Paulo Orenstein
Title
Cited by
Cited by
Year
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
152019
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
122019
A métrica de Hilbert e aplicaçoes
P Orenstein
Trabalho de iniciaçao cientıfica. orientador: Jairo Bochi, Departamento de …, 2009
32009
Finite-sample Guarantees for Winsorized Importance Sampling
P Orenstein
arXiv preprint arXiv:1810.11130, 2018
12018
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
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Articles 1–11