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Michael McCourt
Person information
- affiliation: Distributional, USA
- affiliation (former): SigOpt, San Francisco, USA
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2020 – today
- 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) - 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) - [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
- [j4]Jungtaek Kim
, Michael McCourt, Tackgeun You, Saehoon Kim, Seungjin Choi:
Bayesian optimization with approximate set kernels. Mach. Learn. 110(5): 857-879 (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 - [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
- [j3]Sung Nok Chiu, Leevan Ling, Michael McCourt:
On variable and random shape Gaussian interpolations. Appl. Math. Comput. 377: 125159 (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 - [i12]Eric Hans Lee, David Eriksson, Bolong Cheng, Michael McCourt, David Bindel:
Efficient Rollout Strategies for Bayesian Optimization. CoRR abs/2002.10539 (2020)
2010 – 2019
- 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
- [c2]Ruben Martinez-Cantin, Kevin Tee, Michael McCourt:
Practical Bayesian optimization in the presence of outliers. AISTATS 2018: 1722-1731 - [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
- [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 - [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) - 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
Multiphysics simulations: Challenges and opportunities. Int. J. High Perform. Comput. Appl. 27(1): 4-83 (2013)
Coauthor Index
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