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2 papers in the last four weeks, against 1 the four weeks before. 0.0% of all new papers.

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Latest papers 26

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Distributionally robust linear regression through the lens of adversarial training
Sep 30, 2026Elis Stefansson, David Vävinggren, Antônio H. RibeiroDistributionally-Robust Optimization Robust Optimization
Uppsala University, Sweden · Science for Life Laboratory, Sweden
Noise2Noise Revisited: Training Pair Distributions Dominate Loss Choice in Self-Supervised Denoising
Sep 15, 2026Dingyan Shang, Zhenyu Xu, Youting Wang +2Noise-Aware Loss Function
Independent Researcher · Frisco, USA · Fulshear, USA +3
High-dimensional networks and mean squared error for possibly misspecified models
Aug 13, 2026Lourens WaldorpLasso High-Dimensional
University of Amsterdam, Nieuwe Achtergracht 129-B, 1018 NP, the Netherlands
Automatic Statistical Test for Rationally Expressible Algorithms by Selective Inference, with Applications to Feature Selection
Aug 5, 2026Teruyuki Katsuoka, Tomohiro Shiraishi, Shuichi Nishino +1Feature Selection Two-Sample Testing
Nagoya University · RIKEN
GLOBE: Trajectory-Aligned Gradient Matching with Structured SparseOptimization for Coreset Selection
Aug 3, 2026Hetian Liu, Jin Cui, Mengcheng Shi +4Geometry-Aware Uncertainty Coresets Adaptive Sampling
State Key Laboratory of Human-Machine Hybrid Augmented Intelligence, and Institute of Artificial Intelligence and Robotics, Xi’an Jiaotong University · School of Software Engineering, Xi’an Jiaotong University · School of Computer Science and Technology, Xi’an Jiaotong University
Causal-TS: A Python Library for Causal Discovery in High-Dimensional and Nonstationary Time Series
Jul 27, 2026Mohammad FesangharyCausal Discovery Methods Non-Stationarity
Bloomberg LP, New York, NY, USA
msPCA: An R Package for Sparse PCA with Multiple Components
Jul 6, 2026Ryan Cory-Wright, Jean PauphiletPrincipal Component Analysis Feature Selection
Analytics, Marketing and Operations Imperial Business School · Management Science and Operations London Business School
Adversarial LassoNet: Robust Feature Selection via Stability-Driven Sparse Learning
Jul 4, 2026Zhen Huang, Peicheng Xu, Junbiao Pang +1Feature Selection Adversarial Training
Beijing University of Technology · The First Affiliated Hospital of Zhejiang University School of Medicine
Exact Schur-Sylvester Dimensionality Reductions for Non-Smooth Stochastic Complexity and Manifold Sampling
Jun 22, 2026Trenton Lau, Gary P. T. ChoiMaximum Likelihood Optimal Sample Complexity
Department of Mathematics, The Chinese University of Hong Kong
Scalable Circuit Learning for Interpreting Large Language Models
Jun 15, 2026Naiyu Yin, Dennis Wei, Tian Gao +3Mechanistic Interpretability Circuits
Lehigh University, Mathematics Department, Bethlehem, USA · IBM Research, Yorktown Heights, USA.
Machine-Learning Emulation of Satellite Greenhouse Gas Retrievals: Stability over Time
Jun 8, 2026Nugzar Gognadze, Motonobu Kanagawa, Yu Someya +1Emulators Satellite Data
Data Science Department, EURECOM, 450 Route des Chappes, Biot, 06410, France
LLM Sparsity Prior for Robust Feature Selection
May 21, 2026Caleb Skinner, Yihan Guo, Meng LiLasso Feature Selection
Department of Statistics, Rice University, Houston, TX 77005 · Department of Computer Science, Rice University, Houston, TX 77005
A Unified Framework for Structure-Aware Clustering and Heterogeneous Causal Graph Learning
May 19, 2026Honglin Du, Muxuan Liang, Xiang ZhongDirected Acyclic Graph Graph Representation Learning
Department of Industrial and Systems Engineering, University of Florida, Gainesville, FL 32611, USA · Department of Biostatistics, MD Anderson Cancer Center, Houston, TX 77030, USA
Estimating Subgraph Importance with Structural Prior Domain Knowledge
May 12, 2026Changhyun Kim, Seunghwan An, Jong-June JeonSubgraphs Graph Neural Networks
University of Seoul, Seoul, South Korea · Incheon National University, Incheon, South Korea
EqOD: Symmetry-Informed Stability Selection for PDE Identification
