Expectation-Maximization

Also known as EM

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Overview

Expectation-Maximization (EM) estimates models with hidden variables by alternating an E-step, which infers latent-variable distributions, and an M-step, which updates model parameters. Its variants include generalized and gradient EM, stochastic and online EM, and variational EM, which trades exact inference for scalability. These methods are used in mixture models, hidden Markov models, incomplete-data imputation, and imaging, among other settings. A central challenge is that EM can depend on initialization and converge to local optima; stronger guarantees often require conditions such as component separation, while faster or approximate updates raise questions about accuracy and stability.

Latest papers

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27 papers4 papers in the last four weeks, up 33% on the four weeks before. 0.0% of all new papers.

  1. Robust Covariance Estimation for Entry-wise Contaminated and Partially Observed Data via Expectation Maximization Algorithm

    Oct 15, 2026Matys Savidan, Mohammed Nabil El KorsoCovariance EstimationExpectation-Maximization

    ENS Paris-Saclay, France · Paris Saclay University, France

  2. Neural expectation-maximization and meta-models

    Oct 8, 2026Zhuolin FuTransformerTransformer Attention

  3. Is d\sqrt{d} Separation Necessary for Gradient EM to Learn Gaussian Mixtures in High Dimensions?

    Oct 6, 2026Yiran Zhang, Mo Zhou, Weihang Xu +2Gaussian Mixture ModelsExpectation-Maximization

    University of California, Berkeley · University of Washington · Amazon, Inc.

  4. Joint Estimation of Common-Slope Decay Rates and Spatial Amplitudes Using Parameterized Nonnegative Matrix Factorization

    Oct 4, 2026Jeremy B. Bai, Filip Elvander, Sebastian J. SchlechtNon-Negative Matrix FactorizationExpectation-Maximization

    Multimedia Comms. & Signal Proc., Friedrich-Alexander-Universität Erlangen-Nürnberg, Germany · Dept. of Information and Communications Engineering, Aalto University, Finland

  5. Statistical Rates for Entropic Optimal Transport in the Discrete to SubGaussian Regime

    Sep 22, 2026Tomas Gonzalez, Gonzalo MenaExpectation-MaximizationOptimal Transport

    Department of Machine Learning, Carnegie Mellon University · Department of Statistics & Data Science, Carnegie Mellon University

  6. Impute-EM: Native Mixed-State Diffusion Models for Heterogeneous Data Imputation

    Sep 14, 2026Sergei Kholkin, Kirill Sokolov, Dmitry Baranchuk +2Incomplete Data ImputationExpectation-Maximization

    Applied AI Institute · Applied AI Institute, Moscow State University · Yandex Research +1

  7. Connecting Score Matching, Maximum Likelihood, and Expectation-Maximization in Mixed Linear Regression

    Sep 4, 2026Zhankun Luo, Abolfazl HashemiScore MatchingMixed-Effects Models

    School of Electrical and Computer Engineering Purdue University, West Lafayette, IN, USA

  8. Structure Aware Neural Architecture Search for Mixture of Experts

    Aug 30, 2026Petr Babkin, Oleg BakhteevExpectation-MaximizationNeural Architecture Search

  9. Estimation, Prediction, and Assortment Optimization for Markov Chain Choice Models with Panel Data

    Jul 10, 2026Yalcin Akcay, Gerardo Berbeglia, Young-San LinExpectation-MaximizationDiscrete Choice Modeling

    Melbourne Business School

  10. Stochastic Expectation Maximization for Robust State-Space Radio Interferometric Imaging

    Jun 22, 2026Nawel Arab, Mohammed Nabil El Korso, Isabelle Vin +1Image ReconstructionExpectation-Maximization

    †SATIE, ENS Paris-Saclay, Universit´e Paris-Saclay, France · ‡L2S, CentraleSupelec, Universit´e Paris-Saclay, France

  11. EM-NeSy: Expectation Maximization for Neurosymbolic Learning

    Jun 12, 2026Annegret Seibt, Luc De Raedt, Giuseppe MarraSymbolic ReasoningExpectation-Maximization

    Department of Computer Science KU Leuven

  12. libhmm: A Modern C++20 Library for Hidden Markov Models with Correct MLE Emission M-Steps

    May 28, 2026Gary WolfmanParameter EstimationMarkov Models

    Independent Researcher

  13. Higher-Order Equilibrium Tracking for EM-Compressible Online Estimation

    May 9, 2026ZhiMing Li, Yue SongLatent Variable ModelsStochastic Approximation

    School of Computer Science and Technology TianJin University Tianjin 300350, China · College of AI Tsinghua University Beijing 100084, China

  14. Online Generalised Predictive Coding

    May 4, 2026Mehran H. Z. Bazargani, Szymon Urbas, Adeel Razi +2Parameter EstimationLatent Dynamics Modeling

