Publications & preprints (organized by topic)

Distribution-free inference

• Bagging Provides Assumption-free Stability. (Code.)
  Jake A. Soloff, Rina Foygel Barber, and Rebecca Willett. arxiv:2301.12600

• Conformalized survival analysis with adaptive cutoffs.
  Yu Gui, Rohan Hore, Zhimei Ren, and Rina Foygel Barber. arxiv:2211.01227

• Training-conditional coverage for distribution-free predictive inference. (Code.)
  Michael Bian and Rina Foygel Barber. arXiv:2205.03647

• Conformal prediction beyond exchangeability. (Code; Slides.)
  Rina Foygel Barber, Emmanuel J. Candès, Aaditya Ramdas, and Ryan Tibshirani. arXiv:2202.13415

• Black-box tests for algorithmic stability.
  Byol Kim and Rina Foygel Barber. arXiv:2111.15546

• Distribution-free inference for regression: discrete, continuous, and in between.
  Yonghoon Lee and Rina Foygel Barber. Advances in Neural Information Processing Systems 34 (NeurIPS 2021). arXiv:2105.14075

• Is distribution-free inference possible for binary regression?
  Rina Foygel Barber. Electronic Journal of Statistics 14(2): 3487-3524. arXiv:2004.09477

• Predictive Inference Is Free with the Jackknife+-after-Bootstrap. (Code.)
  Byol Kim, Chen Xu, and Rina Foygel Barber. Advances in Neural Information Processing Systems 33 (NeurIPS 2020). arXiv:2002.09025

• With Malice Towards None: Assessing Uncertainty via Equalized Coverage. (Code here and here.)
  Yaniv Romano, Rina Foygel Barber, Chiara Sabatti, and Emmanuel J. Candès. Harvard Data Science Review 2(2). arXiv:1908.05428

• Predictive inference with the jackknife+. (Code.)
  Rina Foygel Barber, Emmanuel J. Candès, Aaditya Ramdas, and Ryan Tibshirani. Annals of Statistics 49(1): 486-507. arXiv:1905.02928

• Conformal prediction under covariate shift. (Code.)
  Ryan Tibshirani, Rina Foygel Barber, Emmanuel J. Candès, and Aaditya Ramdas. Advances in Neural Information Processing Systems 32 (NeurIPS 2019). arXiv:1904.06019

• The limits of distribution-free conditional predictive inference.
  Rina Foygel Barber, Emmanuel J. Candès, Aaditya Ramdas, and Ryan Tibshirani. Information and Inference 10(2): 455-482. arXiv:1903.04684

• Discretized conformal prediction for efficient distribution-free inference. (Code.)
  Wenyu Chen, Kelli-Jean Chun, and Rina Foygel Barber. STAT 7(1). arXiv:1709.06233

• Trimmed conformal prediction for high-dimensional models.
  Wenyu Chen, Zhaokai Wang, Wooseok Ha, and Rina Foygel Barber. arXiv:1611.09933

Knockoffs, multiple testing, & selective inference

• Selective inference for clustering with unknown variance. (Code.)
  Young-Joo Yun and Rina Foygel Barber. arxiv:2301.12999

• Derandomized Knockoffs: Leveraging E-values for False Discovery Rate Control. (Code.)
  Zhimei Ren and Rina Foygel Barber. arXiv:2205.15461

• A Power Analysis for Knockoffs with the Lasso Coefficient-Difference Statistic.
  Asaf Weinstein, Weijie J. Su, Malgorzata Bogdan, Rina Foygel Barber, and Emmanuel J. Candès. arXiv:2007.15346

• Testing goodness-of-fit and conditional independence with approximate co-sufficient sampling. (Code; Slides.)
  Rina Foygel Barber and Lucas Janson. To appear in Annals of Statistics. arXiv:2007.09851

• On the construction of knockoffs in case-control studies.
  Rina Foygel Barber and Emmanuel J. Candès. STAT 8(1). arXiv:1812.11433

• The conditional permutation test for independence while controlling for confounders. (Code.)
  Thomas B. Berrett, Yi Wang, Rina Foygel Barber, and Richard J. Samworth. Journal of the Royal Statistical Society - Series B 82(1) 175-197. arXiv:1807.05405

• Robust inference with knockoffs.
  Rina Foygel Barber, Emmanuel J. Candès, and Richard J. Samworth. Annals of Statistics 48(3): 1409-1431. arXiv:1801.03896

• A power and prediction analysis for knockoffs with Lasso statistics.
  Asaf Weinstein, Rina Foygel Barber, and Emmanuel J. Candès. arXiv:1712.06465

