1 citations · 1 across the 8 of their papers we have counts for
10 papers
An analysis of binary isotonic regression: degrees of freedom and implications for calibration
Raphael Rossellini, Rina Foygel Barber, Zhimei Ren +1
Isotonic regression is a canonical tool for estimating monotone functions and calibrating probabilistic predictors. We provide a fully sharp finite-sample characterization of its w…
Calibration without labels in multiple testing
Adway S. Wadekar, Jake A. Soloff
Large-scale hypothesis testing supports probability claims about individual hypotheses, as in empirical Bayes methods for estimating local false discovery rates. We study how such…
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…
A frequentist local false discovery rate
Daniel Xiang, Jake A. Soloff, William Fithian
The local false discovery rate (lfdr) of Efron et al. (2001) enjoys major conceptual and decision-theoretic advantages over the false discovery rate (FDR) as an error criterion in…
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…
Testing conditional independence under isotonicity
Rohan Hore, Jake A. Soloff, Rina Foygel Barber +1
We propose a test of the conditional independence of random variables and~ given~ under the additional assumption that is stochastically nondecreasing in~. The wel…
Cross-Validation with Antithetic Gaussian Randomization
Sifan Liu, Snigdha Panigrahi, Jake A. Soloff
We introduce a new cross-validation method based on an equicorrelated Gaussian randomization scheme. Our method is well-suited for problems where sample splitting is infeasible, ei…
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…