Machine Learning literature

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)
• Data Compression with Machine Learning by Karen Ullrich (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)
• Flow Matching Guide and Code by Yaron Lipman et. al. (2024)
• Matrix Calculus (for Machine Learning and Beyond) by Paige Bright, Alan Edelman, Steven G. Johnson (2025)

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)

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
• Denoising as a Building Block for Imaging, Inverse Problems, and Machine Learning by Peyman Milanfar
• Multimodal Deep Learning by Douwe Kiela
• Physics of Language Models by Zeyuan Allen-Zhu (Part 2 | Part 3) (2024)