University of Cambridge > Talks.cam > Machine Learning Reading Group @ CUED > Bayesian optimization / Gaussian Process Bandits

  Browse A-Z

Log in

Cambridge users (raven)details Other usersdetails No account?details

Information on

Subscribing to talksdetails Finding a talkdetails Adding a talkdetails Disseminating talksdetails Help and Documentationdetails

Bayesian optimization / Gaussian Process Bandits

Add to your list(s) Download to your calendar using vCal

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.

Tell a friend about this talk:

This talk is included in these lists:

Note that ex-directory lists are not shown.

Other lists

Mineral Sciences Seminars Type the title of a new list here sleg

Other talks

Reparameterizing Bayesian PCA using Householder transformations to break the rotational symmetry. Democracy Requires Organized Collective Power PA Consulting: Applications of AR/VR On the interactions between our industrial system and the natural system Captive polar bears: politics, (re)production, and performance
 

© 2006-2024 Talks.cam, University of Cambridge. Contact Us | Help and Documentation | Privacy and Publicity