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Eric Lee

2 accepted papers

2020

Efficient Rollout Strategies for Bayesian Optimization

UAI 2020poster

Bayesian optimization (BO) is a class of sample-efficient global optimization methods, where a probabilistic model conditioned on previous observations is used to determine future evaluations via the optimization of an acquisition function. Most acquisition functions are myopic, meaning that they on…

2018

Scaling Gaussian Process Regression with Derivatives

NeurIPS 2018poster

Gaussian processes (GPs) with derivatives are useful in many applications, including Bayesian optimization, implicit surface reconstruction, and terrain reconstruction. Fitting a GP to function values and derivatives at $n$ points in $d$ dimensions requires linear solves and log determinants with an…