4 citations · 7 across the 3 of their papers we have counts for
3 papers
cs.LG2023
Parity Calibration
Youngseog Chung, Aaron Rumack, Chirag Gupta
In a sequential regression setting, a decision-maker may be primarily concerned with whether the future observation will increase or decrease compared to the current one, rather th…
stat.ME2022★ 3 cited
Comparing trained and untrained probabilistic ensemble forecasts of COVID-19 cases and deaths in the United States
Evan L. Ray, Logan C. Brooks, Jacob Bien +12
The U.S. COVID-19 Forecast Hub aggregates forecasts of the short-term burden of COVID-19 in the United States from many contributing teams. We study methods for building an ensembl…
cs.LG2021★ 4 cited
Recalibrating probabilistic forecasts of epidemics
Aaron Rumack, Ryan J. Tibshirani, Roni Rosenfeld
Distributional forecasts are important for a wide variety of applications, including forecasting epidemics. Often, forecasts are miscalibrated, or unreliable in assigning uncertain…