mlsanityData to 14 Oct 2026

Copula Density Neural Estimation

arXiv 2211.15353 ↗First posted 25 Nov 2022cs.LG eess.SP stat.MLcited by 13

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Abstract (arXiv)

Probability density estimation from observed data constitutes a central task in statistics. In this brief, we focus on the problem of estimating the copula density associated to any observed data, as it fully describes the dependence between random variables. We separate univariate marginal distributions from the joint dependence structure in the data, the copula itself, and we model the latter with a neural network-based method referred to as copula density neural estimation (CODINE). Results show that the novel learning approach is capable of modeling complex distributions and can be applied for mutual information estimation and data generation.

Key facts. Cited by 13 papers in the map (data through 14 Oct 2026). Builds on 16 of its 46 references that are in the map. First posted on arXiv 25 Nov 2022.

Builds on

16 of 46 references.

References · 16

  1. 2014Adam: A Method for Stochastic Optimizationcited by 37,240
  2. 2020Denoising Diffusion Probabilistic Modelscited by 21,076
  3. 2017Neural Discrete Representation Learningcited by 4,712
  4. 2017Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning Algorithmscited by 4,349
  5. 2015Variational Inference with Normalizing Flowscited by 2,170
  6. 2016Density estimation using Real NVPcited by 2,042
  7. 2016Pixel Recurrent Neural Networkscited by 816
  8. 2018MINE: Mutual Information Neural Estimationcited by 683
  9. 2015Generative Moment Matching Networkscited by 257
  10. 2019Understanding the Limitations of Variational Mutual Information Estimatorscited by 140
  11. 2020Deep Archimedean Copulascited by 24
  12. 2019Copulas as High-Dimensional Generative Models: Vine Copula Autoencoderscited by 20
4 more
  1. 2015Evading the curse of dimensionality in nonparametric density estimation with simplified vine copulascited by 18
  2. 2021Implicit Generative Copulascited by 17
  3. 2021Multi-Agent Imitation Learning with Copulascited by 13
  4. 2022Neural Copula: A unified framework for estimating generic high-dimensional Copula functionscited by 7

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2023: 2; 2024: 4; 2025: 3; 2026: 4.

13 papers.

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  1. 2023MINDE: Mutual Information Neural Diffusion Estimationcited by 31
  2. 2023Mutual Information Estimation via f-Divergence and Data Derangementscited by 17
  3. 2024f-Divergence Based Classification: Beyond the Use of Cross-Entropycited by 8
  4. 2024Information Theoretic Text-to-Image Alignmentcited by 8
  5. 2024Marginal Causal Flows for Validation and Inferencecited by 8
  6. 2025Neural Mutual Information Estimation with Vector Copulascited by 4
  7. 2025Diffusion Secant Alignment for Score-Based Density Ratio Estimationcited by 3
  8. 2026CAWI: Copula-Aligned Weight Initialization for Randomized Neural Networkscited by 3
  9. 2025INFO-SEDD: Continuous Time Markov Chains as Scalable Information Metrics Estimatorscited by 2
  10. 2026Why Do Unlearnable Examples Work: A Novel Perspective of Mutual Informationcited by 2
  11. 2024Masked Language Modeling Becomes Conditional Density Estimation for Tabular Data Synthesiscited by 1
  12. 2026Amortized Vine Copulas for High-Dimensional Density and Information Estimationcited by 1
1 more
  1. 2026Capturing Multivariate Dependencies of EV Charging Events: From Parametric Copulas to Neural Density Estimationcited by 0