575 citations · 929 across the 52 of their papers we have counts for
11 papers · 1 filter
Algorithmic stability implies training-conditional coverage for distribution-free prediction methods
Ruiting Liang, Rina Foygel Barber
In a supervised learning problem, given a predicted value that is the output of some trained model, how can we quantify our uncertainty around this prediction? Distribution-free pr…
Conformal prediction with local weights: randomization enables local guarantees
Rohan Hore, Rina Foygel Barber
In this work, we consider the problem of building distribution-free prediction intervals with finite-sample conditional coverage guarantees. Conformal prediction (CP) is an increas…
Approximate co-sufficient sampling with regularization
Wanrong Zhu, Rina Foygel Barber
In this work, we consider the problem of goodness-of-fit (GoF) testing for parametric models. This testing problem involves a composite null hypothesis, due to the unknown values o…
Integrating Uncertainty Awareness into Conformalized Quantile Regression
Raphael Rossellini, Rina Foygel Barber, Rebecca Willett
Conformalized Quantile Regression (CQR) is a recently proposed method for constructing prediction intervals for a response given covariates , without making distributional a…
Distribution-free inference with hierarchical data
Yonghoon Lee, Rina Foygel Barber, Rebecca Willett
This paper studies distribution-free inference in settings where the data set has a hierarchical structure -- for example, groups of observations, or repeated measurements. In such…
Conformalized matrix completion
Yu Gui, Rina Foygel Barber, Cong Ma
Matrix completion aims to estimate missing entries in a data matrix, using the assumption of a low-complexity structure (e.g., low rank) so that imputation is possible. While many…
De Finetti's theorem and related results for infinite weighted exchangeable sequences
Rina Foygel Barber, Emmanuel J. Candes, Aaditya Ramdas +1
De Finetti's theorem, also called the de Finetti-Hewitt-Savage theorem, is a foundational result in probability and statistics. Roughly, it says that an infinite sequence of exchan…
Simultaneous activity and attenuation estimation in TOF-PET with TV-constrained nonconvex optimization
Zhimei Ren, Emil Y. Sidky, Rina Foygel Barber +2
An alternating direction method of multipliers (ADMM) framework is developed for nonsmooth biconvex optimization for inverse problems in imaging. In particular, the simultaneous es…
Iterative Approximate Cross-Validation
Yuetian Luo, Zhimei Ren, Rina Foygel Barber
Cross-validation (CV) is one of the most popular tools for assessing and selecting predictive models. However, standard CV suffers from high computational cost when the number of f…
Selective inference for clustering with unknown variance
Youngjoo Yun, Rina Foygel Barber
In many modern statistical problems, the limited available data must be used both to develop the hypotheses to test, and to test these hypotheses-that is, both for exploratory and…
Bagging Provides Assumption-free Stability
Jake A. Soloff, Rina Foygel Barber, Rebecca Willett
Bagging is an important technique for stabilizing machine learning models. In this paper, we derive a finite-sample guarantee on the stability of bagging for any model. Our result…