arXiv Machine Learning
Gaussian Approximation of Besov Functions for Logistic Classification on Manifolds
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Low-Dimensional Embeddings for Gaussian Kernels on Manifolds
arXiv:2609.15179v1 Announce Type: cross Abstract: The Gaussian kernel is a widely used similarity measure underlying kernel methods such as kernel PCA and spectral clustering, but computing Gaussian...
arXiv Machine Learning
Jul 27
Approximation and learning of anisotropic and mixed smooth functions by deep ReLU neural networks
arXiv:2605. 31152v2 Announce Type: replace-cross Abstract: This paper studies how efficiently deep ReLU neural networks can approximate and learn smooth functions.
arXiv Machine Learning
Oct 15
A proximal gradient algorithm for composite log-concave sampling
arXiv:2605. 12461v2 Announce Type: replace-cross Abstract: We propose an algorithm to sample from composite log-concave distributions over $\mathbb{R}^d$, i.
Hugging Face Trending Papers
Aug 25
The Sharp Tail of Uniform Stability
Uniform stability controls how much one training example can change the loss at any test point. A new logarithmic-free upper bound shows that a $γ$-uniformly stable algorithm with loss in $[0,L]$ has...
arXiv Machine Learning
Sep 22
Toscani-Fourier Distance on Probability Measures: Wasserstein Control, Topological Equivalence on Model Classes, and Duality
arXiv:2609.23163v1 Announce Type: cross Abstract: Comparing probability measures in machine learning trades transport geometry against computational cost: Wasserstein distances encode the geometry of...
arXiv Machine Learning
Aug 6
The Sample Complexity of Distributionally Robust PAC Learning under Cressie--Read Divergences
arXiv:2608. 04686v1 Announce Type: new Abstract: We study distributionally robust PAC learning for the $0$--$1$-loss, where adversarial perturbations of the data distribution are constrained by a Cressie--Read divergence of order $k>1$ and radius $\rho\geq 0$.