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title, source_url, ingested, sha256
| title | source_url | ingested | sha256 |
|---|---|---|---|
| Uncertainty Estimation and Generalization Bounds for Modern Deep Learning (PhD Thesis) | https://arxiv.org/abs/2606.13818 | 2026-06-17 | <computed> |
Uncertainty Estimation and Generalization Bounds for Modern Deep Learning
Author: Luis A. Ortega Andrés — Department of Computer Science, Autonomous University of Madrid
Supervisor: Daniel Hernández Lobato
arXiv: 2606.13818v1 [cs.LG] (2026-06-11) — PhD Thesis
Abstract
Investigates how Bayesian principles can deepen understanding of modern deep learning systems. On the methodological side, introduces DVIP (Deep Variational Implicit Process), VaLLA (Variational Linearized Laplace Approximation), and FMGP (Fixed-Mean Gaussian Process). On the theoretical side, develops a unified PAC-Bayesian/large-deviation framework connecting diversity, smoothness, and stochasticity as mechanisms for generalization. Provides quantitative distribution-dependent explanation for double-descent.