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Machine Learning literature
Date written
Jul 5, 2020
Date updated
Last updated: Nov 13, 2020
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ML & Stats
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math
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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)
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
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)
The Permutation Test
:
A Visual Explanation of Statistical Testing
- Jared Wilber (2019)
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)