Extended Bayesian Information Criteria for Gaussian Graphical Models. - Rina Foygel; Mathias Drton (NIPS, 2010)

Learning with the weighted trace-norm under arbitrary sampling distributions. - Rina Foygel; Ruslan Salakhutdinov; Ohad Shamir; Nati Srebro (NIPS, 2011)

Concentration-Based Guarantees for Low-Rank Matrix Reconstruction. - Rina Foygel; Nathan Srebro (COLT, 2011)

Sparse Prediction with the $k$-Support Norm. - Andreas Argyriou; Rina Foygel; Nathan Srebro (NIPS, 2012)

Nonparametric Reduced Rank Regression. - Rina Foygel; Michael Horrell; Mathias Drton; John D. Lafferty (NIPS, 2012)

Matrix reconstruction with the local max norm. - Rina Foygel; Nathan Srebro; Ruslan Salakhutdinov (NIPS, 2012)

Sparse Prediction with the k-Overlap Norm - Andreas Argyriou; Rina Foygel; Nathan Srebro (CoRR, 2012)

Commentary on "Near-Optimal Algorithms for Online Matrix Prediction". - Rina Foygel (COLT, 2012)

Privacy: A few definitional aspects and consequences for minimax mean-squared error. - Rina Foygel Barber; John C. Duchi (CDC, 2014)

Privacy and Statistical Risk: Formalisms and Minimax Bounds. - Rina Foygel Barber; John C. Duchi (CoRR, 2014)

Corrupted Sensing: Novel Guarantees for Separating Structured Signals. - Rina Foygel; Lester W. Mackey (IEEE Trans. Inf. Theory, 2014)

The Log-Shift Penalty for Adaptive Estimation of Multiple Gaussian Graphical Models. - Yuancheng Zhu; Rina Foygel Barber (AISTATS, 2015)

Robust PCA with compressed data. - Wooseok Ha; Rina Foygel Barber (NIPS, 2015)

ROCKET: Robust Confidence Intervals via Kendall's Tau for Transelliptical Graphical Models. - Rina Foygel Barber; Mladen Kolar (CoRR, 2015)

The knockoff filter for FDR control in group-sparse and multitask regression. - Ran Dai; Rina Barber (ICML, 2016)

Selective inference for group-sparse linear models. - Fan Yang; Rina Foygel Barber; Prateek Jain 002; John D. Lafferty (NIPS, 2016)

MOCCA: Mirrored Convex/Concave Optimization for Nonconvex Composite Functions. - Rina Foygel Barber; Emil Y. Sidky (J. Mach. Learn. Res., 2016)

A Spectral CT Method to Directly Estimate Basis Material Maps From Experimental Photon-Counting Data. - Taly Gilat Schmidt; Rina Foygel Barber; Emil Y. Sidky (IEEE Trans. Medical Imaging, 2017)

Prediction Rule Reshaping. - Matt Bonakdarpour; Sabyasachi Chatterjee; Rina Foygel Barber; John Lafferty (ICML, 2018)

Conformal Prediction Under Covariate Shift. - Ryan J. Tibshirani; Rina Foygel Barber; Emmanuel J. Candès; Aaditya Ramdas (NeurIPS, 2019)

With Malice Towards None: Assessing Uncertainty via Equalized Coverage. - Yaniv Romano; Rina Foygel Barber; Chiara Sabatti; Emmanuel J. Candès (CoRR, 2019)

Predictive inference is free with the jackknife+-after-bootstrap. - Byol Kim; Chen Xu; Rina Foygel Barber (NeurIPS, 2020)

Convex and Non-Convex Approaches for Statistical Inference with Class-Conditional Noisy Labels. - Hyebin Song; Ran Dai; Garvesh Raskutti; Rina Foygel Barber (J. Mach. Learn. Res., 2020)

An Equivalence between Critical Points for Rank Constraints Versus Low-Rank Factorizations. - Wooseok Ha; Haoyang Liu; Rina Foygel Barber (SIAM J. Optim., 2020)

Distribution-free inference for regression: discrete, continuous, and in between. - Yonghoon Lee; Rina Barber (NeurIPS, 2021)

Black box tests for algorithmic stability. - Byol Kim; Rina Foygel Barber (CoRR, 2021)

Iterative Approximate Cross-Validation. - Yuetian Luo; Zhimei Ren; Rina Barber (ICML, 2023)

Conformalized matrix completion. - Yu Gui; Rina Barber; Cong Ma 001 (NeurIPS, 2023)

Integrating Uncertainty Awareness into Conformalized Quantile Regression. - Raphael Rossellini; Rina Foygel Barber; Rebecca Willett (AISTATS, 2024)

Online conformal prediction with decaying step sizes. - Anastasios Nikolas Angelopoulos; Rina Barber; Stephen Bates (ICML, 2024)

Building a stable classifier with the inflated argmax. - Jake A. Soloff; Rina Barber; Rebecca Willett (NeurIPS, 2024)

Boosted Conformal Prediction Intervals. - Ran Xie; Rina Barber; Emmanuel J. Candès (NeurIPS, 2024)

The Limits of Assumption-free Tests for Algorithm Performance. - Yuetian Luo; Rina Foygel Barber (CoRR, 2024)

Is Algorithmic Stability Testable? A Unified Framework under Computational Constraints. - Yuetian Luo; Rina Foygel Barber (CoRR, 2024)

Convergence for nonconvex ADMM, with applications to CT imaging. - Rina Foygel Barber; Emil Y. Sidky (J. Mach. Learn. Res., 2024)

Bagging Provides Assumption-free Stability. - Jake A. Soloff; Rina Foygel Barber; Rebecca Willett (J. Mach. Learn. Res., 2024)

Simultaneous Activity and Attenuation Estimation in TOF-PET With TV-Constrained Nonconvex Optimization. - Zhimei Ren; Emil Y. Sidky; Rina Foygel Barber; Chien-Min Kao; Xiaochuan Pan (IEEE Trans. Medical Imaging, 2024)

Are all models wrong? Fundamental limits in distribution-free empirical model falsification. - Manuel M. Müller; Yuetian Luo; Rina Foygel Barber (COLT, 2025)

Can a calibration metric be both testable and actionable? - Raphael Rossellini; Jake A. Soloff; Rina Foygel Barber; Zhimei Ren; Rebecca Willett (COLT, 2025)

Assumption-free stability for ranking problems. - Ruiting Liang; Jake A. Soloff; Rina Foygel Barber; Rebecca Willett (CoRR, 2025)