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Rina Foygel Barber
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- affiliation: University of Chicago
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2020 – today
- 2024
- [j8]Rina Foygel Barber, Emil Y. Sidky:
Convergence for nonconvex ADMM, with applications to CT imaging. J. Mach. Learn. Res. 25: 38:1-38:46 (2024) - [j7]Jake A. Soloff, Rina Foygel Barber, Rebecca Willett:
Bagging Provides Assumption-free Stability. J. Mach. Learn. Res. 25: 131:1-131:35 (2024) - [j6]Zhimei Ren, Emil Y. Sidky
, Rina Foygel Barber
Simultaneous Activity and Attenuation Estimation in TOF-PET With TV-Constrained Nonconvex Optimization. IEEE Trans. Medical Imaging 43(6): 2347-2357 (2024) - [c20]Raphael Rossellini, Rina Foygel Barber, Rebecca Willett:
Integrating Uncertainty Awareness into Conformalized Quantile Regression. AISTATS 2024: 1540-1548 - [c19]Anastasios Nikolas Angelopoulos, Rina Barber, Stephen Bates:
Online conformal prediction with decaying step sizes. ICML 2024 - [i15]Anastasios N. Angelopoulos, Rina Foygel Barber, Stephen Bates:
Online conformal prediction with decaying step sizes. CoRR abs/2402.01139 (2024) - [i14]Yuetian Luo, Rina Foygel Barber:
The Limits of Assumption-free Tests for Algorithm Performance. CoRR abs/2402.07388 (2024) - [i13]Jake A. Soloff, Rina Foygel Barber, Rebecca Willett:
Building a stable classifier with the inflated argmax. CoRR abs/2405.14064 (2024) - [i12]Yuetian Luo, Rina Foygel Barber:
Is Algorithmic Stability Testable? A Unified Framework under Computational Constraints. CoRR abs/2405.15107 (2024) - 2023
- [c18]Yuetian Luo, Zhimei Ren, Rina Barber:
Iterative Approximate Cross-Validation. ICML 2023: 23083-23102 - [c17]Yu Gui, Rina Barber, Cong Ma:
Conformalized matrix completion. NeurIPS 2023 - [i11]Jake A. Soloff, Rina Foygel Barber, Rebecca Willett:
Bagging Provides Assumption-free Stability. CoRR abs/2301.12600 (2023) - 2021
- [c16]Yonghoon Lee, Rina Barber:
Distribution-free inference for regression: discrete, continuous, and in between. NeurIPS 2021: 7448-7459 - [i10]Byol Kim, Rina Foygel Barber:
Black box tests for algorithmic stability. CoRR abs/2111.15546 (2021) - 2020
- [j5]Hyebin Song, Ran Dai, Garvesh Raskutti, Rina Foygel Barber:
Convex and Non-Convex Approaches for Statistical Inference with Class-Conditional Noisy Labels. J. Mach. Learn. Res. 21: 168:1-168:58 (2020) - [j4]Wooseok Ha
An Equivalence between Critical Points for Rank Constraints Versus Low-Rank Factorizations. SIAM J. Optim. 30(4): 2927-2955 (2020) - [c15]Byol Kim, Chen Xu, Rina Foygel Barber:
Predictive inference is free with the jackknife+-after-bootstrap. NeurIPS 2020
2010 – 2019
- 2019
- [c14]Ryan J. Tibshirani, Rina Foygel Barber, Emmanuel J. Candès, Aaditya Ramdas:
Conformal Prediction Under Covariate Shift. NeurIPS 2019: 2526-2536 - [i9]Yaniv Romano, Rina Foygel Barber, Chiara Sabatti, Emmanuel J. Candès:
With Malice Towards None: Assessing Uncertainty via Equalized Coverage. CoRR abs/1908.05428 (2019) - 2018
- [c13]Matt Bonakdarpour, Sabyasachi Chatterjee, Rina Foygel Barber, John Lafferty:
Prediction Rule Reshaping. ICML 2018: 629-637 - [i8]Matt Bonakdarpour, Sabyasachi Chatterjee, Rina Foygel Barber, John D. Lafferty:
