Bayesian Optimization

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• Pawel Budzianowiski; Brian Trippe
• Thursday 24 November 2016, 13:30-15:00
• Engineering Department, CBL Room 438.

If you have a question about this talk, please contact Yingzhen Li.

Bayesian optimization is a framework for performing optimization of black box functions, which is particularly useful when function evaluations are expensive and the space of possible solutions is large. This talk is roughly divided into two parts; first we give an overview of Bayesian optimization, introducing the approach as well as common function models and acquisition functions. In the second half of the talk, we highlight some of the many recent advances in the field and discuss open problems. Namely, we will discuss problems in multitask Bayesian optimization, a recent method of modeling a function with Bayesian neural networks, and connections with active learning. Reading: These will be covered in the talk:

A Recent Review on Bayesian Optimization

Bayesian Optimization with Robust Bayesian Neural Networks (to appear at NIPS 2016 )

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

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