Bayesian Optimization in AlphaGo

The application of Bayesian methods for seeking the extremum

J Moćkus, V Tiesis, and A Źilinskas · 1978

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Gaussian Processes for Machine Learning

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Whole-history rating: A bayesian rating system for players of time-varying strength

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A tutorial on Bayesian optimization of expensive cost functions, with application to active user modeling and hierarchical reinforcement learning

Original

Eric Brochu, Vlad M Cora, and Nando De Freitas · 2010

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Time management for Monte-Carlo tree search applied to the game of Go

Shih-Chieh Huang, Remi Coulom, and Shun-Shii Lin · 2010

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Practical Bayesian optimization of machine learning algorithms

Jasper Snoek, Hugo Larochelle, and Ryan P. Adams · 2012

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Mastering the game of go with deep neural networks and tree search

David Silver, Aja Huang, Chris J. Maddison, Arthur Guez, Laurent Sifre, George van den Driessche, Julian Schrittwieser, Ioannis Antonoglou, Veda Panneershelvam, Marc Lanctot, Sander Dieleman, Dominik Grewe, John Nham, Nal Kalchbrenner, Ilya Sutskever, Timothy Lillicrap, Madeleine Leach, Koray Kavukcuoglu, Thore Graepel, and Demis Hassabis · 2016

Later among the works it cites.

Mastering the game of go without human knowledge

David Silver, Julian Schrittwieser, Karen Simonyan, Ioannis Antonoglou, Aja Huang, Arthur Guez, Thomas Hubert, Lucas Baker, Matthew Lai, Adrian Bolton, Yutian Chen, Timothy Lillicrap, Fan Hui, Laurent Sifre, George van den Driessche, Thore Graepel, and Demis Hassabis · 2017

Later among the works it cites.

A general reinforcement learning algorithm that masters chess, shogi, and go through self-play

David Silver, Thomas Hubert, Julian Schrittwieser, Ioannis Antonoglou, Matthew Lai, Arthur Guez, Marc Lanctot, Laurent Sifre, Dharshan Kumaran, Thore Graepel, Timothy Lillicrap, Karen Simonyan, and Demis Hassabis · 2018

Closest in time.