5 papers
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…
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…
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…
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…