Logistic Regression

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Overview

Logistic regression predicts binary or multiclass outcomes by mapping input features to probabilities, making its coefficients relatively interpretable and the method useful in fields such as medicine, credit, and text classification. Research studies how to fit these models with gradient descent and stochastic or federated optimization, regularization such as L1, and extensions for missing, noisy, private, or externally supplied data. Theory examines finite-sample estimation and training dynamics, including step-size effects, oscillations, max-margin behavior on separable data, and class imbalance; applications also focus on calibration, fairness, and label noise. A central tension is that simple models can be easier to interpret and deploy but may miss nonlinear patterns, while reliable performance with imperfect data and practical solver choices remains challenging.

Latest papers

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38 papers8 papers in the last four weeks, up 100% on the four weeks before. 0.0% of all new papers.

  1. Gradient descent reaches arbitrary accuracy in a fixed number of large steps on multiclass logistic regression

    Oct 19, 2026Andreea-Alexandra Musat, Nicolas BoumalGradient DescentLogistic Regression

    École Polytechnique Fédérale de Lausanne (EPFL), Institute of Mathematics

  2. Gaussian Approximation of Besov Functions for Logistic Classification on Manifolds

    Oct 19, 2026Guanhang Lei, Lei Shi, Xiaoming YuanClassificationStatistical Learning Theory

    Department of Mathematics, The University of Hong Kong, Hong Kong, China · School of Mathematical Sciences, Shanghai Key Laboratory for Contemporary Applied Mathematics, Center for Applied Mathematics, Fudan University, Shanghai, China

  3. Beyond Second-Order Information: Paid Logistic Regression with Noisy Features

    Oct 9, 2026Sanghwa Kim, Kyoungseok Jang, Se-Young YunOnline LearningLogistic Regression

    Kim Jaechul Graduate School of AI, KAIST Seoul 02455, Republic of Korea · Department of AI, Chung-Ang University Soeul 06974, Republic of Korea

  4. Assumption-lean logistic regression with missing covariates

    Oct 5, 2026Jyotishka Ray Choudhury, Kabir Aladin Verchand, Richard J. Samworth +1Logistic RegressionLearning with Missing Data

    School of Industrial and Systems Engineering, Georgia Institute of Technology · Department of Data Sciences and Operations, University of Southern California · Statistical Laboratory, University of Cambridge +1

  5. Improved Convergence of Large Stepsize Gradient Descent for Logistic Regression

    Oct 5, 2026Xiaochuan Gong, Ang LiLogistic RegressionGradient Descent

    University of Maryland, College Park

  6. Large Stepsizes Federated Learning on Logistic Regression with Linearly Separable Data: The Case of Heterogeneous Devices

    Oct 5, 2026Hok Fong Wong, Hoi-To Wai, Chung-Yiu YauLogistic RegressionFederated Learning Aggregation

    Department of CSE, The Chinese University of Hong Kong, Hong Kong SAR · Department of SEEM, The Chinese University of Hong Kong, Hong Kong SAR · Department of ECE, University of Minnesota, Minnesota, USA

  7. Ideal Paths for Approximating Logistic Gradient Descent Trajectories at Large Initialization

    Oct 2, 2026Junjie Xiao, Huiwen JiaLogistic RegressionNeural Network Initialization

    Department of Mathematics, Peking University · Department of Industrial Engineering and Operations Research, University of California, Berkeley

  8. Tight Transition Time Bounds for Separable Logistic Regression at the Edge of Stability

    Oct 1, 2026Haodong Wen, Kaiyue Wen, Jiaye TengEdge of StabilityLogistic Regression

    Stanford University · Shanghai University of Finance and Economics

  9. Simpler Methods Work Better for L1 Penalized Logistic Models and Large Datasets

    Sep 21, 2026Edward Raff, James HoltLogistic Regression

    CrowdStrike, Inc., USA · Univ. of Maryland, Baltimore County, USA

  10. Entropy-aware logistic regression for fusion of large-scale speaker recognition systems

    Sep 20, 2026Pierre-Michel Bousquet, Mickael RouvierLogistic RegressionSpeaker Verification

    LIA, Avignon University, France

  11. PU classification under Non-SCAR: clustering-assisted logistic model with oversampling enhancement

    Sep 13, 2026Konrad Furmańczyk, Kacper PaczutkowskiPositive-Unlabeled LearningWeakly Supervised Learning

    Institute of Information Technology, Warsaw University of Life Sciences, Warsaw, Poland

  12. SGD in Multiclass Logistic Regression: Sequential Learning and Scaling Laws

    Sep 7, 2026Konstantinos Christopher Tsiolis, Denny Wu, Christos Thrampoulidis +1Logistic RegressionMulticlass Classification

    University of Toronto, Vector Institute · New York University, Flatiron Institute · University of British Columbia +1

  13. Scalable Clinical Data Infrastructure and Comparative ML Evaluation for Hospitalisation Risk Prediction in Elderly Patients with Multiple Long-Term Conditions using CPRD

    Aug 29, 2026Asra Aslam, Volodymyr Chapman, Maurice M. O'Connell +9Clinical PredictionClinical Risk Prediction

    School of Information, University of Sheffield, Sheffield, United Kingdom · School of Medicine, Faculty of Medicine and Health, University of Leeds, Leeds, United Kingdom · Division of Informatics, University of Manchester, Manchester, United Kingdom +2