• A unified treatment of multiple testing with prior knowledge. (Code.)
  Aaditya Ramdas, Rina Foygel Barber, Martin J. Wainwright, and Michael I. Jordan. Annals of Statistics 47(5): 2790-2821. arXiv:1703.06222

• Selective inference for group-sparse linear models. (Code.)
  Fan Yang, Rina Foygel Barber, Prateek Jain, and John Lafferty. Advances in Neural Information Processing Systems 29 (NeurIPS 2016). arXiv:1607.08211

• Multiple testing with the structure adaptive Benjamini-Hochberg algorithm. (Code.)
  Ang Li and Rina Foygel Barber. Journal of the Royal Statistical Society - Series B 81(1): 45-74. arXiv:1606.07926

• The knockoff filter for FDR control in group-sparse and multitask regression.
  Ran Dai and Rina Foygel Barber. 33rd International Conference on Machine Learning (ICML 2016). arXiv:1602.03589

• A knockoff filter for high-dimensional selective inference.
  Rina Foygel Barber and Emmanuel J. Candès. Annals of Statistics 47(5): 2504-2537. arXiv:1602.03574

• The p-filter: multilayer FDR control for grouped hypotheses. (Code.)
  Rina Foygel Barber and Aaditya Ramdas. Journal of the Royal Statistical Society - Series B 79(4): 1247-1268. arXiv:1512.03397

• Accumulation tests for FDR control in ordered hypothesis testing. (Code.)
  Ang Li and Rina Foygel Barber. Journal of the American Statistical Association 112(518): 837-849. arXiv:1505.07352

• EigenPrism: inference for high-dimensional signal-to-noise ratios.
  Lucas Janson, Rina Foygel Barber, and Emmanuel J. Candès. Journal of the Royal Statistical Society - Series B 79(4): 1037-1065. arXiv:1505.02097

• Controlling the false discovery rate via knockoffs. (Code; Slides.)
  Rina Foygel Barber and Emmanuel J. Candès. Annals of Statistics 43(5):2055-2085. arXiv:1404.5609

Nonconvex optimization

• Convergence for nonconvex ADMM, with applications to CT imaging. (Code.)
  Rina Foygel Barber and Emil Y. Sidky. arXiv:2006.07278

• An equivalence between critical points for rank constraints versus low-rank factorizations.
  Wooseok Ha, Haoyang Liu, and Rina Foygel Barber. SIAM Journal on Optimization: 30(4) 2927-2955. arXiv:1812.00404

• Between hard and soft thresholding: optimal iterative thresholding algorithms.
  Haoyang Liu and Rina Foygel Barber. Information and Inference 9(4): 899-933. arXiv:1804.08841

• Alternating minimization and alternating descent over nonconvex sets. (Code.)
  Wooseok Ha and Rina Foygel Barber. arXiv:1709.04451

• Gradient descent with nonconvex constraints: local concavity determines convergence. (Code.)
  Rina Foygel Barber and Wooseok Ha. Information and Inference 7(4): 755-806. arXiv:1703.07755

• MOCCA: mirrored convex/concave optimization for nonconvex composite functions. (Code; Slides.)
  Rina Foygel Barber and Emil Y. Sidky. Journal of Machine Learning Research 17: 1-51. arXiv:1510.08842

Medical imaging

• Addressing CT metal artifacts using photon-counting detectors and one-step spectral CT image reconstruction.
  Taly Gilat Schmidt, Barbara A. Sammut, Rina Foygel Barber, Xiaochuan Pan, and Emil Y. Sidky. To appear in Medical Physics (2022+).

• Enhancement-Constrained Acceleration: A Robust Reconstruction Framework in Breast DCE-MRI. (Code.)
  Ty Easley, Zhen Ren, Byol Kim, Gregory Karczmar, Rina Foygel Barber, and Federico D Pineda. PLoS ONE 16(10): e0258621.

• Spectral CT metal artifact reduction using weighted masking and a one step direct inversion reconstruction algorithm.
  Taly Gilat Schmidt, Rina Foygel Barber, and Emil Y. Sidky. Proceedings of the SPIE Conference on Medical Imaging 2020: Physics of Medical Imaging.

• Convergence for nonconvex ADMM, with applications to CT imaging. (Code.)
  Rina Foygel Barber and Emil Y. Sidky. arXiv:2006.07278

• Alternating minimization based framework for simultaneous spectral calibration and image reconstruction in spectral CT.
  Wooseok Ha, Emil Y Sidky, Rina Foygel Barber, Taly Gilat Schmidt, and Xiaochuan Pan. 2018 IEEE Nuclear Science Symposium and Medical Imaging Conference.

