Scaling Gaussian Process Regression with Derivatives. - David Eriksson; Kun Dong; Eric Hans Lee; David Bindel; Andrew Gordon Wilson (NeurIPS, 2018)

Efficient Rollout Strategies for Bayesian Optimization. - Eric Hans Lee; David Eriksson; David Bindel; Bolong Cheng; Mike Mccourt (UAI, 2020)

Cost-aware Bayesian Optimization. - Eric Hans Lee; Valerio Perrone; Cédric Archambeau; Matthias W. Seeger (CoRR, 2020)

Beyond the Pareto Efficient Frontier: Constraint Active Search for Multiobjective Experimental Design. - Gustavo Malkomes; Bolong Cheng; Eric Hans Lee; Mike Mccourt (ICML, 2021)

A Nonmyopic Approach to Cost-Constrained Bayesian Optimization. - Eric Hans Lee; David Eriksson; Valerio Perrone; Matthias W. Seeger (UAI, 2021)

Achieving Diversity in Objective Space for Sample-Efficient Search of Multiobjective Optimization Problems. - Eric Hans Lee; Bolong Cheng; Michael McCourt (WSC, 2022)

Scalable Bayesian Transformed Gaussian Processes. - Xinran Zhu; Leo Huang; Cameron Ibrahim; Eric Hans Lee; David Bindel (CoRR, 2022)

SigOpt Mulch: An intelligent system for AutoML of gradient boosted trees. - Aleksei G. Sorokin; Xinran Zhu; Eric Hans Lee; Bolong Cheng (Knowl. Based Syst., 2023)

Bayesian Transformed Gaussian Processes. - Xinran Zhu; Leo Huang; Eric Hans Lee; Cameron Alexander Ibrahim; David Bindel (Trans. Mach. Learn. Res., 2023)