3 citations · 4 across the 5 of their papers we have counts for
6 papers
Assumption-free stability for ranking problems
Ruiting Liang, Jake A. Soloff, Rina Foygel Barber +1
In this work, we consider ranking problems among a finite set of candidates: for instance, selecting the top- items among a larger list of candidates or obtaining the full ranki…
Can a calibration metric be both testable and actionable?
Raphael Rossellini, Jake A. Soloff, Rina Foygel Barber +2
Forecast probabilities often serve as critical inputs for binary decision making. In such settings, calibration$\unicode{x2014}$ensuring forecasted probabilities match empirical fr…
Stabilizing black-box model selection with the inflated argmax
Melissa Adrian, Jake A. Soloff, Rebecca Willett
Model selection is the process of choosing from a class of candidate models given data. For instance, methods such as the LASSO and sparse identification of nonlinear dynamics (SIN…
Stability via resampling: statistical problems beyond the real line
Jake A. Soloff, Rina Foygel Barber, Rebecca Willett
Model averaging techniques based on resampling methods (such as bootstrapping or subsampling) have been utilized across many areas of statistics, often with the explicit goal of pr…
Building a stable classifier with the inflated argmax
Jake A. Soloff, Rina Foygel Barber, Rebecca Willett
We propose a new framework for algorithmic stability in the context of multiclass classification. In practice, classification algorithms often operate by first assigning a continuo…
Data Assimilation with Machine Learning Surrogate Models: A Case Study with FourCastNet
Melissa Adrian, Daniel Sanz-Alonso, Rebecca Willett
Modern data-driven surrogate models for weather forecasting provide accurate short-term predictions but inaccurate and nonphysical long-term forecasts. This paper investigates onli…