Multiphysics simulations: Challenges and opportunities. - David E. Keyes; Lois C. McInnes; Carol S. Woodward; William Gropp; Eric Myra; Michael Pernice; John B. Bell; Jed Brown; Alain Clo; Jeffrey M. Connors; Emil M. Constantinescu; Donald J. Estep; et al. (Int. J. High Perform. Comput. Appl., 2013)

Sparse Matrix-Matrix Products Executed Through Coloring. - Michael McCourt; Barry Smith 002; Hong Zhang 006 (SIAM J. Matrix Anal. Appl., 2015)

A Strategy for Ranking Optimization Methods using Multiple Criteria. - Ian Dewancker; Michael McCourt; Scott Clark; Patrick Hayes; Alexandra Johnson; George Ke (AutoML@ICML, 2016)

Bayesian Optimization for Machine Learning : A Practical Guidebook. - Ian Dewancker; Michael McCourt; Scott Clark (CoRR, 2016)

A Stratified Analysis of Bayesian Optimization Methods. - Ian Dewancker; Michael McCourt; Scott Clark; Patrick Hayes; Alexandra Johnson; George Ke (CoRR, 2016)

Evaluation System for a Bayesian Optimization Service. - Ian Dewancker; Michael McCourt; Scott Clark; Patrick Hayes; Alexandra Johnson; George Ke (CoRR, 2016)

Preemptive Termination of Suggestions during Sequential Kriging Optimization of a Brain Activity Reconstruction Simulation. - Michael McCourt; Ian Dewancker; Salvatore Ganci (CoRR, 2016)

Robust Bayesian Optimization with Student-t Likelihood. - Ruben Martinez-Cantin; Michael McCourt; Kevin Tee (CoRR, 2017)

Active Preference Learning for Personalized Portfolio Construction. - Kevin Tee; Michael McCourt; Ruben Martinez-Cantin; Ian Dewancker; Frank Liu 006 (CoRR, 2017)

Practical Bayesian optimization in the presence of outliers. - Ruben Martinez-Cantin; Kevin Tee; Michael McCourt (AISTATS, 2018)

Sequential Preference-Based Optimization. - Ian Dewancker; Jakob Bauer; Michael McCourt (CoRR, 2018)

Orchestrate: Infrastructure for Enabling Parallelism during Hyperparameter Optimization. - Alexandra Johnson; Michael McCourt (CoRR, 2018)

Bayesian Optimization over Sets. - Jungtaek Kim 001; Michael McCourt; Tackgeun You; Saehoon Kim; Seungjin Choi (CoRR, 2019)

Sampling Humans for Optimizing Preferences in Coloring Artwork. - Michael McCourt; Ian Dewancker (CoRR, 2019)

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; Juha Kiili; Eero Laaksonen; Zhen Xu 007; Isabelle Guyon (NeurIPS (Competition and Demos), 2020)

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

On variable and random shape Gaussian interpolations. - Sung Nok Chiu; Leevan Ling; Michael McCourt (Appl. Math. Comput., 2020)

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

Bayesian optimization with approximate set kernels. - Jungtaek Kim 001; Michael McCourt; Tackgeun You; Saehoon Kim; Seungjin Choi (Mach. Learn., 2021)

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

Bridging Offline and Online Experimentation: Constraint Active Search for Deployed Performance Optimization. - Junpei Komiyama; Gustavo Malkomes; Bolong Cheng; Michael McCourt (Trans. Mach. Learn. Res., 2022)