| 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 | 105 | 2019 |
| Scalable approximate MCMC algorithms for the horseshoe prior J Johndrow, P Orenstein, A Bhattacharya Journal of Machine Learning Research 21 (73), 1-61, 2020 | 96* | 2020 |
| 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 | 92 | 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 | 38 | 2021 |
| Adaptive bias correction for improved subseasonal forecasting S Mouatadid, P Orenstein, G Flaspohler, J Cohen, M Oprescu, E Fraenkel, ... Nature Communications 14 (1), 3482, 2023 | 27 | 2023 |
| Scalable MCMC for Bayes shrinkage priors JE Johndrow, P Orenstein, A Bhattacharya arXiv preprint arXiv:1705.00841, 0162-8828, 2017 | 22 | 2017 |
| The sub-gaussian property of trimmed means estimators RI Oliveira, P Orenstein Unpubl. IMPA 0, 2019 | 18 | 2019 |
| Split conformal prediction for dependent data RI Oliveira, P Orenstein, T Ramos, J Vitor Romano arXiv e-prints, arXiv: 2203.15885, 2022 | 15 | 2022 |
| SubseasonalClimateUSA: a dataset for subseasonal forecasting and benchmarking S Mouatadid, P Orenstein, G Flaspohler, M Oprescu, J Cohen, F Wang, ... Advances in Neural Information Processing Systems 36, 2024 | 7 | 2024 |
| AmnioML: amniotic fluid segmentation and volume prediction with uncertainty quantification D Csillag, LM Paes, T Ramos, JV Romano, R Schuller, RB Seixas, ... Proceedings of the AAAI Conference on Artificial Intelligence 37 (13), 15494 …, 2023 | 7 | 2023 |
| Learned benchmarks for subseasonal forecasting S Mouatadid, P Orenstein, G Flaspohler, M Oprescu, J Cohen, F Wang, ... arXiv preprint arXiv:2109.10399, 2021 | 7 | 2021 |
| Robust mean estimation with the bayesian median of means P Orenstein arXiv preprint arXiv:1906.01204, 2019 | 6 | 2019 |
| S2S Reboot: An Argument for Greater Inclusion of Machine Learning in Subseasonal to Seasonal Forecasts, WIREs Climate Change, 10, e00567 J Cohen, D Coumou, J Hwang, L Mackey, P Orenstein, S Totz, ... | 5 | 2019 |
| Split conformal prediction and non-exchangeable data RI Oliveira, P Orenstein, T Ramos, JV Romano Journal of Machine Learning Research 25 (225), 1-38, 2024 | 4 | 2024 |
| A métrica de Hilbert e aplicaçoes P Orenstein Trabalho de iniciaçao cientıfica. orientador: Jairo Bochi, Departamento de …, 2009 | 3 | 2009 |
| Deep Hashing via Householder Quantization LR Schwengber, L Resende, P Orenstein, RI Oliveira arXiv preprint arXiv:2311.04207, 2023 | 2 | 2023 |
| Robust importance sampling with adaptive winsorization P Orenstein Bernoulli 28 (4), 2862-2873, 2022 | 2 | 2022 |
| 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 |
| BlockBoost: Scalable and Efficient Blocking through Boosting T Ramos, RL Schuller, AA Okuno, L Nissenbaum, RI Oliveira, P Orenstein International Conference on Artificial Intelligence and Statistics, 2575-2583, 2024 | | 2024 |
