From the 1 of 9 linked papers with an AI index.
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An analysis of binary isotonic regression: degrees of freedom and implications for calibration
Raphael Rossellini, Rina Foygel Barber, Zhimei Ren +1
The paper provides a sharp finite‑sample bound on the worst‑case degrees of freedom of binary isotonic regression and uses this result to derive a distribution‑free guarantee on th…
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…
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…