May 12, 2026Gnankan Landry Regis N'guessan, Bum Jun KimPartial Differential Equations System Identification
1Axiom Research Group · Department of Applied Mathematics and Computational Science, NM-AIST, Tanzania · 3African Institute for Mathematical Sciences (AIMS), Research and Innovation Centre, Rwanda +1
Price of Quality: Sufficient Conditions for Sparse Recovery using Mixed-Quality Data
May 11, 2026Youssef Chaabouni, David GamarnikInformation-Theoretic Limits Sparsity
Operations Research Center Massachusetts Institute of Technology Cambridge, MA 02139, USA
Predictive Radiomics for Evaluation of Cancer Immune SignaturE in Glioblastoma: the PRECISE-GBM study
May 11, 2026Prajwal Ghimire, Junjie Li, Liu Yaou +2Radiomics Glioma
School of Biomedical Engineering & Imaging Sciences, King’s College London, UK · Department of Neurosurgery, King’s College Hospital, London, UK · Department of Neuroradiology, Beijing Tiantan Hospital, Beijing, China +1
Stabilizing Private LASSO under Heterogeneous Covariates via Anisotropic Objective Perturbation
May 2, 2026Haruka Tanzawa, Ayaka SakataLasso Covariates
Department of Information Science · RIKEN center for AIP
Adaptive Norm-Based Regularization for Neural Networks
Apr 30, 2026Muhammad Qasim, Farrukh JavedRegularization Lasso
Department of Statistics, Lund University, Lund, Sweden
Linear Models, Variable Selection, Artificial Intelligence
Apr 29, 2026By Riyadh Alrawkan, Edward Boone, Ryad Ghanam +1Model Selection Lasso
1Statistical Sciences and Operations Research, Virginia Commonwealth University, Richmond, Virginia, USA · 2Liberal Arts and Sciences, Virginia Commonwealth University School of the Arts in Qatar, Doha, Qatar · 3Research School of Finance, Actuarial Studies and Statistics, Australian National University, Canberra, AUS
A proposal for PU classification under Non-SCAR using clustering and logistic model
Apr 18, 2026Konrad Furmanczyk, Kacper PaczutkowskiClustering Positive--Unlabeled Learning
Institute of Information Technology, Warsaw University of Life Sciences, Warsaw, Poland
L1 Regularization Paths in Linear Models by Parametric Gaussian Message Passing
Apr 18, 2026Yun-Peng Li, Hans-Andrea LoeligerLasso Regularization
ETH Zürich, Switzerland
Survival of the fittest Cox model: Pivotal variable selection for time-to-event data
Oct 22, 2025Maxime van Cutsem, Sylvain SardyTime-To-Event Data Survival Analysis
Department of Mathematics, University of Geneva
Formal Bayesian Transfer Learning via the Total Risk Prior
Jul 31, 2025Nathan Wycoff, Ali Arab, Lisa O. SinghBayesian Neural Networks Empirical Risk Minimization
Department of Mathematics and Statistics, University of Massachusetts Amherst · Department of Mathematics and Statistics, Georgetown University and · Department of Computer Science, Georgetown University
Structure-Aware Compound-Protein Affinity Prediction via Graph Neural Networks with Group Lasso Regularization
Jul 4, 2025Zanyu Shi, Yang Wang, Pathum M. Weerawarna +4Drug Design Drug Discovery
Department of Biostatistics & Health Data Science, Indiana University Fairbanks School of Public Health, Indianapolis, 46202 IN, USA. · Department of Computer Science, Luddy School of Informatics, Computing, and Engineering, Indiana University Bloomington, Bloomington, 47405 IN, USA. · Division of Clinical Pharmacology, Indiana University School of Medicine, Indianapolis, 46202 IN, USA. +2
Regularized Estimation and Feature Selection in Mixtures of Generalized Linear Experts
Jul 14, 2019Thin Nguyen-Van, Faicel Chamroukhi, Ha Hoang Van +1Lasso Mixture Models
Faculty of Mathematics and Computer Science, University of Science, Ho Chi Minh City, Vietnam · Vietnam National University, Ho Chi Minh City, Vietnam · IRT SystemX, 91120 Palaiseau, France +1

Sep 30, 2026, stat.MLJ/K move · Enter open · S save

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

Distributionally-Robust Optimization Robust Optimization Lasso Adversarial Training

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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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Latest papers 26