51 citations · 90 across the 7 of their papers we have counts for
8 papers
Adaptive Bias Correction for Improved Subseasonal Forecasting
Soukayna Mouatadid, Paulo Orenstein, Genevieve Flaspohler +4
Subseasonal forecasting -- predicting temperature and precipitation 2 to 6 weeks ahead -- is critical for effective water allocation, wildfire management, and drought and flood mit…
Split Conformal Prediction and Non-Exchangeable Data
Roberto I. Oliveira, Paulo Orenstein, Thiago Ramos +1
Split conformal prediction (CP) is arguably the most popular CP method for uncertainty quantification, enjoying both academic interest and widespread deployment. However, the origi…
SubseasonalClimateUSA: A Dataset for Subseasonal Forecasting and Benchmarking
Soukayna Mouatadid, Paulo Orenstein, Genevieve Flaspohler +8
Subseasonal forecasting of the weather two to six weeks in advance is critical for resource allocation and advance disaster notice but poses many challenges for the forecasting com…
Online Learning with Optimism and Delay
Genevieve Flaspohler, Francesco Orabona, Judah Cohen +4
Inspired by the demands of real-time climate and weather forecasting, we develop optimistic online learning algorithms that require no parameter tuning and have optimal regret guar…
Robust Mean Estimation with the Bayesian Median of Means
Paulo Orenstein
The sample mean is often used to aggregate different unbiased estimates of a parameter, producing a final estimate that is unbiased but possibly high-variance. This paper introduce…
Robust Importance Sampling with Adaptive Winsorization
Paulo Orenstein
Importance sampling is a widely used technique to estimate properties of a distribution. This paper investigates trading-off some bias for variance by adaptively winsorizing the im…
Improving Subseasonal Forecasting in the Western U.S. with Machine Learning
Jessica Hwang, Paulo Orenstein, Judah Cohen +2
Water managers in the western United States (U.S.) rely on longterm forecasts of temperature and precipitation to prepare for droughts and other wet weather extremes. To improve th…
Bayes Shrinkage at GWAS scale: Convergence and Approximation Theory of a Scalable MCMC Algorithm for the Horseshoe Prior
James E. Johndrow, Paulo Orenstein, Anirban Bhattacharya
The horseshoe prior is frequently employed in Bayesian analysis of high-dimensional models, and has been shown to achieve minimax optimal risk properties when the truth is sparse.…