42 citations · 85 across the 8 of their papers we have counts for
8 papers
SigOpt Mulch: An Intelligent System for AutoML of Gradient Boosted Trees
Aleksei Sorokin, Xinran Zhu, Eric Hans Lee +1
Gradient boosted trees (GBTs) are ubiquitous models used by researchers, machine learning (ML) practitioners, and data scientists because of their robust performance, interpretable…
Achieving Diversity in Objective Space for Sample-efficient Search of Multiobjective Optimization Problems
Eric Hans Lee, Bolong Cheng, Michael McCourt
Efficiently solving multi-objective optimization problems for simulation optimization of important scientific and engineering applications such as materials design is becoming an i…
Scalable Bayesian Transformed Gaussian Processes
Xinran Zhu, Leo Huang, Cameron Ibrahim +2
The Bayesian transformed Gaussian process (BTG) model, proposed by Kedem and Oliviera, is a fully Bayesian counterpart to the warped Gaussian process (WGP) and marginalizes out a j…
A Nonmyopic Approach to Cost-Constrained Bayesian Optimization
Eric Hans Lee, David Eriksson, Valerio Perrone +1
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…
Cost-aware Bayesian Optimization
Eric Hans Lee, Valerio Perrone, Cedric Archambeau +1
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 B…
Efficient Rollout Strategies for Bayesian Optimization
Eric Hans Lee, David Eriksson, Bolong Cheng +2
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…