Expectation-Maximization

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

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Impute-EM: Native Mixed-State Diffusion Models for Heterogeneous Data Imputation
Sep 14, 2026Sergei Kholkin, Kirill Sokolov, Dmitry Baranchuk +2Imputation Expectation-Maximization
Applied AI Institute · Applied AI Institute, Moscow State University · Yandex Research +1
Structure Aware Neural Architecture Search for Mixture of Experts
Aug 30, 2026Petr Babkin, Oleg BakhteevMixture-Of-Experts Architectures Neural Architecture Search
Encryption-Compatible Clustered Federated Learning via Distributed Expectation-Maximization over Metadata
Jul 30, 2026Michael Ben Ali, Imen Megdiche, André Péninou +1Federated Learning Clustering
UT3, IRIT, CNRS · INU Champollion, ISIS, IRIT, CNRS · UT2J, IRIT, CNRS +1
Equilibrium Training of Energy-Based Models with Parallel Trajectory Tempering
Jul 29, 2026Nicolas Béreux, Aurélien Decelle, Cyril Furtlehner +1Markov Chain Monte Carlo Expectation-Maximization
Université Paris-Saclay, CNRS, INRIA, LISN, 91190 Gif-sur-Yvette, France · Escuela Técnica Superior de Ingenieros Industriales, Universidad Politécnica de Madrid, 28006 Madrid, Spain · Departamento de Física Teórica & IPARCOS, Universidad Complutense de Madrid, 28040 Madrid, Spain.
Cluster-Weighted EDMD
Jul 14, 2026Lorenzo Tomaz, Judd Rosenblatt, Flavio Kicis +2Dynamic Mode Decomposition Koopman Operator
AE Studio
Simplifying Flow Matching Transformations with Low-Rank Mixture Models
Jun 29, 2026Liam A. Kruse, Houjun Liu, Alexandros E. Tzikas +2Normalizing Flows Conditional Flow Matching
Stanford Intelligent Systems Laboratory in the Department of Aeronautics and Astronautics at Stanford University, Stanford, CA 94305, USA · Industrial and Systems Engineering Department at King Fahd University of Petroleum and Minerals (KFUPM), Dhahran, 31261, KSA
Rethinking Training & Inference for Forecasting: Linking Winner-Take-All back to GMMs
Jun 24, 2026Qiyuan Wu, Katie Z Luo, Bharath Hariharan +2Trajectory Forecasting Posterior
Cornell University · Stanford University · Boston University
Stochastic Expectation Maximization for Robust State-Space Radio Interferometric Imaging
Jun 22, 2026Nawel Arab, Mohammed Nabil El Korso, Isabelle Vin +1State Space Models Radio Map Estimation
†SATIE, ENS Paris-Saclay, Universit´e Paris-Saclay, France · ‡L2S, CentraleSupelec, Universit´e Paris-Saclay, France
Cohort Organized Learning: Clustering Through Agreement
Jun 19, 2026Finn Henry O'Shea, Maria Elena MonzaniCluster Expectation-Maximization
SLAC National Accelerator Laboratory, 2575 Sand Hill Road, Menlo Park, CA 94025-7015, USA · Kavli Institute for Particle Astrophysics and Cosmology, Stanford University, Stanford, CA 94305-4085 USA
EM-NeSy: Expectation Maximization for Neurosymbolic Learning
Jun 12, 2026Annegret Seibt, Luc De Raedt, Giuseppe MarraNeuro-Symbolic Framework Expectation-Maximization
Department of Computer Science KU Leuven
Inverse Design of Realizable Metasurface based Absorbers using Improved Conditioning and Diversity Enhanced Progressively Growing GANs
Jun 4, 2026Vineetha Joy, Mohammad Abdullah, Pramit Pal +3Metamaterials Generative Adversarial Network
Centre for Electromagnetics, CSIR-National Aerospace Laboratories, Kodihalli, Bangalore 560017 · Birla Institute of Technology and Science, Pilani, Rajasthan 333031 · Indian Institute of Technology, Bombay, Maharashtra 400076
Minimax-Optimal Policy Regret in Partially Observable Markov Games
Jun 1, 2026Raman AroraPartially Observable Markov Decision Process Mean Field Games
Department of Computer Science, Johns Hopkins University, Baltimore, MD, USA.
Local linear convergence of gradient methods for overparameterized Gaussian mixtures
May 29, 2026Jingxing Wang, Vasileios Charisopoulos, Maryam FazelGaussian Mixture Models Overparameterization
Electrical & Computer Engineering, University of Washington, Seattle, WA · National Institute for Theory and Mathematics in Biology, Chicago, IL · Amazon, Inc.
Fitting Unknown Number of Hyperplanes with Manifold Optimization
May 27, 2026Zhiqin Cheng, Yu Zhan, Mingjin Zhang +2Data Manifold Expectation-Maximization
Department of Computing, The Hong Kong Polytechnic University, Kowloon, Hong Kong · Department of Electronic and Electrical Engineering, Southern University of Science and Technology, ShenZhen, China · Research Institute of Multiple Agents and Embodied Intelligence, Pengcheng Laboratory, ShenZhen, China +1
Missing Pattern Recognized Diffusion Imputation Model for Missing Not At Random
May 25, 2026Gyuwon Sim, Sumin Lee, Heesun Bae +5Imputation Expectation-Maximization
KAIST · Seoul National University
Physics Guided Conditional Diffusion Framework for Generative Inverse Design of Manufacturable Metasurface based Absorbers
May 19, 2026Vineetha Joy, Jamshed Palai, Satwik Sahu +3Metamaterials Generative Inverse Design
Centre for Electromagnetics, CSIR-National Aerospace Laboratories, Kodihalli, Bangalore 560017 · Birla Institute of Technology and Science, Pilani, Rajasthan 333031 · Indian Institute of Technology, Bombay, Maharashtra 400076
