You are all cordially invited to the AMLab seminar on **Tuesday February 28** at 16:00 in C3.163, where **ChangYong Oh** will give a talk titled “**High dimensional Bayesian Optimization**”. Afterwards there are the usual drinks and snacks!

**Abstract**: Bayesian optimization has been successful in many hyper-parameter optimization problems and reinforcement learning problems. Still, there are many obstacles which prevent it from being extensively applied. Among many obstacles, we focused on the methods for high dimensional spaces. In order to resolve the difficulties of high dimensional Bayesian optimization problems, we devised a principled method to reduce the predictive variance of Gaussian process and other assistive methods for its successful application.

Firstly, brief explanation about general Bayesian optimization will be given. Secondly, I will explain the sources that make high dimensional problems harder, namely, ‘boundary effect’ and ‘hollow ball problem’. Thirdly, I will propose solutions to those problem, so-called, ‘variance reduction’ and ‘adaptive search region’.