mlsanityData to 7 Oct 2026

Neural Conditional Probability for Uncertainty Quantification

arXiv 2407.01171 ↗First posted 1 Jul 2024cs.LG math.ST stat.MEcited by 14

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Abstract (arXiv)

We introduce Neural Conditional Probability (NCP), an operator-theoretic approach to learning conditional distributions with a focus on statistical inference tasks. NCP can be used to build conditional confidence regions and extract key statistics such as conditional quantiles, mean, and covariance. It offers streamlined learning via a single unconditional training phase, allowing efficient inference without the need for retraining even when conditioning changes. By leveraging the approximation capabilities of neural networks, NCP efficiently handles a wide variety of complex probability distributions. We provide theoretical guarantees that ensure both optimization consistency and statistical accuracy. In experiments, we show that NCP with a 2-hidden-layer network matches or outperforms leading methods. This demonstrates that a a minimalistic architecture with a theoretically grounded loss can achieve competitive results, even in the face of more complex architectures.

Key facts. Cited by 14 papers in the map (data through 7 Oct 2026). Builds on 29 of its 61 references that are in the map. First posted on arXiv 1 Jul 2024.

Builds on

29 of 61 references.

References · 29

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  3. 2021Diffusion Models Beat GANs on Image Synthesiscited by 8,185
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  8. 2019Conformalized Quantile Regressioncited by 620
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17 more
  1. 2023Conformal Prediction With Conditional Guaranteescited by 146
  2. 2019Distributional conformal predictioncited by 104
  3. 2023Towards Coherent Image Inpainting Using Denoising Diffusion Implicit Modelscited by 61
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  6. 2019Structured Output Learning with Conditional Generative Flowscited by 37
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  12. 2017The Kernel Mixture Network: A Nonparametric Method for Conditional Density Estimation of Continuous Random Variablescited by 14
  13. 2021LinCDE: Conditional Density Estimation via Lindsey's Methodcited by 13
  14. 2022Spectral Representation Learning for Conditional Moment Modelscited by 12
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Cited by year (2026 to 7 Oct)

Citing papers per year: 2025: 9; 2026: 5.

14 papers.

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  1. 2025Self-Supervised Evolution Operator Learning for High-Dimensional Dynamical Systemscited by 7
  2. 2025Efficient Parametric SVD of Koopman Operator for Stochastic Dynamical Systemscited by 5
  3. 2025Demystifying Spectral Feature Learning for Instrumental Variable Regressioncited by 4
  4. 2025Outcome-Aware Spectral Feature Learning for Instrumental Variable Regressioncited by 2
  5. 2025CHyLL: Learning Continuous Neural Representations of Hybrid Systemscited by 1
  6. 2025Toward Scalable and Valid Conditional Independence Testing with Spectral Representationscited by 1
  7. 2025kooplearn: A Scikit-Learn Compatible Library of Algorithms for Evolution Operator Learningcited by 1
  8. 2025Revisiting Orbital Minimization Method for Neural Operator Decompositioncited by 0
  9. 2025Supervised Learning of Random Neural Architectures Structured by Latent Random Fields on Compact Boundaryless Multiply-Connected Manifoldscited by 0
  10. 2026Geometric Dictionary Learning of Dynamical Systems with Optimal Transportcited by 0
  11. 2026Score the Algebra, Not the Span: Dimension Reduction for Transfer Operator Models of Dynamical Systemscited by 0
  12. 2026Multi-Task Learning of Conditional Mean Operators: applications to dynamical systems and uncertainty quantificationcited by 0
2 more
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  2. 2026Langevin-Informed Transfer Learning: Replacing Target Samples by Black-Box Feedbackcited by 0