Latent Variable Models

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9 papers in the last four weeks, up 50% on the four weeks before. 0.1% of all new papers.

Jul 13Week of Sep 28

Latest papers 105

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The Hyperspherical Geometry of CLIP Latent Space: A Semantic Mixture Model
Jul 15, 2026Zijie Yu, Gaowen Liu, Ramana Rao Kompella +2Latent Variable Models Hyperspherical Representation Learning
Tsinghua University · Cisco Research · University of Illinois Chicago
The Spectral Structure of Latent Treatment Effects
Jul 12, 2026Hamza Virk, Bijan Mazaheri, Yihren WuCausal Effect Estimation Latent Variable Models
1Dartmouth College · 2Broad Institute of MIT and Harvard · 3Hofstra University
CASL-VAE: Learning Structured Latent Variables from Unpaired Data for Semi-supervised Clustering and Paired Sample Generation
Jul 9, 2026Sai Spandana Chintapalli, Pratik Chaudhari, Christos DavatzikosVariational Autoencoders Contrastive Learning
Center for AI and Data Science for Integrated Diagnostics, University of Pennsylvania · Department of Bioengineering, University of Pennsylvania · Department of Electrical and Systems Engineering, University of Pennsylvania +1
Recovering Latent Structures after Variational Bayesian Variable Selection: Fit Assessment and Factor-Number Selection in Partially Exploratory Factor Analysis
Jul 8, 2026Jinsong Chen, Yi JinLatent Variable Models
Faculty of Education The University of Hong Kong · Department of Curriculum and Instruction The Education University of Hong Kong
FedSPM: Routing-Enabled Federated Learning under Dual Heterogeneity via Semiparametric Mixture
Jul 5, 2026Zijian Wang, Pengfei Li, Guangyu Yang +1Latent Variable Models Adaptive Model Routing
Institute of Statistics and Big Data, Renmin University of China · Department of Statistics and Actuarial Science, University of Waterloo
Poisson-Gamma Modeling of Inter-Relational Dependencies in Dynamic Knowledge Graphs
Jul 3, 2026Nan Fang, Yijun Wang, Hao Liao +1Latent Variable Models Temporal Link Prediction
School of Computing and Information Technology, Great Bay University, Dongguan, Guangdong, China · Tsinghua University, Shenzhen, Guangdong, China · College of Computer Science and Software Engineering, Shenzhen University, Shenzhen, Guangdong, China +1
From Structural Equation Modelling to Double Machine Learning: Robustness Analysis for Survey-Based Research
Jul 1, 2026Ka Ching Chan, Qiana Liu, Sanjib Tiwari +1Double ML Latent Variable Models
School of Business, Law, Humanities and Pathways, University of Southern Queensland, Springfield, 4300, Queensland, Australia
Perspectives on Latent Factor Indeterminacy and its Implications for Data Representation
Jun 27, 2026Carel F. W. PeetersRepresentation Learning Latent Variable Models
Mathematical & Statistical Methods group – Biometris, Wageningen University & Research
RetiSEM: Generalising Causal Models for Fragmented Biomedical Data
Jun 23, 2026Inam Ullah, Imran Razzak, Shoaib JameelLatent Variable Models Structural Causal Models
University of Southampton, Southampton, SO18 1PB, Hampshir, United Kingdom · Mohamed bin Zayed University of Artificial Intelligence, United Arab Emirates
FLFL: Federated Latent Factor Learning for Private Recovery of Spatio-Temporal Signals
Jun 22, 2026Chengjun Yu, Di Wu, Yi He +1Latent Variable Models Privacy-Preserving ML
Southwest University Chongqing, China · College of William & Mary Williamsburg, Virginia, United States · Beihang University Beijing, China
Unsupervised Disentanglement Without Compromises : How Functional Orthogonality Enforces Identifiability
Jun 19, 2026Mathieu Cyrille Simon, Pascal Frossard, Christophe De VleeschouwerUnsupervised Learning Disentangled Representation Learning
1UCLouvain, ICTEAM, Louvain-la-Neuve, Belgium · 2EPFL, LTS4 laboratory, Lausanne, Switzerland.
Shrinkage priors for Bayesian Substitute Confounders
Jun 16, 2026Yordan P. Raykov, Hengrui Luo, Justin D. Strait +1Causal Effect Estimation Latent Variable Models
School of Mathematical Sciences, University of Nottingham, Nottingham, UK · Department of Statistics, Rice University, USA · Lawrence Berkeley National Laboratory, USA +1
Concept Modulation Models: A Unified Framework for Identifiability and Extrapolation
Jun 16, 2026Soheun Yi, Yizhou Lu, Chandler Squires +1Latent Variable Models Generative Modeling
Department of Statistics and Data Science, Carnegie Mellon University · Machine Learning Department, Carnegie Mellon University
Information Gap and Feasibility-Aware Inference in Binomial Logistic Mixtures
Jun 14, 2026Yuta Hayashida, Shonosuke SugasawaLatent Variable Models
Graduate School of Economics, Keio University · Faculty of Economics, Keio University
Structured Nonparametric Variational Inference for Dependent Latent Modeling
