Readings and Resources
Andrew Gordon Wilson
Veroli OBA Summer School, 2022
https://cims.nyu.edu/~andrewgw


(1) Bayesian Deep Learning and a Probabilistic Perspective of Generalization. https://arxiv.org/abs/2002.08791

(2) Information Theory, Inference, and Learning Algorithms, Chapter 28. https://www.inference.org.uk/itprnn/book.html

(3) Loss Surfaces, Mode Connectivity, and Fast Ensembling of DNNs. https://arxiv.org/abs/1802.10026

(4) A Simple Baseline for Bayesian Uncertainty in Deep Learning. https://arxiv.org/abs/1902.02476

(5) Covariance Kernels for Fast Automatic Pattern Discovery and Extrapolation with Gaussian Processes, Chapters 1-2. http://www.cs.cmu.edu/~andrewgw/andrewgwthesis.pdf

(6) Pattern Recognition and Machine Learning, Chapters 1-3. https://www.microsoft.com/en-us/research/uploads/prod/2006/01/Bishop-Pattern-Recognition-and-Machine-Learning-2006.pdf

(7) Bayesian Model Selection, the Marginal Likelihood, and Generalization. https://arxiv.org/abs/2202.11678

(8) ICML 2020 Tutorial on Bayesian Deep Learning. https://www.youtube.com/watch?v=E1qhGw8QxqY

(9) Gaussian Processes for Machine Learning, Chapter 2. https://gaussianprocess.org/gpml/chapters/

(10) Loss Surface Simplexes for Mode Connecting Volumes and Fast Ensembling. https://arxiv.org/abs/2102.13042

(11) GPyTorch (gpytorch.ai) and BoTorch (botorch.ai)



