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+title: "Uncertainty Estimation and Generalization Bounds for Modern Deep Learning (PhD Thesis)"
+source_url: https://arxiv.org/abs/2606.13818
+ingested: 2026-06-17
+sha256: <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.
+
+## Key Concepts
+
+- [[deep-variational-implicit-process|DVIP]]
+- [[variational-linearized-laplace-approximation|VaLLA]]
+- [[fixed-mean-gaussian-process|FMGP]]
+- [[pac-bayesian-bounds|PAC-Bayesian 界]]
+- [[implicit-processes|隐式过程]]
+- [[function-space-modeling|函数空间建模]]
+- [[generalization-bounds|泛化界]]
+- [[double-descent|双下降]]
+- [[deep-gaussian-process|深度高斯过程]]