Jun 13, 2026Yuda Shao, Zhiling Gu, Shan YuLatent Variable Models Variational Inference
Department of Statistics, University of Virginia · Department of Biostatistics, Yale University
Zero-Inflated Gaussian Distributions Enable Parameter-Space Sparsity in Estimation-of-Distribution Algorithms
Jun 11, 2026Andreas Faust, Sven Nitzsche, Juergen BeckerStochastic Optimization Parameter Estimation
University of Freiburg, Freiburg, Germany · FZI Research Center for Information Technology, Karlsruhe, Germany · Karlsruhe Institute of Technology, Karlsruhe, Germany
Generative Modeling of Bach-Style Symbolic Music: A Comparative Study of Autoregressive, Latent-Variable, and Adversarial Approaches
Jun 11, 2026Dezhi Yu, Kyuil Lee, Yongkang HuangVariational Autoencoders Latent Variable Models
Simultaneous Latent Budget Trees for Stratified Classification
Jun 11, 2026Cristian Buoncompagni, Stefano Pellegrino, Giulia Vannucci +2Latent Variable Models Interpretable ML
Department of Physics, University of Naples Federico II, via Cintia 21, Napoli, 80125, Italy. · Department of Electrical Engineering and Information Technologies, University of Naples Federico II, via Claudio 21, Napoli, 80125, Italy. · Department of Neuroscience, Reproductive Sciences and Odontostomatology, University of Naples Federico II, via Sergio Pansini 5, Napoli, 80131, Italy.
Identifiability Without Gaussianity: Symbolic World Models and Near-Infinite Temporal Consistency
Jun 9, 2026Seth Dobrin, Łukasz ChmielLatent Variable Models Causal Representation Learning
1ARYA Labs, PBC
Machine Learning Methods for Studying Latent Neural Activity Dynamics
Jun 9, 2026Shufeng Kong, Fumei Deng, Xinyi Dong +7Dynamical Systems Latent Variable Models
School of Software Engineering, Sun Yat-sen University, Zhuhai, China · School of Computer Science and Artificial Intelligence, Foshan University, Foshan, China · Department of Ecology and Evolutionary Biology, Cornell University, Ithaca, NY, USA +2
Identifiability and Estimation for Unlabeled Finite Mixtures under Marginal Independence
Jun 6, 2026Takafumi Kanamori, Yushi Hirose, Shohei YamamotoLatent Variable Models Parameter Identifiability
Department of Mathematical and Computing Science, Institute of Science Tokyo · RIKEN Center for Advanced Intelligence Project
Generative Modeling of Discrete Latent Structures via Dynamic Policy Gradients
Jun 5, 2026Stefan Ivanovic, Ge Liu, Mohammed El-KebirLatent Variable Models Policy Gradient Methods
Siebel School of Computing and Data Science, University of Illinois at Urbana-Champaign, IL 61801, USA · Cancer Center at Illinois, University of Illinois Urbana-Champaign, IL 61801, USA
Disentangling Latent Risk Pathways via Bayesian Hypergraph Inference
Jun 4, 2026Shengxian Ding, Haonan Gao, Pangpang Liu +2Latent Variable Models Bayesian Inference
Department of Biostatistics, Yale University, New Haven, USA
Gaussian Process Latent Factor Regression for Low-Data, High-Dimensional Output Problems
Jun 4, 2026Edward T. Stevenson, Eric T. Wolf, Mei Ting Mak +2Latent Variable Models Dimensionality Reduction
University of Cambridge · University of Colorado Boulder · University of Oxford +1
Non-Negative Matrix Factorization for Event Data
Jun 4, 2026Raphaël RomeroTemporal Point Processes Latent Variable Models
Ghent University · Ghent, Belgium
Environment-Robust Representation Learning with Empirical Bayes
Jun 3, 2026Yuli Slavutsky, Matthew Shen, Bohan Wu +1Latent Variable Models Empirical Bayes
Department of Statistics Columbia University New York, NY 10027, USA · Departments of Statistics and Computer Science Columbia University New York, NY 10027, USA
An Ensembled Latent Factor Model via Differential Evolution and Gradient Descent Optimization
Jun 3, 2026Rui Zhang, Jinhang Liu, Wenbo ZhangRepresentation Learning Latent Variable Models
Chongqing Academy of Economics Research Chongqing, China · College of Computer and Information Science, Southwest University Chongqing, China
Network Learning with Semi-relaxed Gromov-Wasserstein
Jun 1, 2026Charles Dufour, Ulysse Naepels, Leonardo V. SantoroGraph Structure Learning Latent Variable Models
EPFL, Institute of Mathematics Lausanne, Switzerland
VLBM: Variational Latent Basis Modeling for OOD Robust Multivariate Time Series Forecasting
Jun 1, 2026Xudong Zhang, Jierui Lei, Jiacheng Li +3Multivariate Time Series Forecasting Latent Variable Models
School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing 100049, China. · Center for Machine Learning Research, Peking University, Beijing, China. · Amap, Alibaba Group, China. +2
Large-scale Uncertainty Quantification for Latent Variable Models Using Subsampling Markov Chain Monte Carlo
May 29, 2026Xiaoyu Wang, Jonathan H. HugginsMarkov Chain Monte Carlo Stochastic Gradient Langevin Dynamics
Department of Mathematics & Statistics, Boston University · Faculty of Computing & Data Sciences, Boston University

