Eric Hans Lee
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Achieving Diversity in Objective Space for Sample-Efficient Search of Multiobjective Optimization Problems Open
Efficiently solving multi-objective optimization problems for simulation\noptimization of important scientific and engineering applications such as\nmaterials design is becoming an increasingly important research topic. This is\ndue largel…
View article: A Nonmyopic Approach to Cost-Constrained Bayesian Optimization
A Nonmyopic Approach to Cost-Constrained Bayesian Optimization Open
Bayesian optimization (BO) is a popular method for optimizing expensive-to-evaluate black-box functions. BO budgets are typically given in iterations, which implicitly assumes each evaluation has the same cost. In fact, in many BO applicat…
View article: Cost-aware Bayesian Optimization
Cost-aware Bayesian Optimization Open
Bayesian optimization (BO) is a class of global optimization algorithms, suitable for minimizing an expensive objective function in as few function evaluations as possible. While BO budgets are typically given in iterations, this implicitl…
View article: Efficient Rollout Strategies for Bayesian Optimization
Efficient Rollout Strategies for Bayesian Optimization Open
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…
Scaling Gaussian Process Regression with Derivatives Open
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 $d$ d…
Scaling Gaussian Process Regression with Derivatives Open
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 $d$ d…