and covariance parameters for functional data is a critical task, with local linear smoothing being a popular choice. In recent years, many scientific domains are producing multivariate functional data for which $p$, the number
Externí odkaz:
http://arxiv.org/abs/2408.01326