• Estimating the spectrum in computed tomography via Kullback-Leibler divergence constrained optimization.
  Wooseok Ha, Emil Y. Sidky, Rina Foygel Barber, Taly Gilat Schmidt, and Xiaochuan Pan. Medical Physics 46(1): 81-92. arXiv:1805.00162

• Three material decomposition for spectral computed tomography enabled by block-diagonal step-preconditioning.
  Emil Y. Sidky, Rina Foygel Barber, Taly Gilat Schmidt, and Xiaochuan Pan. arXiv:1801.06263

• A Spectral CT method to directly estimate basis material maps from experimental photon-counting data.
  Taly Gilat Schmidt, Rina Foygel Barber, and Emil Y. Sidky. IEEE Transactions on Medical Imaging 36(9):1808-1819.

• X-ray spectral calibration from transmission measurements using Gaussian blur model.
  Wooseok Ha, Emil Y. Sidky, and Rina Foygel Barber. Proceedings of the SPIE Conference on Medical Imaging 2017: Physics of Medical Imaging.

• Spectral CT metal artifact reduction with an optimization-based reconstruction algorithm.
  Taly Gilat Schmidt, Rina Foygel Barber, and Emil Y. Sidky. Proceedings of the SPIE Conference on Medical Imaging 2017: Physics of Medical Imaging.

• An algorithm for constrained one-step inversion of spectral CT data.
  Rina Foygel Barber, Emil Y. Sidky, Taly Gilat Schmidt, and Xiaochuan Pan. Physics in Medicine and Biology 61: 3784-3818. arXiv:1511.03384

• MOCCA: mirrored convex/concave optimization for nonconvex composite functions. (Code; Slides.)
  Rina Foygel Barber and Emil Y. Sidky. Journal of Machine Learning Research 17: 1-51. arXiv:1510.08842

Shape-constrained estimation

• Convergence guarantee for the sparse monotone single index model.
  Ran Dai, Hyebin Song, Rina Foygel Barber, and Garvesh Raskutti. Electronic Journal of Statistics 16(2): 4449-4496. arXiv:2105.07587

• Local continuity of log-concave projection, with applications to estimation under model misspecification.
  Rina Foygel Barber and Richard Samworth. Bernoulli 27(4): 2437-2472. arXiv:2002.06117

• Convex and Non-convex Approaches for Statistical Inference with Noisy Labels.
  Hyebin Song, Ran Dai, Garvesh Raskutti, and Rina Foygel Barber. Journal of Machine Learning Research 21(168):1-58. arXiv:1910.02348

• The bias of isotonic regression. (Code.)
  Ran Dai, Hyebin Song, Rina Foygel Barber, and Garvesh Raskutti. Electronic Journal of Statistics 14(1): 801-834. arXiv:1908.04462

• Prediction rule reshaping.
  Matt Bonakdarpour, Sabyasachi Chatterjee, Rina Foygel Barber, and John Lafferty. 35th International Conference on Machine Learning (ICML 2018). arXiv:1805.06439

• Contraction and uniform convergence of isotonic regression. (Code.)
  Fan Yang and Rina Foygel Barber. Electronic Journal of Statistics 13(1): 646-677. arXiv:1706.01852

Sparse and low-rank estimation

• Convergence guarantee for the sparse monotone single index model.
  Ran Dai, Hyebin Song, Rina Foygel Barber, and Garvesh Raskutti. arXiv:2105.07587

• Robust PCA with compressed data.
  Wooseok Ha and Rina Foygel Barber. Advances in Neural Information Processing Systems 28 (NeurIPS 2015).

• ROCKET: robust confidence intervals via Kendall’s Tau for transelliptical graphical models. (Code.)
  Rina Foygel Barber and Mladen Kolar. Annals of Statistics 46(6B): 3422-3450. arXiv:1502.07641

• The Function-on-scalar LASSO with applications to longitudinal GWAS.
  Rina Foygel Barber, Matthew Reimherr, and Thomas Schill. Electronic Journal of Statistics 11(1): 1351-1389. arXiv:1610.07403

• The log-shift penalty for adaptive estimation of multiple Gaussian graphical models.
  Yuancheng Zhu and Rina Foygel Barber. 18th International Conference on Artificial Intelligence and Statistics (AISTATS 2015). arXiv:1406.1812

• High-dimensional Ising model selection with Bayesian information criteria.
  Rina Foygel Barber and Mathias Drton. Electronic Journal of Statistics 9:567-607. arXiv:1403.3374

