1 citations · 1 across the 8 of their papers we have counts for
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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…