  14. Sharp Minimax Regret for Infinite-Memory Logistic Prediction

    Aug 27, 2026Vaneet AggarwalLogistic RegressionBayesian Mixture Models

    Purdue University

  15. findr\texttt{findr}: Transparent and Fair Credit Risk Decisions through Semi-Structured Regressions

    Aug 25, 2026Victor Medina-Olivares, Stefan Lessmann, Jonathan CrookInterpretable MLCredit Scoring

    University of Edinburgh, Edinburgh, UK · Humboldt-Universität zu Berlin, Berlin, Germany

  16. Non-asymptotic implicit bias of logistic regression at early-stage gradient descent dynamics

    Aug 5, 2026Han BaoLogistic RegressionImplicit Bias

    The Institute of Statistical Mathematics · Tohoku University · RIKEN AIP

  17. Beyond Modern Asymptotics for Log-Likelihood Ratios in Logistic Regression

    Aug 3, 2026Hugo Chardon, Reese Pathak, Nikita ZhivotovskiyLogistic RegressionLikelihood Ratio Testing

    Department of Statistics, University of California, Berkeley · School of Operations Research and Information Engineering (ORIE), Cornell University

  18. Finite-Sample Performance of Gradient Descent in Logistic Regression with Gaussian Design

    Jun 19, 2026Junren Chen, Arya MazumdarLogistic RegressionGradient Descent

    Department of Mathematics, University of Maryland, College Park. · Halıcıoğlu Data Science Institute, UC San Diego.

  19. A Logistic Regression Model to Predict Malaria Severity in Children

    May 17, 2026Mary Opokua Ansong, Asare Yaw Obeng, Samuel King OpokuLogistic RegressionClinical Outcome Prediction

  20. Separating Shortcut Transition from Cross-Family OOD Failure in a Minimal Model

    May 13, 2026Hongmin LiLogistic RegressionOOD Generalization

    School of Life Science and Technology, Institute of Science Tokyo · Department of Computational Biology and Medical Sciences, Graduate School of Frontier Sciences, The University of Tokyo

  21. Hybrid TF--IDF Logistic Regression and MLP Neural Baseline for Indonesian Three-Class Sentiment Analysis on Social Media Text

    May 8, 2026Allya Nurul Islami Pasha, Eka Fidiya Putri, Luluk Muthoharoh +2Social Media AnalysisLogistic Regression

    Faculty of Science · Institut Teknologi Sumatera · Faculty of Industrial Technology

  22. Transformers Efficiently Perform In-Context Logistic Regression via Normalized Gradient Descent

    May 7, 2026Chenyang Zhang, Yuan CaoLogistic RegressionSelf-Attention

    School of Computing & Data Science, The University of Hong Kong

  23. Weak-to-Strong Generalization is Nearly Inevitable (in Linear Models)

    May 7, 2026Scott Geng, Dutch Hansen, Jerry LiLogistic RegressionWeak-to-Strong Generalization

    University of Washington, Seattle.

  24. Networked Information Aggregation for Binary Classification

    May 1, 2026MohammadHossein Bateni, Zahra Hadizadeh, MohammadTaghi Hajiaghayi +2Logistic RegressionBinary Classification

    Google Research, New York City, NY, USA · University of California, Irvine, CA, USA · University of Maryland, College Park, MD, USA

  25. A proposal for PU classification under Non-SCAR using clustering and logistic model

    Apr 18, 2026Konrad Furmanczyk, Kacper PaczutkowskiLogistic RegressionWeakly Supervised Learning

    Institute of Information Technology, Warsaw University of Life Sciences, Warsaw, Poland

  26. Fused Multinomial Logistic Regression Utilizing Summary-Level External Machine-learning Information

    Apr 5, 2026Chi-Shian Dai, Jun ShaoData IntegrationLogistic Regression

    Department of Statistics, National Cheng Kung University, Tainan, 701, Taiwan · Department of Statistics, University of Wisconsin-Madison, Madison, 53706, USA

  27. Efficient Logistic Regression with Mixture of Sigmoids

    Apr 3, 2026Federico Di Gennaro, Saptarshi Chakraborty, Nikita ZhivotovskiyLogistic RegressionOnline Learning

    ETH Zürich · University of Michigan · UC Berkeley

  28. GRMLR: Knowledge-Enhanced Small-Data Learning for Deep-Sea Cold Seep Stage Inference

    Mar 25, 2026Chenxu Zhou, Zelin Liu, Rui Cai +7Small-Data LearningData-Efficient Learning

    Shanghai Jiao Tong University, 200240 Shanghai, China

  29. Amortized Variational Inference for Logistic Regression with Missing Covariates

    Mar 22, 2026M. Cherifi, Aude Sportisse, Xujia Zhu +2Learning with Missing DataLogistic Regression

    Lab. Traitement du signal, Ecole Militaire Polytechnique, BP 17 Bordj El Bahri, Algeria · Lab. d’Informatique de Grenoble, CNRS, France · University of Paris Saclay, CNRS, CentraleSupelec, Laboratoire des signaux et systèmes, 91190, Gif-sur-Yvette, France

  30. Hard labels sampled from sparse targets mislead rotation invariant algorithms

    Mar 21, 2026Avrajit Ghosh, Bin Yu, Manfred Warmuth +1Logistic RegressionSoft-Label Learning

    University of California, Berkeley. · Google Research · Google Deepmind.