Session VIII (May 16, 3:30pm-5:00pm): Design Issues in Uncertainty Quantification, organized by Robert Gramacy 

• Title: Scalable Bayesian Optimization for Noisy Problems 

• Speaker: Mickael Binois, INRIA

• Abstract: Stochastic simulators may exhibit low signal-to-noise ratios, hence requiring large budget of evaluations to find accurate solutions. For deterministic and high signal to noise ratio problems, Bayesian optimization is a sample efficient technique. In particular, recent works have shown the interest of combining it with trust region methods to help convergence. Here we revisit this combination for noisier problems, when the number of evaluations may reach millions or even billions. We build upon local heteroscedastic Gaussian process modeling to adapt to both noise and nonstationarity, while adapting the degree of replication at each design to scale with respect to the number of observations. In particular, our method is able to identify solutions with an arbitrary precision. We illustrate our method on several synthetic test cases, as well as some more realistic ones.