1 citations · 1 across the 8 of their papers we have counts for
10 papers
Online Quantile Omniprediction for Proper Losses
Hyunsuk Kim, Isaac Gibbs, Michael W. Mahoney +1
The task of constructing a predictor that is simultaneously accurate under multiple evaluation metrics has been popularized recently under the name omniprediction. Prior work in th…
Fast, Frequentist Estimation of Epidemic Reproduction Numbers
Jeremy Goldwasser, Ryan J. Tibshirani, Alyssa Bilinski
The effective reproduction number is one of the most important indicators of epidemic dynamics. Estimating , typically from case reports or hospitalization counts, poses…
On the Equivalence of Instantaneous and Mechanistic Reproduction Numbers
Jeremy Goldwasser, Ryan J. Tibshirani, Alyssa Bilinski
The effective reproduction number () is widely used to track epidemic dynamics in real time. The standard estimation framework uses "instantaneous ," defined via the rene…
Basic Inequalities for First-Order Optimization with Applications to Statistical Risk Analysis
Seunghoon Paik, Kangjie Zhou, Matus Telgarsky +1
In this work, we introduce for first-order iterative optimization algorithms, forming a simple yet versatile framework which connects implicit and exp…
Calibrated Multi-Level Quantile Forecasting
Tiffany Ding, Isaac Gibbs, Ryan J. Tibshirani
We develop an online method that guarantees calibration of quantile forecasts at multiple quantile levels simultaneously. In this work, a sequence of quantile forecasts is said to…
Sample-Efficient Omniprediction for Proper Losses
Isaac Gibbs, Ryan J. Tibshirani
We consider the problem of constructing probabilistic predictions that lead to accurate decisions when employed by downstream users to inform actions. For a single decision maker,…
Estimating Time-Varying Epidemic Severity Rates with Adaptive Deconvolution
Jeremy Goldwasser, Addison J. Hu, Alyssa Bilinski +2
Several key metrics in public health convey the probability that a primary event will lead to a more serious secondary event in the future.Several key metrics in public health conv…
Asymmetric Penalties Underlie Proper Loss Functions in Probabilistic Forecasting
Erez Buchweitz, João Vitor Romano, Ryan J. Tibshirani
Accurately forecasting the probability distribution of phenomena of interest is a classic and ever more widespread goal in statistics and decision theory. In comparison to point fo…
Two-Sample Testing with a Graph-Based Total Variation Integral Probability Metric
Alden Green, Sivaraman Balakrishnan, Ryan J. Tibshirani
We consider a novel multivariate nonparametric two-sample testing problem where, under the alternative, distributions and are separated in an integral probability metric ov…
Integral Probability Metrics Meet Neural Networks: The Radon-Kolmogorov-Smirnov Test
Seunghoon Paik, Michael Celentano, Alden Green +1
Integral probability metrics (IPMs) constitute a general class of nonparametric two-sample tests that are based on maximizing the mean difference between samples from one distribut…