Zobrazeno 1 - 10
of 2 106
pro vyhledávání: '"62g08"'
Autor:
Wu, Yantao, Maggioni, Mauro
Several statistical models for regression of a function $F$ on $\mathbb{R}^d$ without the statistical and computational curse of dimensionality exist, for example by imposing and exploiting geometric assumptions on the distribution of the data (e.g.
Externí odkaz:
http://arxiv.org/abs/2411.09686
Models based on recursive partitioning such as decision trees and their ensembles are popular for high-dimensional regression as they can potentially avoid the curse of dimensionality. Because empirical risk minimization (ERM) is computationally infe
Externí odkaz:
http://arxiv.org/abs/2411.04394
Autor:
Stepanova, Natalia, Turcicova, Marie
We observe an unknown regression function of $d$ variables $f(\boldsymbol{t})$, $\boldsymbol{t} \in[0,1]^d$, in the Gaussian white noise model of intensity $\varepsilon>0$. We assume that the function $f$ is regular and that it is a sum of $k$-variat
Externí odkaz:
http://arxiv.org/abs/2411.04320
Graph Convolutional Networks (GCNs) have become a pivotal method in machine learning for modeling functions over graphs. Despite their widespread success across various applications, their statistical properties (e.g. consistency, convergence rates)
Externí odkaz:
http://arxiv.org/abs/2410.20068
Conditional quantile treatment effect (CQTE) can provide insight into the effect of a treatment beyond the conditional average treatment effect (CATE). This ability to provide information over multiple quantiles of the response makes CQTE especially
Externí odkaz:
http://arxiv.org/abs/2410.12454
Autor:
Szabo, Botond, Zhu, Yichen
Gaussian Processes (GPs) are widely used to model dependency in spatial statistics and machine learning, yet the exact computation suffers an intractable time complexity of $O(n^3)$. Vecchia approximation allows scalable Bayesian inference of GPs in
Externí odkaz:
http://arxiv.org/abs/2410.10649
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