From the 1 of 9 linked papers with an AI index.
9 papers
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…
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…
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…
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…