    Insight Research Ireland Centre, UCD, Dublin, Ireland. · UCD School of Mathematics and Statistics, UCD, Belfield, Dublin, Ireland. · Turner Institute for Brain and Mental Health, School of Psychological Sciences, Monash University, Melbourne, Victoria, Australia. +5

  15. Fitting Large Nonlinear Mixed Effects Models Using Variational Expectation Maximization

    Apr 28, 2026Mohamed Tarek, Pedro AfonsoExpectation-MaximizationVariational Inference

    PumasAI, USA

  16. Fractionally Supervised Classification with Maxima Nominated Samples

    Apr 28, 2026Mohammad Jafari Jozani, Jingyu WangLatent Variable ModelsExpectation-Maximization

    Department of Statistics, University of Manitoba, Winnipeg, MB, Canada · RBC Capital Markets, Toronto, Canada

  17. Expectation Maximization (EM) Converges for General Agnostic Mixtures

    Apr 7, 2026Avishek GhoshExpectation-MaximizationData Mixture Optimization

    Department of Computer Science and Engineering, IIT Bombay, Mumbai, India

  18. GEM: Guided Expectation-Maximization for Behavior-Normalized Candidate Action Selection in Offline RL

    Mar 24, 2026Haoyu Wang, Jingcheng Wang, Shunyu Wu +1Offline RLExpectation-Maximization

    School of Automation and Intelligent Sensing, Shanghai Jiao Tong University, Shanghai, China

  19. Deep EM with Hierarchical Latent Label Modelling for Multi-Site Prostate Lesion Segmentation

    Mar 15, 2026Wen Yan, Yipei Wang, Shiqi Huang +5Medical Image SegmentationLearning with Noisy Labels

    UCL Hawkes Institute; Department of Medical Physics and Biomedical Engineering, University College London, Gower St, London WC1E 6BT, U.K. · Centre for Medical Imaging, Division of Medicine, University College London, Gower St, London WC1E 6BT, U.K. · Division of Surgery and Interventional Science, University College London, 10 Pond St, London NW3 2PS, U.K. +2

  20. Momentum SVGD-EM for Accelerated Maximum Marginal Likelihood Estimation

    Mar 9, 2026Adam Rozzio, Rafael Athanasiades, O. Deniz AkyildizMaximum Marginal Likelihood EstimationExpectation-Maximization

    Department of Mathematics, Ecole Normale Supérieure Paris-Saclay, France · Department of Mathematics, Imperial College London, UK

  21. Gradient Descent as Implicit EM in Distance-Based Neural Models

    Dec 31, 2025Alan OurslandExpectation-MaximizationNeural Network Training Dynamics

  22. Differentiable Expectation-Maximisation and Applications to Gaussian Mixture Model Optimal Transport

    Sep 2, 2025Samuel Boïté, Eloi Tanguy, Julie Delon +2Gaussian Mixture ModelsExpectation-Maximization

    Université Paris Cité, CNRS, MAP5, F-75006 Paris, France · Centre Borelli, CNRS and ENS Paris-Saclay, F-91190 Gif-sur-Yvette, France · CMAP, CNRS, Ecole Polytechnique, Institut Polytechnique de Paris

  23. The Fenchel-Young Generalization of Bayesian Principles

    Feb 14, 2025Sophia Sklaviadis, Thomas Moellenhoff, Emtiyaz Khan +2Generalized Bayesian InferenceBayesian Inference

    Instituto de Telecomunicacoes, and Instituto Superior Técnico, Lisbon, Portugal · RIKEN AIP

  24. FastManly: An EM-Gradient Algorithm for Manly Mixture Models

    Oct 1, 2024Katharine M. Clark, Paul D. McNicholasParameter EstimationExpectation-Maximization

    Department of Mathematics & Statistics, Trent University, Ontario, Canada. · Department of Mathematics & Statistics, McMaster University, Ontario, Canada.

  25. The EM-algorithm and the Method of Moments in Softmax Mixture Models

    Sep 16, 2024Xin Bing, Florentina Bunea, Jonathan Niles-Weed +1Expectation-MaximizationDiscrete Choice Modeling

    Department of Statistical Sciences, University of Toronto · Department of Statistics and Data Science, Cornell University · Courant Institute of Mathematical Sciences and Center for Data Science, New York University +1

  26. An Expectation-Maximization Algorithm for Training Clean Diffusion Models from Corrupted Observations

    Jul 1, 2024Weimin Bai, Yifei Wang, Wenzheng Chen +1Expectation-MaximizationDiffusion-Based Inverse Problems

    Academy for Advanced Interdisciplinary Studies, Peking University · College of Future Technology, Peking University · National Biomedical Imaging Center, Peking University +3

  27. Regularized Estimation and Feature Selection in Mixtures of Generalized Linear Experts

    Jul 14, 2019Thin Nguyen-Van, Faicel Chamroukhi, Ha Hoang Van +1Mixture of ExpertsSparse Mixture-of-Experts

    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