Robust Regression

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3 papers in the last four weeks, with none the four weeks before. 0.0% of all new papers.

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Distributionally robust linear regression through the lens of adversarial training
Sep 30, 2026Elis Stefansson, David Vävinggren, Antônio H. RibeiroRobust Regression Distributionally Robust Optimization
Uppsala University, Sweden · Science for Life Laboratory, Sweden
CVaR anchor regression protects against rare shifts
Sep 22, 2026Malte LondschienDistribution Shift Robustness Robust Regression
Sparse Regression Distilled from a Single Robust Fit
Sep 20, 2026Wooyoung Shin, Seunghwan ParkRobust Regression Linear Regression
Department of Information Statistics, Kangwon National University, 1, Kangwondaehak-gil, Chuncheon-si, Gangwon-do, 24341, Republic of Korea.
Higher-Order Certified Robustness for Regression
Jul 6, 2026Jie Zhang, Natalie FrankRobust Regression Randomized Smoothing
University of Washington, USA
Distributionally Robust Linear Regression With Block Lewis Weights
Jun 30, 2026Naren Sarayu Manoj, Kumar Kshitij PatelRobust Regression Distributionally Robust Optimization
TTIC · Institute for Foundations of Data Science, Yale University
Adversarial Contamination Meets Hard Thresholding: An Iterative Algorithm with Signal Adaptivity and Minimax Optimality
Jun 26, 2026Shixiang Liu, Hanming YangRobust Regression Sparse Recovery
School of Statistics, Renmin University of China · Institute of Statistics and Big Data, Renmin University of China
Alternate loss functions and regression models that achieve robustness to outliers by modulating the learning rate
Jun 20, 2026Mathew Mithra Noel, Arindam Banerjee, Yug D. Oswal +2Robust Regression Robust Loss Functions
aSchool of Electrical Engineering, Vellore Institute of Technology, Vellore, 632 014, Tamil Nadu, India · bErnst and Young GDS, Godrej Waterside, Ring Rd, DP Block, Sector V, Bidhannagar, Kolkata, 700 091, West Bengal, India · cSchool of Computer Science and Engineering, Vellore Institute of Technology, Vellore, 632 014, Tamil Nadu, India
Joint Model and Data Sparsification via the Marginal Likelihood
May 28, 2026Alexander Timans, Thomas Möllenhoff, Christian A. Naesseth +2Dataset Pruning Robust Regression
1UvA-Bosch Delta Lab, University of Amsterdam · 2RIKEN Center for AI Project, Tokyo, Japan · Department of Computer Science, Johns Hopkins University.
Algorithms with Polynomially-Improved Approximation Factors for the $2 \rightarrow q$ Norm, and Applications
May 24, 2026Samuel B. Hopkins, Stefan TiegelRobust Regression Approximation Algorithms
MIT
Multi-task Linear Regression without Eigenvalue Lower Bounds: Adaptivity, Robustness, and Safety
May 16, 2026Seok-Jin KimRobust Regression Multi-Task Learning
Columbia IEOR
Byzantine-Robust Distributed Sparse Learning Revisited
May 13, 2026Yuxuan Wang, Lixin Zhang, Kangqiang LiRobust Regression Distributed Optimization
School of Mathematical Sciences, Zhejiang University, Hangzhou, 310027, Zhejiang, China · School of Statistics and Mathematics, Zhejiang Gongshang University, Hangzhou, 310018, Zhejiang, China · Information Center, Hubei Provincial Tobacco Monopoly Administration, Wuhan, 430030, Hubei, China
SHIFT: Robust Double Machine Learning for Average Dose-Response Functions under Heavy-Tailed Contamination
Apr 30, 2026Eichi UeharaDouble ML Robust Regression
Independent researcher.
Bayesian X-Learner: Calibrated Posterior Inference for Heterogeneous Treatment Effects under Heavy-Tailed Outcomes
Apr 30, 2026Eichi UeharaRobust Regression Causal Effect Estimation
Independent researcher
Meta Additive Model: Interpretable Sparse Learning With Auto Weighting
Apr 22, 2026Xuelin Zhang, Xinyue Liu, Lingjuan Wu +1Robust Regression Adaptive Loss Weighting
College of Informatics, Huazhong Agricultural University, Wuhan, China
Linear Regression under Missing or Corrupted Coordinates
Sep 23, 2025Ilias Diakonikolas, Jelena Diakonikolas, Daniel M. Kane +2Robust Regression Learning with Missing Data
University of Wisconsin-Madison · University of California, San Diego · University of California, Davis
Robust Linear Predictions: Analyses of Uniform Concentration, Fast Rates and Model Misspecification
Jan 6, 2022Saptarshi Chakraborty, Debolina Paul, Swagatam DasRobust Regression Model Misspecification
Department of Statistics, University of California, Berkeley · Department of Statistics, Stanford University · Electronics and Communication Sciences Unit, Indian Statistical Institute, Kolkata, India

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Distributionally robust linear regression through the lens of adversarial training

Elis Stefansson, David Vävinggren, Antônio H. Ribeiro

Uppsala University, Sweden · Science for Life Laboratory, Sweden

Robust Regression Distributionally Robust Optimization Wasserstein Distributionally Robust Optimization Linear Regression Robust Estimation

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Distributionally robust optimization (DRO) studies parameter estimation under uncertainty in the underlying probability distribution and has emerged as a principled framework for analyzing robustness and generalization. In particular, Wasserstein DRO, with distributional uncertainty induced by the Wasserstein distance, generalizes several popular regularizers. This paper studies Wasserstein DRO linear regression, unifying square-root Lasso and adversarial linear regression as important special cases. We prove that many properties of these two special cases carry over to this general method. In particular, we show (i) deterministic and non-asymptotic in-sample error bounds

O(n^{-1/2})

in general and

O(n^{-1})

under design matrix and sparsity conditions; (ii) insensitivity to the noise level, also known as the pivotal property; and (iii) solution equivalences for small and large ambiguity sets. The key proof step is to recast the method into a quadratic form, mimicking adversarial linear regression. We also show that the method can be solved efficiently, and we validate our findings through numerical simulations.

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