An Objective Performance Evaluation of the LSTM Networks in Time Series Classification
May 19, 2026Sooraj Sunil, Balakumar BalasingamTime-Series Classification Cnn-Lstm
Electrical and Computer Engineering University of Windsor Windsor, ON, Canada
Online Generalised Predictive Coding
May 4, 2026Mehran H. Z. Bazargani, Szymon Urbas, Adeel Razi +2Predictive Coding Data Assimilation
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
Physically-Informed Fuzzy Clustering of Vertical Sounding Ionograms
Apr 30, 2026Oleg I. Berngardt, Sergey N. PonomarchukClustering Expectation-Maximization
Fitting Large Nonlinear Mixed Effects Models Using Variational Expectation Maximization
Apr 28, 2026Mohamed Tarek, Pedro AfonsoExpectation-Maximization Generalized Linear Model
PumasAI, USA
Planar Gaussian Splatting with Bilinear Spatial Transformer for Wireless Radiance Field Reconstruction
Apr 17, 2026Jinghan Zhang, Xitao Gong, Qi Wang +2Wireless Radiance Field Gaussian Splatting
Munich Research Center, Huawei Technologies Duesseldorf GmbH, Munich, Germany · Department of Electrical Engineering and Computer Science, Technical University of Berlin, Berlin, Germany
Gradient Descent as Implicit EM in Distance-Based Neural Models
Dec 31, 2025Alan OurslandExpectation-Maximization Gradient Descent
Differentiable Expectation-Maximisation and Applications to Gaussian Mixture Model Optimal Transport
Sep 2, 2025Samuel Boïté, Eloi Tanguy, Julie Delon +2Expectation-Maximization Wasserstein Distance
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
LatentCRS: A Variational EM Framework for Bridging Semantics and Behavior in LLM-based Conversational Recommendation
Mar 12, 2025Guanrong Li, Kuo Tian, Jinnan Qi +4Recommendation Semantic Representations
Nanjing University Nanjing, Jiangsu, China
A stochastic smoothing framework for nonconvex-nonconcave minEmax problems with applications to Wasserstein distributionally robust optimization
Feb 24, 2025Wei Liu, Muhammad Khan, Gabriel Mancino-Ball +1Stochastic Convex Optimization Distributionally-Robust Optimization
Department of Applied Mathematics, Hong Kong Polytechnic University, Hong Kong, China · Institute for Math & AI, Wuhan (IMAI), Wuhan university, China · Department of Mathematical Sciences Rensselaer Polytechnic Institute, Troy, NY, USA
FastManly: An EM-Gradient Algorithm for Manly Mixture Models
Oct 1, 2024Katharine M. Clark, Paul D. McNicholasExpectation-Maximization Mixture Models
Department of Mathematics & Statistics, Trent University, Ontario, Canada. · Department of Mathematics & Statistics, McMaster University, Ontario, Canada.
The EM-algorithm and the Method of Moments in Softmax Mixture Models
Sep 16, 2024Xin Bing, Florentina Bunea, Jonathan Niles-Weed +1Mixture Models Expectation-Maximization
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
An Expectation-Maximization Algorithm for Training Clean Diffusion Models from Corrupted Observations
Jul 1, 2024Weimin Bai, Yifei Wang, Wenzheng Chen +1Diffusion Models Expectation-Maximization
Academy for Advanced Interdisciplinary Studies, Peking University · College of Future Technology, Peking University · National Biomedical Imaging Center, Peking University +3
EM-GANSim: Real-time and Accurate EM Simulation Using Conditional GANs for 3D Indoor Scenes
May 27, 2024Ruichen Wang, Dinesh ManochaEmulators Conditional Gans
Department of Computer Science and the Department of Electrical and Computer Engineering, University of Maryland, College Park, MD 20742 USA

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

Sergei Kholkin, Kirill Sokolov, Dmitry Baranchuk, Evgeny Burnaev+1

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

Imputation Expectation-Maximization Imbalanced Classification Diffusion Models Data Mining

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Missing values are ubiquitous in heterogeneous data mining, where numerical, categorical, and binary variables often coexist. Many imputation methods, especially diffusion-based ones, treat discrete variables through continuous surrogates such as one-hot relaxations rather than modeling them natively. This creates a mismatch between the model state space and the mixed discrete and continuous structure of the data. We propose Impute-EM, an Expectation Maximization style framework that alternates between imputing missing entries with the current model and refitting a diffusion backbone on completed data. We instantiate Impute-EM with native mixed-state diffusion backbones for heterogeneous data, combining Gaussian and masked categorical components without one-hot relaxations. In exact settings, we characterize the update and show that the observed mask-indexed marginals match the targets at the limit, while making explicit that the full data distribution is generally non-identifiable from incomplete observations alone. Empirically, Impute-EM delivers the best distributional fidelity on mixed-type tabular imputation, on which downstream modeling relies, with text imputation serving as a controlled validation of the native discrete backbone.

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