575 citations · 929 across the 52 of their papers we have counts for
32 papers · 1 filter
Monte Carlo testing: non-asymptotic guarantees without joint exchangeability
Rina Foygel Barber, Aaditya Ramdas
In hypothesis testing, Monte Carlo tests are usually justified either by exact null simulation or by joint exchangeability of the observed data and its simulated copies. This leave…
Conformal prediction after data-dependent model selection
Ruiting Liang, Wanrong Zhu, Rina Foygel Barber
Given a family of pretrained models and a hold-out set, how can we construct a valid conformal prediction set while selecting a model that minimizes the width of the set? If we use…
Boosted Conformal Prediction Intervals
Ran Xie, Rina Foygel Barber, Emmanuel J. Candès
This paper introduces a boosted conformal procedure designed to tailor conformalized prediction intervals toward specific desired properties, such as enhanced conditional coverage…
Group-Weighted Conformal Prediction
Aabesh Bhattacharyya, Rina Foygel Barber
Conformal prediction (CP) is a method for constructing a prediction interval around the output of a fitted model, whose validity does not rely on the model being correct--the CP in…
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…
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…
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…
Conformalized survival analysis with adaptive cutoffs
Yu Gui, Rohan Hore, Zhimei Ren +1
This paper introduces an assumption-lean method that constructs valid and efficient lower predictive bounds (LPBs) for survival times with censored data. We build on recent work by…
Derandomized knockoffs: leveraging e-values for false discovery rate control
Zhimei Ren, Rina Foygel Barber
Model-X knockoffs is a flexible wrapper method for high-dimensional regression algorithms, which provides guaranteed control of the false discovery rate (FDR). Due to the randomnes…
Permutation tests using arbitrary permutation distributions
Aaditya Ramdas, Rina Foygel Barber, Emmanuel J. Candes +1
Permutation tests date back nearly a century to Fisher's randomized experiments, and remain an immensely popular statistical tool, used for testing hypotheses of independence betwe…
Conformal prediction beyond exchangeability
Rina Foygel Barber, Emmanuel J. Candes, Aaditya Ramdas +1
Conformal prediction is a popular, modern technique for providing valid predictive inference for arbitrary machine learning models. Its validity relies on the assumptions of exchan…
Predictive Inference Is Free with the Jackknife+-after-Bootstrap
Byol Kim, Chen Xu, Rina Foygel Barber
Ensemble learning is widely used in applications to make predictions in complex decision problems---for example, averaging models fitted to a sequence of samples bootstrapped from…
Convex and Non-convex Approaches for Statistical Inference with Class-Conditional Noisy Labels
Hyebin Song, Ran Dai, Garvesh Raskutti +1
We study the problem of estimation and testing in logistic regression with class-conditional noise in the observed labels, which has an important implication in the Positive-Unlabe…
With Malice Towards None: Assessing Uncertainty via Equalized Coverage
Yaniv Romano, Rina Foygel Barber, Chiara Sabatti +1
An important factor to guarantee a fair use of data-driven recommendation systems is that we should be able to communicate their uncertainty to decision makers. This can be accompl…
Predictive inference with the jackknife+
Rina Foygel Barber, Emmanuel J. Candes, Aaditya Ramdas +1
This paper introduces the jackknife+, which is a novel method for constructing predictive confidence intervals. Whereas the jackknife outputs an interval centered at the predicted…
Conformal Prediction Under Covariate Shift
Ryan J. Tibshirani, Rina Foygel Barber, Emmanuel J. Candes +1
We extend conformal prediction methodology beyond the case of exchangeable data. In particular, we show that a weighted version of conformal prediction can be used to compute distr…
On the Construction of Knockoffs in Case-Control Studies
Rina Foygel Barber, Emmanuel Candes
Consider a case-control study in which we have a random sample, constructed in such a way that the proportion of cases in our sample is different from that in the general populatio…
The conditional permutation test for independence while controlling for confounders
Thomas B. Berrett, Yi Wang, Rina Foygel Barber +1
We propose a general new method, the conditional permutation test, for testing the conditional independence of variables and given a potentially high-dimensional random vec…
Between hard and soft thresholding: optimal iterative thresholding algorithms
Haoyang Liu, Rina Foygel Barber
Iterative thresholding algorithms seek to optimize a differentiable objective function over a sparsity or rank constraint by alternating between gradient steps that reduce the obje…
Robust inference with knockoffs
Rina Foygel Barber, Emmanuel J. Candès, Richard J. Samworth
We consider the variable selection problem, which seeks to identify important variables influencing a response out of many candidate features . We wish to do…
A Power and Prediction Analysis for Knockoffs with Lasso Statistics
Asaf Weinstein, Rina Barber, Emmanuel Candes
Knockoffs is a new framework for controlling the false discovery rate (FDR) in multiple hypothesis testing problems involving complex statistical models. While there has been great…