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.

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Latest papers 105

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Empirical Variational Autoencoder
Oct 5, 2026Kaede ShioharaVariational Autoencoders Latent Variable Models
The University of Tokyo
Latent Similarity Gaussian Processes: A Theory-Grounded Approach to Personalized Suicide-Risk Forecasting for Clinical Decision-Support
Oct 5, 2026Yaniv Yacoby, Weiwei Pan, Hope Neveux +4Latent Variable Models Suicide Risk Modeling
Wellesley College · Harvard University · Harvard University Harvard Medical School Massachusetts General Hospital
vMF Sentence LDA: A Spherical Topic Model over Sentence Embeddings
Oct 4, 2026Ryotaro Kobayashi, Yuri Murayama, Kiyoshi IzumiLatent Variable Models Topic Modeling
Graduate School of Engineering, The University of Tokyo, Tokyo, Japan
Amortized Structured Stochastic Variational Inference for Gaussian Process Latent Variable Models
Oct 2, 2026Maksym Tretiakov, Sarah Filippi, Vincent Fortuin +3Latent Variable Models Amortized Inference
LMU Munich · Munich Center for Machine Learning (MCML) · Imperial College London +3
Predictive Self-Supervised Learning Provably Identifies Stochastic Signals under Nuisance
Sep 29, 2026Fabian A. Mikulasch, Friedemann ZenkeLatent Variable Models Representation Identifiability
Friedrich Miescher Institute for Biomedical Research, Basel, Switzerland · Faculty of Science, University of Basel, Switzerland
Generative Residual Factorization
Sep 28, 2026Letian Gong, Yuzhou HongRepresentation Collapse Latent Variable Models
Zhejiang University of Science and Technology · Zhejiang Sci-Tech University
SLP-ProbHard: Probabilistic Hard-Constrained Learning via Structural Latent Parameterization
Sep 27, 2026Wondesen Teshome Bekele, Marco D'OriaConstrained Generative Modeling Latent Variable Models
Department of Engineering and Architecture, University of Parma, Parma, Italy · University School for Advanced Studies IUSS Pavia, Pavia, Italy
Towards Identifiable Representations under Misspecified Structure
Sep 27, 2026Yuke Li, Yujia Zheng, Ziyi Chen +2Latent Variable Models Representation Identifiability
University of Maryland College Park, College Park, MD, USA · Carnegie Mellon University, Pittsburgh PA, USA
Learning Prognostic Variables for AI Convective Parameterizations via Symbolic Distillation
Sep 21, 2026Jurij Schönfeld, Tom Beucler, Julien Savre +2Latent Variable Models Climate Modeling
Deutsches Zentrum für Luft- und Raumfahrt, Institut für Physik der Atmosphäre, Oberpfaffenhofen, Germany · University of Bremen, Institute of Environmental Physics (IUP), Bremen, Germany · Faculty of Geosciences and Environment, University of Lausanne, Lausanne, Switzerland +3
Hessian Rank Constraint for Learning Structure of Nonlinear Latent Variable Models
Sep 21, 2026Zijian Li, Ruichu Cai, Feng Xie +7Latent Variable Models Causal Discovery
Mohamed bin Zayed University of Artificial Intelligence, Abu Dhabi, UAE · Carnegie Mellon University, Pittsburgh, PA, USA · Guangdong University of Technology, Guangzhou, China +3
A Closed-Form Estimator and Diagnostic Battery for Anchor-Judge Error Correlation, Under a Single-Common-Factor Model
Sep 8, 2026Veerendra Kumar SunkavalliLatent Variable Models LLM-as-a-Judge
Independent Researcher
Towards unsupervised representation learning for quantum data: quantum models with inference and generation
Aug 31, 2026Robin Lorenz, Eric Brunner, Marcello BenedettiLatent Variable Models Quantum Generative Modeling
Quantinuum, London, United Kingdom
When Can We Work in Embedding Space? What Text Embeddings Preserve
Aug 31, 2026Simon FreyaldenhovenText Embeddings Latent Variable Models
Federal Reserve Bank of Philadelphia
A Deep Latent Variable Framework for Jointly Modeling Missingness, Measurement Error, and Heterogeneity
Aug 30, 2026Yasin Khadem Charvadeh, Grace Y. Yi, Mithat Gönen +1Variational Autoencoders Incomplete Data Imputation
Department of Radiology and Diagnostic Imaging, University of Alberta, Edmonton, Alberta, Canada · Department of Statistical and Actuarial Sciences, Department of Computer Science, University of Western Ontario, Ontario, Canada · Department of Epidemiology & Biostatistics, Memorial Sloan Kettering Cancer Center, New York, New York, USA +1
Debiased Inference for AI-Generated Data without Gold-Standard Labels: Identification via Multiple Imperfect Measurements
Aug 18, 2026Naoki Egami, Sooahn ShinSemiparametric Inference Latent Variable Models
Department of Political Science and Statistics and Data Science Center, Massachusetts Institute of Technology, Cambridge, MA 02139 · Department of Political Science, Massachusetts Institute of Technology, Cambridge, MA 02139
Latent variable models for simultaneous EOV identification and removal in population-based SHM
Aug 12, 2026M. D. Champneys, M. R. Jones, A. J. Hughes +3Latent Variable Models Structural Health Monitoring
Dynamics Research Group, University of Sheffield, Mappin Street, Sheffield S1 3JD, UK · Centre for Machine Intelligence, University of Sheffield, Mappin Street, Sheffield S1 3JD, UK
