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ORCIDMichael McCourt
Person information
- affiliation: Distributional, USA
- affiliation (former): SigOpt, San Francisco, USA
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Journal Articles
- 2022
[j5]Junpei Komiyama, Gustavo Malkomes, Bolong Cheng, Michael McCourt:
Bridging Offline and Online Experimentation: Constraint Active Search for Deployed Performance Optimization. Trans. Mach. Learn. Res. 2022 (2022)- 2021
- [j4]Jungtaek Kim
, Michael McCourt, Tackgeun You, Saehoon Kim, Seungjin Choi:
Bayesian optimization with approximate set kernels. Mach. Learn. 110(5): 857-879 (2021) - 2020
- [j3]Sung Nok Chiu, Leevan Ling, Michael McCourt:
On variable and random shape Gaussian interpolations. Appl. Math. Comput. 377: 125159 (2020) - 2015
- [j2]Michael McCourt, Barry Smith, Hong Zhang:
Sparse Matrix-Matrix Products Executed Through Coloring. SIAM J. Matrix Anal. Appl. 36(1): 90-109 (2015) - 2013
- [j1]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, Kate Evans, Charbel Farhat, Ammar Hakim, Glenn Hammond, Glen Hansen, Judith Hill, Tobin Isaac, Xiangmin Jiao, Kirk E. Jordan, Dinesh K. Kaushik, Efthimios Kaxiras, Alice E. Koniges, Kihwan Lee, Aaron Lott, Qiming Lu, John Magerlein, Reed Maxwell, Michael McCourt, Miriam Mehl, Roger P. Pawlowski, Amanda Peters Randles, Daniel R. Reynolds, Beatrice Riviere, Ulrich Rüde, Timothy D. Scheibe, John N. Shadid, Brendan Sheehan, Mark S. Shephard, Andrew R. Siegel, Barry Smith, Xianzhu Tang, Cian Wilson, Barbara I. Wohlmuth:
Multiphysics simulations: Challenges and opportunities. Int. J. High Perform. Comput. Appl. 27(1): 4-83 (2013)
Conference and Workshop Papers
- 2022
- [c6]Eric Hans Lee, Bolong Cheng, Michael McCourt:
Achieving Diversity in Objective Space for Sample-Efficient Search of Multiobjective Optimization Problems. WSC 2022: 3146-3157 - 2021
- [c5]Gustavo Malkomes, Bolong Cheng, Eric Hans Lee, Mike Mccourt:
Beyond the Pareto Efficient Frontier: Constraint Active Search for Multiobjective Experimental Design. ICML 2021: 7423-7434 - 2020
- [c4]Ryan Turner, David Eriksson, Michael McCourt, Juha Kiili, Eero Laaksonen, Zhen Xu, Isabelle Guyon:
Bayesian Optimization is Superior to Random Search for Machine Learning Hyperparameter Tuning: Analysis of the Black-Box Optimization Challenge 2020. NeurIPS (Competition and Demos) 2020: 3-26 - [c3]Eric Hans Lee, David Eriksson, David Bindel, Bolong Cheng, Mike Mccourt:
Efficient Rollout Strategies for Bayesian Optimization. UAI 2020: 260-269 - 2018
- [c2]Ruben Martinez-Cantin, Kevin Tee, Michael McCourt:
Practical Bayesian optimization in the presence of outliers. AISTATS 2018: 1722-1731 - 2016
- [c1]Ian Dewancker, Michael McCourt, Scott Clark, Patrick Hayes, Alexandra Johnson, George Ke:
A Strategy for Ranking Optimization Methods using Multiple Criteria. AutoML@ICML 2016: 11-20
Informal and Other Publications
- 2023
- [i14]Eric Hans Lee, Bolong Cheng, Michael McCourt:
Achieving Diversity in Objective Space for Sample-efficient Search of Multiobjective Optimization Problems. CoRR abs/2306.13780 (2023) - 2021
- [i13]Ryan Turner, David Eriksson, Michael McCourt, Juha Kiili, Eero Laaksonen, Zhen Xu, Isabelle Guyon:
Bayesian Optimization is Superior to Random Search for Machine Learning Hyperparameter Tuning: Analysis of the Black-Box Optimization Challenge 2020. CoRR abs/2104.10201 (2021) - 2020
- [i12]Eric Hans Lee, David Eriksson, Bolong Cheng, Michael McCourt, David Bindel:
Efficient Rollout Strategies for Bayesian Optimization. CoRR abs/2002.10539 (2020) - 2019
- [i11]Jungtaek Kim, Michael McCourt, Tackgeun You, Saehoon Kim, Seungjin Choi:
Bayesian Optimization over Sets. CoRR abs/1905.09780 (2019) - [i10]Michael McCourt, Ian Dewancker:
Sampling Humans for Optimizing Preferences in Coloring Artwork. CoRR abs/1906.03813 (2019) - 2018
- [i9]Ian Dewancker, Jakob Bauer, Michael McCourt:
Sequential Preference-Based Optimization. CoRR abs/1801.02788 (2018) - [i8]Alexandra Johnson, Michael McCourt:
Orchestrate: Infrastructure for Enabling Parallelism during Hyperparameter Optimization. CoRR abs/1812.07751 (2018) - 2017
- [i7]Ruben Martinez-Cantin, Michael McCourt, Kevin Tee:
Robust Bayesian Optimization with Student-t Likelihood. CoRR abs/1707.05729 (2017) - [i6]Kevin Tee, Michael McCourt, Ruben Martinez-Cantin, Ian Dewancker, Frank Liu:
Active Preference Learning for Personalized Portfolio Construction. CoRR abs/1708.07567 (2017) - [i5]Ruben Martinez-Cantin, Kevin Tee, Michael McCourt:
Practical Bayesian optimization in the presence of outliers. CoRR abs/1712.04567 (2017) - 2016
- [i4]Ian Dewancker, Michael McCourt, Scott Clark, Patrick Hayes, Alexandra Johnson, George Ke:
A Stratified Analysis of Bayesian Optimization Methods. CoRR abs/1603.09441 (2016) - [i3]Ian Dewancker, Michael McCourt, Scott Clark, Patrick Hayes, Alexandra Johnson, George Ke:
Evaluation System for a Bayesian Optimization Service. CoRR abs/1605.06170 (2016) - [i2]Michael McCourt, Ian Dewancker, Salvatore Ganci:
Preemptive Termination of Suggestions during Sequential Kriging Optimization of a Brain Activity Reconstruction Simulation. CoRR abs/1612.04451 (2016) - [i1]Ian Dewancker, Michael McCourt, Scott Clark:
Bayesian Optimization for Machine Learning : A Practical Guidebook. CoRR abs/1612.04858 (2016)
Coauthor Index
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