31 lines
1.4 KiB
Markdown
31 lines
1.4 KiB
Markdown
---
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title: "Uncertainty Estimation and Generalization Bounds for Modern Deep Learning (PhD Thesis)"
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source_url: https://arxiv.org/abs/2606.13818
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ingested: 2026-06-17
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sha256: <computed>
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---
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# Uncertainty Estimation and Generalization Bounds for Modern Deep Learning
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**Author:** Luis A. Ortega Andrés — Department of Computer Science, Autonomous University of Madrid
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**Supervisor:** Daniel Hernández Lobato
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**arXiv:** 2606.13818v1 [cs.LG] (2026-06-11) — PhD Thesis
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## Abstract
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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.
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## Key Concepts
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- [[deep-variational-implicit-process|DVIP]]
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- [[variational-linearized-laplace-approximation|VaLLA]]
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- [[fixed-mean-gaussian-process|FMGP]]
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- [[pac-bayesian-bounds|PAC-Bayesian 界]]
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- [[implicit-processes|隐式过程]]
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- [[function-space-modeling|函数空间建模]]
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- [[generalization-bounds|泛化界]]
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- [[double-descent|双下降]]
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- [[deep-gaussian-process|深度高斯过程]]
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