When Is a General Factor Distinguishable? Non-Proportionality, Stable Structure, and the Bifactor Decision
Aug 11, 2026Jinsong ChenLatent Variable Models Parameter Identifiability
Faculty of Education, The University of Hong Kong
Marginal Matching Does Not License Factorized Sampling: Auditing Conditional Style Leakage in Factorized Generative Models
Aug 5, 2026Duong Bach, Hai Nguyen Hong, Cuong DoDisentangled Representation Learning Latent Variable Models
Smart Health Center (VISHC), VinUniversity, Ha Noi, Viet Nam
UNVaMP: Neural Knowledge Tracing with Variational Regularization of Latent Knowledge Dynamics
Aug 4, 2026Carson J. Cook, Ahmed J. Zerouali, Anthony Schmidt +3Latent Variable Models Interpretable ML
Amplify Education, Inc.
Conditionally Identifiable Latent-Environment Modeling for Out-of-Distribution Recommendation
Aug 4, 2026Qianqian Wang, Wenwu Gong, Yunshan Li +3Latent Variable Models OOD Generalization
Shenzhen Key Laboratory of Safety and Security for Next Generation of Industrial Internet, Southern University of Science and Technology, Shenzhen, China · Department of Statistics and Data Science, Southern University of Science and Technology, Shenzhen, China · School of Mathematical and Computational Sciences, Massey University, Auckland, New Zealand
DAIF: A Data-Driven Intermediate Fusion Framework for Multimodal Supervised Learning via Approximate Message Passing
Aug 3, 2026Sagnik Nandy, Samriddha Lahiry, Pragya Sur +1Latent Variable Models Multimodal Fusion
Ohio State University · National University of Singapore · Harvard University
Learning Latent Reasoning Traces for Scalar Reward Models End-to-End
Jul 31, 2026Sanwoo Lee, Clive Bai, Hsiu-Yuan Huang +3Reward Modeling LLM Alignment
National Key Laboratory for Multimedia Information Processing, Peking University · School of Computer Science, Peking University · LLM Department, Tencent
Latent-Kernel Discrete Flow Maps for Few-Step Generation
Jul 29, 2026Mansoor Ahmed, Yue-Tsz Fan, Hemanth Venkateswara +1Latent Variable Models Few-Step Diffusion Sampling
Georgia State University, Atlanta, GA, USA · Georgia Institute of Technology, Atlanta, GA, USA
Beyond ICA: Identifiability by Symmetry Breaking
Jul 25, 2026Pengzhou WuLatent Variable Models Representation Identifiability
SANKEN, University of Osaka
RAMP: Recognition parametrisation by Amortised Message Passing
Jul 21, 2026Lior Fox, Kai Biegun, James Heald +3Unsupervised Learning Latent Variable Models
1Gatsby Computational Neuroscience Unit · Centre for Artificial Intelligence University College London
Unveiling Invariant and Transferable Latent Factors Across Heterogeneous Environments via ATLAS
Jul 20, 2026Yihong Gu, Katherine Liao, Tianxi CaiLatent Variable Models Domain Generalization
Harvard University
An efficient adaptive dimension selection algorithm for multidimensional probit graded response models
Jul 20, 2026Yu Zhou, Yincai Tang, Bin Lv +1Latent Variable Models Bayesian Inference
1KLATASDS-MOE 1School of Statistics, East China Normal University, Shanghai, 200062, China · 2Zhiyuan School of Liberal Arts,Beijing Institute of Petrochemical Technology,Beijing 102617, China
Coordinated Disentanglement with Iterative Mode Discovery Under Hidden Correlations
Jul 19, 2026Rong Hu, Ling ChenDisentangled Representation Learning Latent Variable Models
State Key Laboratory of Blockchain and Data Security, Zhejiang University, Hangzhou, China · College of Computer Science and Technology, Zhejiang University, Hangzhou, China
What does a Bayes-filtered transformer believe? A predictive Monte Carlo approach
Jul 19, 2026Afiq Abdillah Effiezal Aswadi, Haotong Ma, Susan WeiTransformer Interpretability Latent Variable Models
Department of Econometrics and Business Statistics Monash University
Partially Correlated Verifier Cascades in LLM Harnesses: Concave Log-Odds, Polynomial Reliability, and Blind-Spot Ceilings
Jul 15, 2026Jiangang HanLatent Variable Models LLM Answer Verification
Independent researcher

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Empirical Variational Autoencoder

Kaede Shiohara

The University of Tokyo

Variational Autoencoders Latent Variable Models Image Generation Autoregressive Generation Audio Generation

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We present Empirical Variational Autoencoder, a general generative framework for continuous-valued (i.e., non-vector-quantized) sequences. EVA is based on the evidence lower bound of the Variational Autoencoder (VAE) but learns autoregressive latent priors empirically from training data, which can be implemented only by an additional single linear layer on top of VAEs. By replacing the conventional standard-Gaussian constraint with the self-predicted priors, EVA significantly alleviates the latent distribution gap between prior and posterior which is typically observed in conventional VAEs, and leads to high-fidelity ancestral sampling for sequential data generation. Extensive experiments on image and sound synthesis demonstrate that EVA achieves competitive generation quality with autoregressive diffusion baselines despite its much faster inference time.

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