| Scalable global optimization via local Bayesian optimization D Eriksson, M Pearce, J Gardner, RD Turner, M Poloczek Advances in neural information processing systems 32, 2019 | 331 | 2019 |
| Continuous multi-task bayesian optimisation with correlation M Pearce, J Branke European Journal of Operational Research 270 (3), 1074-1085, 2018 | 41 | 2018 |
| Recent advances in methodology for clinical trials in small populations: the InSPiRe project T Friede, M Posch, S Zohar, C Alberti, N Benda, E Comets, S Day, ... Orphanet journal of rare diseases 13, 1-9, 2018 | 35 | 2018 |
| Bayesian simulation optimization with input uncertainty M Pearce, J Branke Winter Simulation Conference, 2268 - 2278, 2017 | 27 | 2017 |
| Approaches to sample size calculation for clinical trials in rare diseases F Miller, S Zohar, N Stallard, J Madan, M Posch, SW Hee, M Pearce, ... Pharmaceutical statistics 17 (3), 214-230, 2018 | 26 | 2018 |
| Scalable Gaussian process variational autoencoders M Jazbec, M Ashman, V Fortuin, M Pearce, S Mandt, G Rätsch International Conference on Artificial Intelligence and Statistics, 3511-3519, 2021 | 22 | 2021 |
| Sparse Gaussian process variational autoencoders M Ashman, J So, W Tebbutt, V Fortuin, M Pearce, RE Turner arXiv preprint arXiv:2010.10177, 2020 | 19 | 2020 |
| The gaussian process prior vae for interpretable latent dynamics from pixels M Pearce Symposium on advances in approximate bayesian inference, 1-12, 2020 | 16 | 2020 |
| Efficient expected improvement estimation for continuous multiple ranking and selection M Pearce, J Branke 2017 winter simulation conference (wsc), 2161-2172, 2017 | 15 | 2017 |
| Value of information methods to design a clinical trial in a small population to optimise a health economic utility function M Pearce, SW Hee, J Madan, M Posch, S Day, F Miller, S Zohar, ... BMC Medical Research Methodology 18 (1), 1-9, 2018 | 13 | 2018 |
| Bayesian optimization allowing for common random numbers MAL Pearce, M Poloczek, J Branke Operations Research 70 (6), 3457-3472, 2022 | 11 | 2022 |
| Bayesian optimisation vs. input uncertainty reduction J Ungredda, M Pearce, J Branke ACM Transactions on Modeling and Computer Simulation (TOMACS) 32 (3), 1-26, 2022 | 10 | 2022 |
| Bayesian optimization allowing for common random numbers M Pearce, M Poloczek, J Branke arXiv preprint arXiv:1910.09259, 2019 | 9 | 2019 |
| Factorized Gaussian process variational autoencoders M Jazbec, M Pearce, V Fortuin arXiv preprint arXiv:2011.07255, 2020 | 8 | 2020 |
| Practical bayesian optimization of objectives with conditioning variables M Pearce, J Klaise, M Groves arXiv preprint arXiv:2002.09996, 2020 | 6 | 2020 |
| Comparing interpretable inference models for videos of physical motion M Pearce, S Chiappa, U Paquet 1st symposium on advances in approximate bayesian inference, 2018 | 6 | 2018 |
| On parallelizing multi-task bayesian optimization M Groves, M Pearce, J Branke 2018 Winter Simulation Conference (WSC), 1993-2002, 2018 | 5 | 2018 |
| Efficient information collection on portfolios M Pearce, J Branke University of Warwick, 2017 | 3 | 2017 |
| Multi-objective path planning for environmental monitoring using an autonomous surface vehicle F Peralta, M Pearce, M Poloczek, DG Reina, S Toral, J Branke Proceedings of the Genetic and Evolutionary Computation Conference Companion …, 2022 | 2 | 2022 |
| Bayesian simulation optimization with common random numbers M Pearce, M Poloczek, J Branke 2019 Winter Simulation Conference (WSC), 3492-3503, 2019 | 2 | 2019 |
Matthias PoloczekPrincipal Scientist at AmazonVerified email at cornell.edu
