Machine Learning literature

Tutorials

• A modern Bayesian look at the multi-armed bandit - Steven L. Scott (2010)
• A Tutorial on Particle Filtering and Smoothing: Fifteen years later - Arnaud Doucet, Adam M. Johansen (2011)
• Determinantal point processes for machine learning - Alex Kulesza, Ben Taskar (2012)
• A Tutorial on Principal Component Analysis - Jonathan Shlens (2014)
• Expectation propagation as a way of life: A framework for Bayesian inference on partitioned data - Aki Vehtari, Andrew Gelman, Tuomas Sivula, Pasi Jylänki, Dustin Tran, Swupnil Sahai, Paul Blomstedt, John P. Cunningham, David Schiminovich, Christian Robert
• A Tutorial on Fisher Information - Alexander Ly, Maarten Marsman, Josine Verhagen, Raoul Grasman, Eric-Jan Wagenmakers (2017)
• Reinforcement Learning and Control as Probabilistic Inference: Tutorial and Review - Sergey Levine (2018)
• A Tutorial on Bayesian Optimization - Peter I. Frazier (2018)
• A Primer on PAC-Bayesian Learning - Benjamin Guedj (2019)
• Introduction to Multi-Armed Bandits - Aleksandrs Slivkins (2019)
• A Modern Introduction to Online Learning - Francesco Orabona (2019)

Surveys

• Bayesian Reinforcement Learning: A Survey - Mohammad Ghavamzadeh, Shie Mannor, Joelle Pineau, Aviv Tamar
• Elements of Sequential Monte Carlo - Christian A. Naesseth, Fredrik Lindsten, Thomas B. Schön (2019)
• Generalized Variational Inference: Three arguments for deriving new Posteriors - Jeremias Knoblauch, Jack Jewson, Theodoros Damoulas (2019)
• Monte Carlo Gradient Estimation in Machine Learning - Shakir Mohamed, Mihaela Rosca, Michael Figurnov, Andriy Mnih (2019)
• Normalizing Flows for Probabilistic Modeling and Inference - George Papamakarios, Eric Nalisnick, Danilo Jimenez Rezende, Shakir Mohamed, Balaji Lakshminarayanan (2019)
• Computing Bayes: Bayesian Computation from 1763 to the 21st Century - Gael M. Martin, David T. Frazier, Christian P. Robert (2020)

Theses

• Bayesian Learning for Neural Networks by Radford Neal (1995)
• Variational Algorithms for Approximate Bayesian Inference by Matthew J. Beal (2003)
• On the Sample Complexity of Reinforcement Learning by Sham Kakade (2003)
• Advances in Markov chain Monte Carlo methods by Iain Murray (2007)
• Decision making with inference and learning methods by Matthew William Hoffman (2013)
• Efficient Bayesian Active Learning and Matrix Modeling by Neil MT Houlsby (2014)
• Stochastic Gradient MCMC: Algorithms and Applications by Sungjin Ahn (2015)
• Optimizing Expectations: From Deep Reinforcement Learning to Stochastic Computation Graphs by John Schulman (2016)
• On Priors for Bayesian Neural Networks by Eric Nalisnick (2018)

Uncategorized

• Gaussian Identities - Sam Roweis
• A Mathematical Theory of Communication - Claude E. Shannon (1948)
• A Complete Recipe for Stochastic Gradient MCMC - Yi-An Ma, Tianqi Chen, Emily B. Fox (2015)
• A Conceptual Introduction to Hamiltonian Monte Carlo - Michael Betancourt (2017)
• The Permutation Test: A Visual Explanation of Statistical Testing - Jared Wilber (2019)
• Holes in Bayesian Statistics - Andrew Gelman, Yuling Yao (2020)
• Understanding the Neural Tangent Kernel - Rajat Vadiraj Dwaraknath (2020)
• Planning as Inference in Epidemiological Models - Frank Wood, Andrew Warrington, Saeid Naderiparizi, Christian Weilbach, Vaden Masrani, William Harvey, Adam Scibior, Boyan Beronov, Ali Nasseri (2020)
• Information-Theoretic Probing with Minimum Description Length - Elena Voita, Ivan Titov (2020)
• The Illustrated Transformer - Jay Alammar (2018)