| Multiphysics simulations: Challenges and opportunities DE Keyes, LC McInnes, C Woodward, W Gropp, E Myra, M Pernice, J Bell, ... The International Journal of High Performance Computing Applications 27 (1 …, 2013 | 500 | 2013 |
| Kernel-based approximation methods using Matlab GE Fasshauer, MJ McCourt World Scientific Publishing Company, 2015 | 447 | 2015 |
| Bayesian optimization is superior to random search for machine learning hyperparameter tuning: Analysis of the black-box optimization challenge 2020 R Turner, D Eriksson, M McCourt, J Kiili, E Laaksonen, Z Xu, I Guyon Proceedings of the NeurIPS 2020 Competition and Demonstration Track 133, 3-26, 2021 | 316 | 2021 |
| Stable evaluation of Gaussian radial basis function interpolants GE Fasshauer, MJ McCourt SIAM Journal on Scientific Computing 34 (2), A737-A762, 2012 | 314 | 2012 |
| Bayesian optimization for machine learning: A practical guidebook I Dewancker, M McCourt, S Clark arXiv preprint arXiv:1612.04858, 2016 | 93 | 2016 |
| Bayesian optimization primer I Dewancker, M McCourt, S Clark URL https://app. sigopt. com/static/pdf/SigOpt_ Bayesian_Optimization_Primer …, 2015 | 65 | 2015 |
| An introduction to the Hilbert-Schmidt SVD using iterated Brownian bridge kernels R Cavoretto, GE Fasshauer, M McCourt Numerical Algorithms 68, 393-422, 2015 | 61 | 2015 |
| Practical Bayesian optimization in the presence of outliers R Martinez-Cantin, K Tee, M McCourt International conference on artificial intelligence and statistics, 1722-1731, 2018 | 60 | 2018 |
| A stratified analysis of Bayesian optimization methods I Dewancker, M McCourt, S Clark, P Hayes, A Johnson, G Ke arXiv preprint arXiv:1603.09441, 2016 | 45 | 2016 |
| Creating glasswing butterfly-inspired durable antifogging superomniphobic supertransmissive, superclear nanostructured glass through Bayesian learning and optimization S Haghanifar, M McCourt, B Cheng, J Wuenschell, P Ohodnicki, PW Leu Materials Horizons 6 (8), 1632-1642, 2019 | 41 | 2019 |
| The method of fundamental solutions in solving coupled boundary value problems for M/EEG G Ala, GE Fasshauer, E Francomano, S Ganci, MJ McCourt SIAM Journal on Scientific Computing 37 (4), B570-B590, 2015 | 33 | 2015 |
| Systems and methods implementing an intelligent optimization platform P Hayes, M McCourt, A Johnson, G Ke, S Clark US Patent 10,217,061, 2019 | 32 | 2019 |
| A strategy for ranking optimization methods using multiple criteria I Dewancker, M McCourt, S Clark, P Hayes, A Johnson, G Ke Workshop on Automatic Machine Learning, 11-20, 2016 | 30 | 2016 |
| A meshfree solver for the MEG forward problem G Ala, E Francomano, GE Fasshauer, S Ganci, MJ McCourt IEEE Transactions on Magnetics 51 (3), 1-4, 2015 | 30 | 2015 |
| An augmented MFS approach for brain activity reconstruction G Ala, GE Fasshauer, E Francomano, S Ganci, MJ McCourt Mathematics and Computers in Simulation 141, 3-15, 2017 | 24 | 2017 |
| Discovering high-performance broadband and broad angle antireflection surfaces by machine learning S Haghanifar, M McCourt, B Cheng, J Wuenschell, P Ohodnicki, PW Leu Optica 7 (7), 784-789, 2020 | 23 | 2020 |
| Sparse matrix-matrix products executed through coloring M McCourt, B Smith, H Zhang SIAM Journal on Matrix Analysis and Applications 36 (1), 90-109, 2015 | 21 | 2015 |
| Beyond the pareto efficient frontier: Constraint active search for multiobjective experimental design G Malkomes, B Cheng, EH Lee, M Mccourt International Conference on Machine Learning, 7423-7434, 2021 | 20 | 2021 |
| Efficient rollout strategies for Bayesian optimization E Lee, D Eriksson, D Bindel, B Cheng, M Mccourt Conference on Uncertainty in Artificial Intelligence, 260-269, 2020 | 19 | 2020 |
| Bayesian optimization with approximate set kernels J Kim, M McCourt, T You, S Kim, S Choi Machine Learning 110, 857-879, 2021 | 17* | 2021 |
Gregory E FasshauerProfessor of Applied Mathematics, Colorado School of MinesVerified email at mines.edu
