| Bayes shrinkage at GWAS scale: Convergence and approximation theory of a scalable MCMC algorithm for the horseshoe prior JE Johndrow, P Orenstein, A Bhattacharya arXiv preprint arXiv:1705.00841, 2017 | 41* | 2017 |
| S2S reboot: An argument for greater inclusion of machine learning in subseasonal to seasonal forecasts J Cohen, D Coumou, J Hwang, L Mackey, P Orenstein, S Totz, ... Wiley Interdisciplinary Reviews: Climate Change 10 (2), e00567, 2019 | 15 | 2019 |
| Improving subseasonal forecasting in the western US with machine learning J Hwang, P Orenstein, J Cohen, K Pfeiffer, L Mackey Proceedings of the 25th ACM SIGKDD International Conference on Knowledge …, 2019 | 12 | 2019 |
| A métrica de Hilbert e aplicaçoes P Orenstein Trabalho de iniciaçao cientıfica. orientador: Jairo Bochi, Departamento de …, 2009 | 3 | 2009 |
| Finite-sample Guarantees for Winsorized Importance Sampling P Orenstein arXiv preprint arXiv:1810.11130, 2018 | 1 | 2018 |
| Applying Machine Learning to Improve Subseasonal to Seasonal (S2S) Forecasts JL Cohen, L Mackey, P Orenstein, J Hwang, K Pfeiffer, S Totz AGUFM 2019, A24A-02, 2019 | | 2019 |
| Robust Mean Estimation with the Bayesian Median of Means P Orenstein arXiv preprint arXiv:1906.01204, 2019 | | 2019 |
| Winsorized Importance Sampling P Orenstein | | 2019 |
| Topics in Robust Mean Estimation and Applications to Importance Sampling PN Orenstein PQDT-Global, 2019 | | 2019 |
| Scalable MCMC for Bayes Shrinkage Priors P Orenstein | | 2018 |
| When (and how) to favor incumbents in optimal dynamic procurement auctions V Carrasco, P Orenstein, P Salgado Journal of Mathematical Economics 62, 52-61, 2016 | | 2016 |
