mlsanityData to 7 Oct 2026

Conditional Density Estimation with Neural Networks: Best Practices and Benchmarks

arXiv 1903.00954 ↗First posted 3 Mar 2019stat.ML cs.LG q-fin.CPcited by 37

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Abstract (arXiv)

Given a set of empirical observations, conditional density estimation aims to capture the statistical relationship between a conditional variable x and a dependent variable y by modeling their conditional probability p(y|x). The paper develops best practices for conditional density estimation for finance applications with neural networks, grounded on mathematical insights and empirical evaluations. In particular, we introduce a noise regularization and data normalization scheme, alleviating problems with over-fitting, initialization and hyper-parameter sensitivity of such estimators. We compare our proposed methodology with popular semi- and non-parametric density estimators, underpin its effectiveness in various benchmarks on simulated and Euro Stoxx 50 data and show its superior performance. Our methodology allows to obtain high-quality estimators for statistical expectations of higher moments, quantiles and non-linear return transformations, with very little assumptions about the return dynamic.

Key facts. Cited by 37 papers in the map (data through 7 Oct 2026). First posted on arXiv 3 Mar 2019.

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Citing papers per year: 2021: 5; 2022: 4; 2023: 5; 2024: 13; 2025: 6; 2026: 4.

37 papers.

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25 more
  1. 2024Deep Learning of Multivariate Extremes via a Geometric Representationcited by 4
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  5. 2025Conformal Inference of Individual Treatment Effects Using Conditional Density Estimatescited by 3
  6. 2023Conditional Kernel Imitation Learning for Continuous State Environmentscited by 2
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  9. 2023Data-Driven Latency Probability Prediction for Wireless Networks: Focusing on Tail Probabilitiescited by 1
  10. 2024Deep Modeling of Non-Gaussian Aleatoric Uncertaintycited by 1
  11. 2025Nonparametric estimation of conditional probability distributions using a generative approach based on conditional push-forward neural networkscited by 1
  12. 2026GGMPs: Generalized Gaussian Mixture Processescited by 1
  13. 2021Construction de variables a l'aide de classifieurs comme aide a la regressioncited by 0
  14. 2022MCD: Marginal Contrastive Discrimination for conditional density estimationcited by 0
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  19. 2024Generative modeling of density regression through tree flowscited by 0
  20. 2025CEC-MMR: Cross-Entropy Clustering Approach to Multi-Modal Regressioncited by 0
  21. 2025Normalizing Flow to Augmented Posterior: Conditional Density Estimation with Interpretable Dimension Reduction for High Dimensional Datacited by 0
  22. 2025Smoothing-Based Conformal Prediction for Balancing Efficiency and Interpretabilitycited by 0
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