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Bayesian optimization / Gaussian Process Bandits

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If you have a question about this talk, please contact Elre Oldewage.

Sequential optimization is one of the fastest growing areas of machine learning. In this presentation we deep dive into sequential optimization based on Gaussian process models (aka Bayesian optimization). We will take a look at the analysis of popular algorithms such as UCB and Thompson sampling and wrap up with an overview of recent results and open problems.

Recommended reading:

Srinivas et al. 2010 (https://arxiv.org/abs/0912.3995), recipient of ICML 2020 Test of Time award Chowdhury and Gopalan 2017 (https://arxiv.org/abs/1704.00445) Vakili et al. 2020 (https://arxiv.org/abs/2009.06966) Wilson et al. 2020 (https://arxiv.org/abs/2002.09309)

This talk is part of the Machine Learning Reading Group @ CUED series.

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