Latent Variable Models

Ask about this topic

Overview

Latent variable models represent observed data as arising from unobserved factors, classes, states, or embeddings, helping explain structure when measurements are incomplete, noisy, or high-dimensional. Common approaches include factor analysis and matrix factorization, finite mixtures and topic models, probabilistic graphical models, and deep generative models such as variational autoencoders; they are fit with EM, Bayesian or variational inference, and spectral or gradient-based methods. Applications range from clustering, recommendation, and multimodal integration to causal discovery, time-series forecasting, and personalized clinical prediction. A central challenge is identifiability: different hidden representations can explain the same observations, so work seeks assumptions and diagnostics that make latent structure recoverable while balancing flexibility, interpretability, computation, and calibrated uncertainty.

Latest papers

CardsList

153 papers14 papers in the last four weeks, level with the four weeks before. 0.1% of all new papers.

  1. Automatic Laplace Collapsed Sampling: Scalable Marginalisation of Latent Parameters via Automatic Differentiation

    Mar 27, 2026Toby Lovick, David Yallup, Will HandleyBayesian InferenceLatent Variable Models

    Institute of Astronomy, University of Cambridge · Kavli Institute for Cosmology, University of Cambridge · Institute of Astronomy & Kavli Institute for Cosmology, University of Cambridge

  2. Conditionally Identifiable Latent Representation for Multivariate Time Series with Structural Dynamics

    Mar 24, 2026Minkey Chang, Jae-Young KimLatent Variable ModelsLatent Dynamics Modeling

  3. Rule-State Inference (RSI): A Bayesian Framework for Compliance Monitoring in Rule-Governed Domains

    Mar 23, 2026Abdou-Raouf AtarmlaAI Regulatory ComplianceBayesian Inference

    Institut National des Postes et Télécommunications (INPT), Rabat, Morocco · Togo DataLab, Ministry of Digital Economy, Lomé, Togo

  4. Var-JEPA: A Variational Formulation of the Joint-Embedding Predictive Architecture - Bridging Predictive and Generative Self-Supervised Learning

    Mar 20, 2026Moritz Gögl, Christopher YauJoint-Embedding Predictive ArchitectureSelf-Supervised Learning

    University of Oxford, UK · Health Data Research UK

  5. Hybrid topic modelling for computational close reading: Mapping narrative themes in Pushkin's Evgenij Onegin

    Mar 20, 2026Angelo Maria SabatiniComputational Literary StudiesTopic Modeling

    The BioRobotics Institute, Scuola Superiore Sant’Anna Pisa, Italy

  6. Hierarchical Latent Structure Learning through Online Inference

    Mar 19, 2026Ines Aitsahalia, Kiyohito IigayaHierarchical Representation LearningLatent Variable Models

    Center for Theoretical Neuroscience and Zuckerman Institute, Columbia University, New York, NY 10027 · Department of Psychiatry, Columbia University Irving Medical Center, New York, NY 10032 · New York State Psychiatric Institute, New York, NY 10032 +1

  7. Exploring the Dimensions of a Variational Neuron

    Mar 14, 2026Yves RuffenachLatent Variable ModelsVariational Inference

    Conservatoire National des Arts et Métiers

  8. I Know What I Don't Know: Latent Posterior Factor Models for Multi-Evidence Probabilistic Reasoning

    Mar 13, 2026Aliyu Agboola AlegeLatent Variable ModelsUncertainty Calibration

    Epalea

  9. Probabilistic Joint and Individual Variation Explained (ProJIVE) for Data Integration

    Mar 12, 2026Raphiel J. Murden, Ganzhong Tian, Deqiang Qiu +1Latent Variable ModelsPrincipal Component Analysis

    Department of Biostatistics and Bioinformatics, Rollins School of Public Health, Emory University, 1518 Clifton Rd NE, Atlanta, GA 30322, USA · Department of Radiology and Imaging Sciences, Emory University School of Medicine, 1364 Clifton Rd NE, Atlanta, GA 30322, USA

  10. Slack More, Predict Better: Proximal Relaxation for Probabilistic Latent Variable Model-based Soft Sensors

    Mar 12, 2026Zehua Zou, Yiran Ma, Yulong Zhang +5Latent Variable ModelsVariational Inference

    the Hangzhou International Innovation Institute, Beihang University, Hangzhou 311115, China · the State Key Laboratory of Industrial Control Technology, College of Control science and Engineering, Zhejiang University, Hangzhou 310027, Zhejiang, China · the College of Electrical Engineering and Automation, Fuzhou University, Fuzhou 350108, China +4

  11. Beyond ReinMax: Low-Variance Gradient Estimators for Discrete Latent Variables

    Mar 9, 2026Daniel Wang, Thang D. BuiStraight-Through EstimatorVariance Reduction

    School of Computing, College of Systems and Society, Australian National University

  12. Random Dot Product Graphs as Dynamical Systems: Limitations and Opportunities

    Mar 5, 2026Giulio Valentino Dalla RivaLatent Dynamics ModelingDynamic Graph Learning

  13. Probabilistic Learning and Generation in Deep Sequence Models

    Mar 1, 2026Wenlong ChenBayesian InferenceSequence Modeling

    Imperial College London Department of Computing

  14. Hierarchical Action Learning for Weakly-Supervised Action Segmentation

    Feb 27, 2026Junxian Huang, Ruichu Cai, Hao Zhu +5Temporal Action SegmentationLatent Action Learning

