4 papers
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…