165 citations · 301 across the 19 of their papers we have counts for
19 papers
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…
Bayesian Optimization is Superior to Random Search for Machine Learning Hyperparameter Tuning: Analysis of the Black-Box Optimization Challenge 2020
Ryan Turner, David Eriksson, Michael McCourt +4
This paper presents the results and insights from the black-box optimization (BBO) challenge at NeurIPS 2020 which ran from July-October, 2020. The challenge emphasized the importa…
Quasi-Monte Carlo Software
Sou-Cheng T. Choi, Fred J. Hickernell, R. Jagadeeswaran +2
Practitioners wishing to experience the efficiency gains from using low discrepancy sequences need correct, robust, well-written software. This article, based on our MCQMC 2020 tut…
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…
Sampling Humans for Optimizing Preferences in Coloring Artwork
Michael McCourt, Ian Dewancker
Many circumstances of practical importance have performance or success metrics which exist implicitly---in the eye of the beholder, so to speak. Tuning aspects of such problems req…
Bayesian Optimization with Approximate Set Kernels
Jungtaek Kim, Michael McCourt, Tackgeun You +2
We propose a practical Bayesian optimization method over sets, to minimize a black-box function that takes a set as a single input. Because set inputs are permutation-invariant, tr…