    Guangdong University of Technology · Carnegie Mellon University · Mohamed bin Zayed University of Artificial Intelligence +1

  15. Goodness-of-Fit Tests for Latent Class Models with Ordinal Categorical Data

    Feb 25, 2026Huan QingLatent Variable Models

    School of Economics and Finance, Chongqing University of Technology, Chongqing, 400054, China

  16. Fair Model-based Clustering

    Feb 25, 2026Jinwon Park, Kunwoong Kim, Jihu Lee +1ClusteringGroup Fairness

    Graduate School of Data Science, Seoul National University · Department of Statistics, Seoul National University

  17. Characterizing the Predictive Impact of Modalities with Supervised Latent-Variable Modeling

    Feb 19, 2026Divyam Madaan, Sumit Chopra, Kyunghyun ChoMissing-Modality LearningMultimodal Learning

    New York University. · New York University Grossman School of Medicine · Genentech +1

  18. Knowledge-Embedded Latent Projection for Robust Representation Learning

    Feb 18, 2026Weijing Tang, Ming Yuan, Zongqi Xia +1Representation LearningLatent Variable Models

    Department of Statistics and Data Science, Carnegie Mellon University · Department of Statistics, Columbia University · Department of Neurology, University of Pittsburgh +1

  19. Quantifying construct validity in large language model evaluations

    Feb 17, 2026Ryan Othniel KearnsLLM EvaluationLatent Variable Models

    St Catherine’s College

  20. Traceable Latent Variable Discovery Based on Multi-Agent Collaboration

    Feb 16, 2026Huaming Du, Tao Hu, Yijie Huang +5Causal Discovery with Latent ConfoundingCausal Structure Learning

    Southwestern University of Finance and Economics, Chengdu, Sichuan, China · Hunan University of Technology and Business, Xiangjiang Laboratory, Hunan, China · Emory University, Atlanta, Georgia, United States

  21. Hierarchical Latent Structures in Data Generation Process Unify Mechanistic Phenomena across Scale

    Feb 4, 2026Jonas Rohweder, Subhabrata Dutta, Iryna GurevychTransformer InterpretabilityLatent Variable Models

    UKP Lab, TU Darmstadt

  22. LLM surprisal is necessary but not sufficient to capture English garden-path effects: Evidence from joint latent modeling of reading paradigms

    Feb 4, 2026Dario Paape, Tal Linzen, Shravan VasishthLatent Variable ModelsCognitive Modeling

    Department of Linguistics, University of Potsdam · Department Linguistik, Universität Potsdam, Karl-Liebknecht-Straße 24–25, D-14476, Germany. · Center for Data Science/Department of Linguistics, New York University

  23. Nonnegative matrix factorizations and related compositional models: Equivalence, identifiability, and an application on the grain-size analysis of sediments

    Dec 25, 2025Qianqian Qi, Peter G. M. van der Heijden, Maarten A. PrinsLatent Variable ModelsParameter Identifiability

    Hangzhou Dianzi University, China · Utrecht University, the Netherlands · University of Southampton, UK +1

  24. Emergence of Nonequilibrium Latent Cycles in Unsupervised Generative Modeling

    Dec 12, 2025Marco Baiesi, Alberto RossoLatent Variable ModelsLatent Dynamics Modeling

    Department of Physics and Astronomy, University of Padova, Via Marzolo 8, I-35131 Padova, Italy · INFN, Sezione di Padova, Via Marzolo 8, I-35131 Padova, Italy · LPTMS, CNRS, Universit´e Paris-Saclay, 91405, Orsay, France

  25. Latency-Response Theory Model: Evaluating Large Language Models via Response Accuracy and Chain-of-Thought Length

    Dec 7, 2025Zhiyu Xu, Jia Liu, Yixin Wang +1LLM EvaluationLatent Variable Models

    Department of Statistics, Columbia University. · Department of Statistics, University of Michigan

  26. Improving Forecasts of Suicide Attempts for Patients with Little Data

    Nov 22, 2025Genesis Hang, Annie Chen, Hope Neveux +2Latent Variable ModelsGaussian Processes

    Wellesley College · Harvard University

  27. Learning Latent Energy-Based Models via Interacting Particle Langevin Dynamics

    Oct 14, 2025Joanna Marks, Tim Y. J. Wang, O. Deniz AkyildizParameter EstimationLatent Variable Models

    Department of Mathematics, Imperial College London

  28. Variational Learning of Disentangled Representations

    Jun 20, 2025Yuli Slavutsky, Ozgur Beker, David Blei +1Variational AutoencodersDisentangled Representation Learning

    Department of Statistics, Columbia University, New York City, USA · Irving Institute for Cancer Dynamics, Columbia University, New York City, USA · Department of Computer Science, Columbia University, New York City, USA +1

  29. Scalable Krylov Subspace Methods for Generalized Mixed-Effects Models with Crossed Random Effects

    May 14, 2025Pascal Kündig, Fabio SigristLatent Variable ModelsKrylov Subspace Methods

    Lucerne University of Applied Sciences and Arts · University of Basel · Seminar for Statistics, ETH Zurich

  30. The observational partial order of causal structures with latent variables

    Feb 11, 2025Marina Maciel Ansanelli, Elie Wolfe, Robert W. SpekkensLatent Variable ModelsCausal Discovery

    Perimeter Institute for Theoretical Physics, 31 Caroline Street North, Waterloo, Ontario Canada N2L 2Y5 · Department of Physics and Astronomy, University of Waterloo, Waterloo, Ontario, Canada, N2L 3G1