# Machine Learning literature

Recommendations for tutorials, surveys and theses.

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)
- An invitation to sequential Monte Carlo samplers by Chenguang Dai, Jeremy Heng, Pierre E. Jacob, Nick Whiteley (2020)
- 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)
- An Invitation to Deep Reinforcement Learning by Bernhard Jaeger, Andreas Geiger (2023)
- Foundations of Vector Retreival by Sebastian Bruch (2024)
- User-friendly Introduction to PAC-Bayes Bounds by Pierre Alquier (2024)

## 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. SchÃ¶n (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,Â 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 togetherâ€¦ by Keith Winstein
- Bayesian or Frequentist, Which Are You? by Michael I. Jordan
- Optimization for Machine Learning I by Elad Hazan
- Denoising as a Building Block for Imaging, Inverse Problems, and Machine Learning by Peyman Milanfar