Copula Models

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3 papers in the last four weeks, with none the four weeks before. 0.0% of all new papers.

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MIND: Marginal-Invariant Neural Dependency Diffusion for Mixed-Type Tabular Generation
Sep 30, 2026Pengfei Li, Mohammad KhalilCopula Models Synthetic Tabular Data Generation
Centre for the Science of Learning & Technology (SLATE), University of Bergen, Bergen, Norway
On Generalized Naive Bayes with Continuous Features
Sep 20, 2026Ábrahám Papp, Botond Szilágyi, Edith Alice KovácsCopula Models Probabilistic Graphical Models
Department of Analysis and Operations Research, Institute of Mathematics, Budapest University of Technology and Economics · Department of Chemical and Environmental Process Engineering, Faculty of Chemical Technology and Biotechnology, Budapest University of Technology and Economics
Copula Adapted Directed Acyclic Graph for Cluster Representation of Biomedical Data
Sep 14, 2026Heranga K. Rathnasekara, Norou Diawara, Manar D. SamadCopula Models Unsupervised Clustering
Department of Mathematics and Statistics Old Dominion University, Norfolk, VA 23529, USA · Department of Computer Science North Carolina Agricultural and Technical State University Greensboro, NC 27411, USA
Hierarchical Copula-Gumbel-Top-\texorpdfstring{ $K$ }{K} Routing: Two-Sided Dependence Control for Frozen Mixture-of-Experts at Fixed Per-Token Routing Laws
Jul 25, 2026Richard Yi Da XuCopula Models Expert Load Balancing
Hong Kong Baptist University and TadReamk Limited
TSCoNet: A Two-Stage Copula CNN-LSTM for Uncertainty-Aware Spatio-Temporal Forecasting
Jul 11, 2026Jongwook Kim, Jong-Min KimMultivariate Time Series Forecasting Copula Models
Statistics Discipline, Division of Science and Mathematics, University of Minnesota-Morris
Towards Diverse and Comprehensive Benchmarks for Mutual Information Estimation
Jul 3, 2026Alberto Foresti, Ivan Butakov, Alexander Tolmachev +3Copula Models Benchmark Design
Department of Data Science EURECOM 450 Route des Chappes, 06410 Biot, France · Applied AI Institute Presnenskaya naberezhnaya, 12, 121205 Moscow, Russia · Moscow Independent Research Institute of Artificial Intelligence Bolshaya Cheremushkinskaya Street, 20, Building 4, Premises 3/1, 117218 Moscow, Russia
BSTabDiff: Block-Subunit Diffusion Priors for High-Dimensional Tabular Data Generation
Jun 8, 2026Al Zadid Sultan Bin Habib, Md Younus Ahamed, Prashnna Gyawali +2Copula Models Synthetic Tabular Data Generation
1,2,3,5Lane Department of Computer Science and Electrical Engineering West Virginia University, Morgantown, WV 26506, USA · 4Scientific Computing and Imaging Institute & Department of Biomedical Informatics The University of Utah, Salt Lake City, UT 84112, USA
Identifiable Bayesian Deep Generative Copulas with Unknown Layer Widths for Data with Arbitrary Marginal Distributions
May 26, 2026Joseph Feldman, Yuqi GuCopula Models Latent Variable Models
Department of Statistics and Data Science Washington University in St. Louis · Department of Statistics Columbia University
Valid and Expressive Copulas for Irregular Multivariate Time Series
May 22, 2026Christian Klötergens, Tom Hanika, Lars Schmidt-Thieme +1Irregular Time-Series Modeling Copula Models
Institute of Computer Science University of Hildesheim Hildesheim, Germany
Archimedean Copula Inference via Taylor-Mode AD
May 22, 2026Cambridge Yang, Dongdong LiParameter Estimation Automatic Differentiation
Independent · Harvard Medical School
Probabilistic Multivariate Time Series Forecasting with Diffusion Copulas
May 19, 2026David Huk, Dongshan Wang, Miha BresarMultivariate Time Series Forecasting Copula Models
Department of Statistics The University of Warwick · School of Data Science The Chinese University of Hong Kong, Shenzhen
TabKDE: Simple and Scalable Tabular Data Generation with Kernel Density Estimates
May 17, 2026Meysam Alishahi, Yan Zheng, Junpeng Wang +2Copula Models Synthetic Tabular Data Generation
University of Utah · Visa Research
Causal Algorithmic Recourse: Foundations and Methods
May 12, 2026Drago Plecko, Collin Wang, Elias BareinboimCopula Models Causal Effect Estimation
Department of Statistics & Data Science UCLA Los Angeles, CA 90095, USA · Department of Computer Science Columbia University New York, NY 10027, USA
Dynamic Vine Copulas: Detecting and Quantifying Time-Varying Higher-Order Interactions
May 4, 2026Houman Safaai, Alessandro Marin VargasCopula Models Non-Stationary Time Series
Kempner Institute at Harvard University · Stanford University
Amortized Vine Copulas for High-Dimensional Density and Information Estimation
Apr 22, 2026Houman SafaaiCopula Models Amortized Inference
Kempner Institute for the Study of Natural and Artificial Intelligence at Harvard University

Sep 30, 2026, cs.LGJ/K move · Enter open · S save

MIND: Marginal-Invariant Neural Dependency Diffusion for Mixed-Type Tabular Generation

Pengfei Li, Mohammad Khalil

Centre for the Science of Learning & Technology (SLATE), University of Bergen, Bergen, Norway

Copula Models Synthetic Tabular Data Generation Generative Modeling Conditional Diffusion Models

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This paper proposes MIND, a marginal-invariant neural dependency diffusion model for mixed-type tabular data. MIND does not directly learn the joint distribution in the original heterogeneous feature space. Instead, it first maps different variable types into a unified latent dependency space via column-wise marginal transport. A conditional diffusion model then learns cross-column relationships. Copula-tangent denoising separates known marginal components from learnable dependency residuals. Rank projection during the sampling phase further mitigates marginal shift in reverse diffusion. Experiments across nine diverse tabular benchmarks show that MIND consistently improves marginal fidelity and dependency preservation over existing unified approaches. By explicitly isolating marginal modelling from dependency learning, MIND achieves a strong and stable balance among marginal fidelity, joint dependency preservation, and downstream prediction utility. This work supports separating marginal and dependency modelling as a principled and highly effective paradigm for complex mixed-type tabular generation.

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