Search Results for author: Rina Foygel Barber

Found 16 papers, 2 papers with code

Black box tests for algorithmic stability

no code implementations30 Nov 2021 Byol Kim, Rina Foygel Barber

Algorithmic stability is a concept from learning theory that expresses the degree to which changes to the input data (e. g., removal of a single data point) may affect the outputs of a regression algorithm.

Learning Theory

Binary classification with corrupted labels

no code implementations16 Jun 2021 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.

Classification

With Malice Towards None: Assessing Uncertainty via Equalized Coverage

1 code implementation15 Aug 2019 Yaniv Romano, Rina Foygel Barber, Chiara Sabatti, Emmanuel J. Candès

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.

Prediction Intervals Recommendation Systems

Predictive inference with the jackknife+

no code implementations8 May 2019 Rina Foygel Barber, Emmanuel J. Candes, Aaditya Ramdas, Ryan J. Tibshirani

This paper introduces the jackknife+, which is a novel method for constructing predictive confidence intervals.

Methodology

Conformal Prediction Under Covariate Shift

1 code implementation NeurIPS 2019 Rina Foygel Barber, Emmanuel J. Candes, Aaditya Ramdas, Ryan J. Tibshirani

We extend conformal prediction methodology beyond the case of exchangeable data.

Methodology

The limits of distribution-free conditional predictive inference

no code implementations12 Mar 2019 Rina Foygel Barber, Emmanuel J. Candès, Aaditya Ramdas, Ryan J. Tibshirani

We consider the problem of distribution-free predictive inference, with the goal of producing predictive coverage guarantees that hold conditionally rather than marginally.

Statistics Theory Statistics Theory

An equivalence between critical points for rank constraints versus low-rank factorizations

no code implementations2 Dec 2018 Wooseok Ha, Haoyang Liu, Rina Foygel Barber

Two common approaches in low-rank optimization problems are either working directly with a rank constraint on the matrix variable, or optimizing over a low-rank factorization so that the rank constraint is implicitly ensured.

Optimization and Control

The conditional permutation test for independence while controlling for confounders

no code implementations14 Jul 2018 Thomas B. Berrett, Yi Wang, Rina Foygel Barber, Richard J. Samworth

Like the conditional randomization test of Cand\`es et al. (2018), our test relies on the availability of an approximation to the distribution of $X \mid Z$.

Methodology Statistics Theory Statistics Theory

Between hard and soft thresholding: optimal iterative thresholding algorithms

no code implementations24 Apr 2018 Haoyang Liu, Rina Foygel Barber

Instead, a general class of thresholding operators, lying between hard thresholding and soft thresholding, is shown to be optimal with the strongest possible convergence guarantee among all thresholding operators.

Alternating minimization and alternating descent over nonconvex sets

no code implementations13 Sep 2017 Wooseok Ha, Rina Foygel Barber

We analyze the performance of alternating minimization for loss functions optimized over two variables, where each variable may be restricted to lie in some potentially nonconvex constraint set.

A unified treatment of multiple testing with prior knowledge using the p-filter

no code implementations18 Mar 2017 Aaditya Ramdas, Rina Foygel Barber, Martin J. Wainwright, Michael. I. Jordan

There is a significant literature on methods for incorporating knowledge into multiple testing procedures so as to improve their power and precision.

The knockoff filter for FDR control in group-sparse and multitask regression

no code implementations11 Feb 2016 Ran Dai, Rina Foygel Barber

We propose the group knockoff filter, a method for false discovery rate control in a linear regression setting where the features are grouped, and we would like to select a set of relevant groups which have a nonzero effect on the response.

Methodology

The p-filter: multi-layer FDR control for grouped hypotheses

no code implementations10 Dec 2015 Rina Foygel Barber, Aaditya Ramdas

In many practical applications of multiple hypothesis testing using the False Discovery Rate (FDR), the given hypotheses can be naturally partitioned into groups, and one may not only want to control the number of false discoveries (wrongly rejected null hypotheses), but also the number of falsely discovered groups of hypotheses (we say a group is falsely discovered if at least one hypothesis within that group is rejected, when in reality the group contains only nulls).

Two-sample testing

Robust PCA with compressed data

no code implementations NeurIPS 2015 Wooseok Ha, Rina Foygel Barber

The robust principal component analysis (RPCA) problem seeks to separate low-rank trends from sparse outlierswithin a data matrix, that is, to approximate a $n\times d$ matrix $D$ as the sum of a low-rank matrix $L$ and a sparse matrix $S$. We examine the robust principal component analysis (RPCA) problem under data compression, wherethe data $Y$ is approximately given by $(L + S)\cdot C$, that is, a low-rank $+$ sparse data matrix that has been compressed to size $n\times m$ (with $m$ substantially smaller than the original dimension $d$) via multiplication witha compression matrix $C$.

Data Compression

ROCKET: Robust Confidence Intervals via Kendall's Tau for Transelliptical Graphical Models

no code implementations26 Feb 2015 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 edge between two nodes $a$ and $b$ indicates that the corresponding two variables $X_a$ and $X_b$ are believed to be conditionally independent, after controlling for all other measured variables.

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