1 code implementation • 9 Jul 2024 • Yu Gui, Rina Foygel Barber, Cong Ma

Statistical learning under distribution shift is challenging when neither prior knowledge nor fully accessible data from the target distribution is available.

1 code implementation • 11 Jun 2024 • 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 or reduced interval length.

no code implementations • 23 May 2024 • Yuetian Luo, Rina Foygel Barber

Algorithmic stability is a central notion in learning theory that quantifies the sensitivity of an algorithm to small changes in the training data.

1 code implementation • 22 May 2024 • Jake A. Soloff, Rina Foygel Barber, Rebecca Willett

We propose a new framework for algorithmic stability in the context of multiclass classification.

no code implementations • 12 Feb 2024 • Yuetian Luo, Rina Foygel Barber

In particular, we make a distinction between two questions: how good is an algorithm $A$ at the problem of learning from a training set of size $n$, versus, how good is a particular fitted model produced by running $A$ on a particular training data set of size $n$?

1 code implementation • 2 Feb 2024 • Anastasios N. Angelopoulos, Rina Foygel Barber, Stephen Bates

We introduce a method for online conformal prediction with decaying step sizes.

1 code implementation • 14 Jun 2023 • Raphael Rossellini, Rina Foygel Barber, Rebecca Willett

The reason is that the prediction intervals of CQR do not distinguish between two forms of uncertainty: first, the variability of the conditional distribution of $Y$ given $X$ (i. e., aleatoric uncertainty), and second, our uncertainty in estimating this conditional distribution (i. e., epistemic uncertainty).

1 code implementation • 5 Mar 2023 • Yuetian Luo, Zhimei Ren, Rina Foygel Barber

Cross-validation (CV) is one of the most popular tools for assessing and selecting predictive models.

1 code implementation • 30 Jan 2023 • Jake A. Soloff, Rina Foygel Barber, Rebecca Willett

Bagging is an important technique for stabilizing machine learning models.

no code implementations • 30 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.

no code implementations • 16 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.

1 code implementation • 15 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.

no code implementations • 8 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

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

no code implementations • 12 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

no code implementations • 2 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

no code implementations • 14 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

no code implementations • ICML 2018 • Matt Bonakdarpour, Sabyasachi Chatterjee, Rina Foygel Barber, John Lafferty

Two methods are proposed for high-dimensional shape-constrained regression and classification.

no code implementations • 24 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.

no code implementations • 13 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.

no code implementations • 18 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.

no code implementations • 11 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

no code implementations • 10 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).

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 $C$.

no code implementations • 26 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.

Cannot find the paper you are looking for? You can
Submit a new open access paper.

Contact us on:
hello@paperswithcode.com
.
Papers With Code is a free resource with all data licensed under CC-BY-SA.