• Corrupted sensing: novel guarantees for separating structured signals.
  Rina Foygel and Lester Mackey. IEEE Transactions on Information Theory 60(2): 1223-1247. arXiv:1305.2524

• Matrix reconstruction with the local max norm.
  Rina Foygel, Nathan Srebro, and Ruslan Salakhutdinov. Advances in Neural Information Processing Systems 25 (NeurIPS 2012). arXiv:1210.5196

• Sparse Prediction with the k-Support Norm.
  Andreas Argyriou, Rina Foygel, and Nathan Srebro. Advances in Neural Information Processing Systems 25 (NeurIPS 2012). arXiv:1204.5043

• Nonparametric reduced rank regression.
  Rina Foygel, Michael Horrell, Mathias Drton, and John Lafferty. Advances in Neural Information Processing Systems 25 (NeurIPS 2012). arXiv:1301.1919

• Bayesian model choice and information criteria in sparse generalized linear models.
  Rina Foygel and Mathias Drton. arXiv:1112.5635

• Learning with the weighted trace-norm under arbitrary sampling distributions.
  Rina Foygel, Ruslan Salakhutdinov, Ohad Shamir, and Nathan Srebro. Advances in Neural Information Processing Systems 24 (NeurIPS 2011). arXiv:1106.4251

• Fast-rate and optimistic-rate error bounds for L1-regularized regression.
  Rina Foygel and Nathan Srebro. arXiv:1108.0373

• Concentration-based guarantees for low-rank matrix reconstruction.
  Rina Foygel and Nathan Srebro. 24th Annual Conference on Learning Theory (COLT 2011). arXiv:1102.3923

• Extended Bayesian information criteria for Gaussian graphical models.
  Rina Foygel and Mathias Drton. Advances in Neural Information Processing Systems 23 (NeurIPS 2010). arXiv:1011.6640

• Exact block-wise optimization in group lasso for linear regression.
  Rina Foygel and Mathias Drton. arXiv:1010.3320

Identifiability in graphical models

• Half-trek criterion for identifiability of latent variable models.
  Rina Foygel Barber, Mathias Drton, Nils Sturma, and Luca Weihs. Annals of Statistics 50(6): 3174-3196. arXiv:2201.04457

• Half-trek criterion for generic identifiability of linear structural equation models. (Code.)
  Rina Foygel, Jan Draisma, and Mathias Drton. Annals of Statistics 40(3): 1682-1713. arXiv:1107.5552

• Global identifiability of linear structural equation models. (Code.)
  Mathias Drton, Rina Foygel, and Seth Sullivant. Annals of Statistics 39(2):865-886. arXiv:1003.1146

Other topics

• Iterative Approximate Cross-Validation. (Code.)
  Yuetian Luo, Zhimei Ren, Rina Foygel Barber. arxiv:2303.02732

• Permutation tests using arbitrary permutation distributions.
  Aaditya Ramdas, Rina Foygel Barber, Emmanuel J. Candès, and Ryan Tibshirani. arXiv:2204.13581

• Binary classification with corrupted labels.
  Yonghoon Lee and Rina Foygel Barber. Electronic Journal of Statistics 16(1): 1367-1392. arXiv:2106.09136

• Fast and Flexible Estimation of Effective Migration Surfaces.
  Joseph H. Marcus, Wooseok Ha, Rina Foygel Barber, and John Novembre. eLife, 10, e61927. bioRXiv:2020.08.07.242214

• Inferring skeletal production from time-averaged assemblages: skeletal loss pulls the timing of production pulses towards the modern period.
  Adam Tomasovych, Susan M. Kidwell, and Rina Foygel Barber. Paleobiology 42(1): 54-76.

• Laplace approximation in high-dimensional Bayesian regression.
  Rina Foygel Barber, Mathias Drton, and Kean Ming Tan. Springer series Abel Symposium vol 11 (Statistical Analysis for High Dimensional Data): 15-36. arXiv:1503.08337

• Privacy and statistical risk: Formalisms and minimax bounds.
  Rina Foygel Barber and John Duchi. arXiv:1412.4451
  Shorter conference version: Privacy: a few definitional aspects and consequences for minimax mean-squared error. 53rd IEEE Conference on Decision and Control (CDC 2014).

• Long-term accumulation of carbonate shells reflects a 100-fold drop in loss rate.
  Adam Tomasovych, Susan M. Kidwell, Rina Foygel Barber, and Darrell S. Kaufman. Geology 42(9):819-822.