575 citations · 929 across the 52 of their papers we have counts for
20 papers · 1 filter
Conformal Prediction Through the Lens of Hypothesis Testing: Universality, Impossibility, and Optimality
Ryan J. Tibshirani, Rina Foygel Barber, Aaditya Ramdas
The connections between conformal prediction and permutation tests are already widely-known in the literature. Some authors motivate conformal prediction by saying that it computes…
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…
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…
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…
Training-conditional coverage for distribution-free predictive inference
Michael Bian, Rina Foygel Barber
The field of distribution-free predictive inference provides tools for provably valid prediction without any assumptions on the distribution of the data, which can be paired with a…
Half-Trek Criterion for Identifiability of Latent Variable Models
Rina Foygel Barber, Mathias Drton, Nils Sturma +1
We consider linear structural equation models with latent variables and develop a criterion to certify whether the direct causal effects between the observable variables are identi…
Binary classification with corrupted labels
Yonghoon Lee, Rina Foygel Barber
In a binary classification problem where the goal is to fit an accurate predictor, the presence of corrupted labels in the training data set may create an additional challenge. How…
Convergence guarantee for the sparse monotone single index model
Ran Dai, Hyebin Song, Rina Foygel Barber +1
We consider a high-dimensional monotone single index model (hdSIM), which is a semiparametric extension of a high-dimensional generalize linear model (hdGLM), where the link functi…
A Power Analysis for Model-X Knockoffs with -Regularized Statistics
Asaf Weinstein, Weijie J. Su, Małgorzata Bogdan +2
Variable selection properties of procedures utilizing penalized-likelihood estimates is a central topic in the study of high dimensional linear regression problems. Existing litera…
Is distribution-free inference possible for binary regression?
Rina Foygel Barber
For a regression problem with a binary label response, we examine the problem of constructing confidence intervals for the label probability conditional on the features. In a setti…
Local continuity of log-concave projection, with applications to estimation under model misspecification
Rina Foygel Barber, Richard J. Samworth
The log-concave projection is an operator that maps a d-dimensional distribution P to an approximating log-concave density. Prior work by D{ü}mbgen et al. (2011) establishes that,…
The bias of isotonic regression
Ran Dai, Hyebin Song, Rina Foygel Barber +1
We study the bias of the isotonic regression estimator. While there is extensive work characterizing the mean squared error of the isotonic regression estimator, relatively little…
The limits of distribution-free conditional predictive inference
Rina Foygel Barber, Emmanuel J. Candès, Aaditya Ramdas +1
We consider the problem of distribution-free predictive inference, with the goal of producing predictive coverage guarantees that hold conditionally rather than marginally. Existin…
Contraction and uniform convergence of isotonic regression
Fan Yang, Rina Foygel Barber
We consider the problem of isotonic regression, where the underlying signal is assumed to satisfy a monotonicity constraint, that is, lies in the cone $\{ x\in\mathbb{R}^n…
Trimmed Conformal Prediction for High-Dimensional Models
Wenyu Chen, Zhaokai Wang, Wooseok Ha +1
In regression, conformal prediction is a general methodology to construct prediction intervals in a distribution-free manner. Although conformal prediction guarantees strong statis…
The Function-on-Scalar LASSO with Applications to Longitudinal GWAS
Rina Foygel Barber, Matthew Reimherr, Thomas Schill
We present a new methodology for simultaneous variable selection and parameter estimation in function-on-scalar regression with an ultra-high dimensional predictor vector. We exten…
Laplace Approximation in High-dimensional Bayesian Regression
Rina Foygel Barber, Mathias Drton, Kean Ming Tan
We consider Bayesian variable selection in sparse high-dimensional regression, where the number of covariates may be large relative to the samples size , but at most a moder…
ROCKET: Robust Confidence Intervals via Kendall's Tau for Transelliptical Graphical Models
Rina Foygel Barber, Mladen Kolar
Undirected graphical models are used extensively in the biological and social sciences to encode a pattern of conditional independences between variables, where the absence of an e…
Privacy and Statistical Risk: Formalisms and Minimax Bounds
Rina Foygel Barber, John C. Duchi
We explore and compare a variety of definitions for privacy and disclosure limitation in statistical estimation and data analysis, including (approximate) differential privacy, tes…
High-dimensional Ising model selection with Bayesian information criteria
Rina Foygel Barber, Mathias Drton
We consider the use of Bayesian information criteria for selection of the graph underlying an Ising model. In an Ising model, the full conditional distributions of each variable fo…