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The Hyperspherical Geometry of CLIP Latent Space: A Semantic Mixture Model

Zijie Yu, Gaowen Liu, Ramana Rao Kompella, Philip S. Yu+1

Tsinghua University · Cisco Research · University of Illinois Chicago

Latent Variable Models Hyperspherical Representation Learning Multimodal Embedding Multimodal Model Interpretability Density Estimation

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Contrastive Language-Image Pretraining (CLIP) representations form a semantic embedding space governed by cosine similarity, reflecting an intrinsic hyperspherical geometry. However, existing probabilistic interpretations typically rely on Gaussian assumptions, which fail to capture this directional and multimodal structure. We propose a principled density model for the CLIP latent space based on Mixtures of von Mises-Fisher (MovMF) distributions defined on the unit hypersphere. Using the Expectation-Maximization (EM) algorithm, we efficiently learn a probabilistic model in which each mixture component corresponds to a coherent semantic concept. This formulation yields a closed-form likelihood naturally aligned with hyperspherical geometry, enabling accurate and interpretable density estimation. Empirically, our model significantly improves long-tailed and out-of-distribution detection and provides a natural semantic decomposition, representing each embedding as a sparse probabilistic combination of interpretable concepts. These results suggest that CLIP latent space is more faithfully characterized as a hyperspherical semantic mixture rather than an isotropic Gaussian, establishing a simple and geometrically consistent probabilistic framework for modeling and understanding multimodal representations. Project page is available at https://xiaoyuzhizi.github.io/movmf-clip/.

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