Machine Learning literature

Recommendations for tutorials, surveys and theses.

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Alse see Topics in Bayesian Machine Learning.

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)
• An Introduction to Matrix Concentration Inequalities by Joel A. Tropp (2015)
• 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 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)
• A Gentle Introduction to Conformal Prediction and Distribution-Free Uncertainty Quantification - Anastasios N. Angelopoulos and Stephen Bates (2022)
• Tutorial on amortized optimization by Brandon Amos (2022)
• A First Course in Causal Inference by Peng Ding (2023)

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)
• 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)

• Randomized Numerical Linear Algebra: Foundations & Algorithms by Per-Gunnar Martinsson, Joel Tropp (2020)
• Machine Learning with a Reject Option: A survey by Kilian Hendrickx, Lorenzo Perini, Dries Van der Plas, Wannes Meert, Jesse Davis (2021)

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)

Talks

• Probability Divergences and Generative Models - Arthur Gretton
• And the Bayesians and the frequentists shall lie down together... by Keith Winstein
• Bayesian or Frequentist, Which Are You? by Michael I. Jordan
• Optimization for Machine Learning I by Elad Hazan