Prediction Rule Reshaping. CoRR abs/1805.06439 (2018) - 2017
- [j3]Taly Gilat Schmidt
A Spectral CT Method to Directly Estimate Basis Material Maps From Experimental Photon-Counting Data. IEEE Trans. Medical Imaging 36(9): 1808-1819 (2017) - 2016
- [j2]Rina Foygel Barber, Emil Y. Sidky:
MOCCA: Mirrored Convex/Concave Optimization for Nonconvex Composite Functions. J. Mach. Learn. Res. 17: 144:1-144:51 (2016) - [c12]Ran Dai, Rina Barber:
The knockoff filter for FDR control in group-sparse and multitask regression. ICML 2016: 1851-1859 - [c11]Fan Yang, Rina Foygel Barber, Prateek Jain, John D. Lafferty:
Selective inference for group-sparse linear models. NIPS 2016: 2469-2477 - 2015
- [c10]Yuancheng Zhu, Rina Foygel Barber:
The Log-Shift Penalty for Adaptive Estimation of Multiple Gaussian Graphical Models. AISTATS 2015 - [c9]Wooseok Ha, Rina Foygel Barber:
Robust PCA with compressed data. NIPS 2015: 1936-1944 - [i7]Rina Foygel Barber, Mladen Kolar:
ROCKET: Robust Confidence Intervals via Kendall's Tau for Transelliptical Graphical Models. CoRR abs/1502.07641 (2015) - 2014
- [j1]Rina Foygel, Lester W. Mackey
Corrupted Sensing: Novel Guarantees for Separating Structured Signals. IEEE Trans. Inf. Theory 60(2): 1223-1247 (2014) - [c8]Rina Foygel Barber, John C. Duchi:
Privacy: A few definitional aspects and consequences for minimax mean-squared error. CDC 2014: 1365-1369 - [i6]Rina Foygel Barber, John C. Duchi:
Privacy and Statistical Risk: Formalisms and Minimax Bounds. CoRR abs/1412.4451 (2014) - 2013
- [i5]Rina Foygel, Lester W. Mackey:
Corrupted Sensing: Novel Guarantees for Separating Structured Signals. CoRR abs/1305.2524 (2013) - 2012
- [c7]Rina Foygel, Nathan Srebro, Ruslan Salakhutdinov:
Matrix reconstruction with the local max norm. NIPS 2012: 944-952 - [c6]Andreas Argyriou, Rina Foygel, Nathan Srebro:
Sparse Prediction with the $k$-Support Norm. NIPS 2012: 1466-1474 - [c5]Rina Foygel, Michael Horrell, Mathias Drton, John D. Lafferty:
Nonparametric Reduced Rank Regression. NIPS 2012: 1637-1645 - [c4]Rina Foygel:
Commentary on "Near-Optimal Algorithms for Online Matrix Prediction". COLT 2012: 38.14-38.17 - [i4]Andreas Argyriou, Rina Foygel, Nathan Srebro:
Sparse Prediction with the k-Overlap Norm. CoRR abs/1204.5043 (2012) - [i3]Rina Foygel, Nathan Srebro, Ruslan Salakhutdinov:
Matrix reconstruction with the local max norm. CoRR abs/1210.5196 (2012) - 2011
- [c3]Rina Foygel, Ruslan Salakhutdinov, Ohad Shamir, Nati Srebro:
Learning with the weighted trace-norm under arbitrary sampling distributions. NIPS 2011: 2133-2141 - [c2]Rina Foygel, Nathan Srebro:
Concentration-Based Guarantees for Low-Rank Matrix Reconstruction. COLT 2011: 315-340 - [i2]Rina Foygel, Nathan Srebro:
Concentration-Based Guarantees for Low-Rank Matrix Reconstruction. CoRR abs/1102.3923 (2011) - [i1]Rina Foygel, Ruslan Salakhutdinov, Ohad Shamir, Nathan Srebro:
Learning with the Weighted Trace-norm under Arbitrary Sampling Distributions. CoRR abs/1106.4251 (2011) - 2010
- [c1]Rina Foygel, Mathias Drton:
Extended Bayesian Information Criteria for Gaussian Graphical Models. NIPS 2010: 604-612
Coauthor Index
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last updated on 2024-09-18 01:12 CEST by the dblp team
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