| Scalable approximate MCMC algorithms for the horseshoe prior J Johndrow, P Orenstein, A Bhattacharya Journal of Machine Learning Research 21 (73), 2020 | 88* | 2020 |
| 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 | 69 | 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 | 61 | 2019 |
| Online learning with optimism and delay GE Flaspohler, F Orabona, J Cohen, S Mouatadid, M Oprescu, ... International Conference on Machine Learning, 3363-3373, 2021 | 17 | 2021 |
| The sub-gaussian property of trimmed means estimators RI Oliveira, P Orenstein Unpublished, IMPA, 2019 | 12 | 2019 |
| Split conformal prediction for dependent data RI Oliveira, P Orenstein, T Ramos, JV Romano arXiv preprint arXiv:2203.15885, 2022 | 6 | 2022 |
| Robust mean estimation with the bayesian median of means P Orenstein arXiv preprint arXiv:1906.01204, 2019 | 5 | 2019 |
| A métrica de Hilbert e aplicaçoes P Orenstein Trabalho de iniciaçao cientıfica. orientador: Jairo Bochi, Departamento de …, 2009 | 3 | 2009 |
| Learned benchmarks for subseasonal forecasting S Mouatadid, P Orenstein, G Flaspohler, M Oprescu, J Cohen, F Wang, ... arXiv preprint arXiv:2109.10399, 2021 | 2 | 2021 |
| Supplement to “Robust importance sampling with adaptive winsorization.” P Orenstein | 1 | 2022 |
| Finite-sample Guarantees for Winsorized Importance Sampling P Orenstein arXiv preprint arXiv:1810.11130, 2018 | 1 | 2018 |
| Advancing Ensemble Subseasonal Forecasting with Machine Learning S Totz, J Cohen, G Flaspohler, S Mouatadid, P Orenstein, L Mackey, ... 103rd AMS Annual Meeting, 2023 | | 2023 |
| Robust importance sampling with adaptive winsorization P Orenstein Bernoulli 28 (4), 2862-2873, 2022 | | 2022 |
| Adaptive Bias Correction for Improved Subseasonal Forecasting S Mouatadid, P Orenstein, G Flaspohler, J Cohen, M Oprescu, E Fraenkel, ... arXiv preprint arXiv:2209.10666, 2022 | | 2022 |
| Split Conformal Prediction for Dependent Data P Orenstein | | 2022 |
| ExactBoost: Directly Boosting the Margin in Combinatorial and Non-decomposable Metrics D Csillag, C Piazza, T Ramos, JV Romano, RI Oliveira, P Orenstein International Conference on Artificial Intelligence and Statistics, 9017-9049, 2022 | | 2022 |
| Applying Machine Learning to Improve Subseasonal to Seasonal (S2S) Forecasts JL Cohen, L Mackey, P Orenstein, J Hwang, K Pfeiffer, S Totz AGU Fall Meeting Abstracts 2019, A24A-02, 2019 | | 2019 |
| Winsorized Importance Sampling P Orenstein | | 2019 |
| Topics in Robust Mean Estimation and Applications to Importance Sampling PN Orenstein Stanford University, 2019 | | 2019 |
| Scalable MCMC for Bayes Shrinkage Priors P Orenstein | | 2018 |
