mlsanityData to 7 Oct 2026

Conditional Density Estimation with Bayesian Normalising Flows

arXiv 1802.04908 ↗First posted 14 Feb 2018stat.MLcited by 54

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Abstract (arXiv)

Modeling complex conditional distributions is critical in a variety of settings. Despite a long tradition of research into conditional density estimation, current methods employ either simple parametric forms or are difficult to learn in practice. This paper employs normalising flows as a flexible likelihood model and presents an efficient method for fitting them to complex densities. These estimators must trade-off between modeling distributional complexity, functional complexity and heteroscedasticity without overfitting. We recognize these trade-offs as modeling decisions and develop a Bayesian framework for placing priors over these conditional density estimators using variational Bayesian neural networks. We evaluate this method on several small benchmark regression datasets, on some of which it obtains state of the art performance. Finally, we apply the method to two spatial density modeling tasks with over 1 million datapoints using the New York City yellow taxi dataset and the Chicago crime dataset.

Key facts. Cited by 54 papers in the map (data through 7 Oct 2026). First posted on arXiv 14 Feb 2018.

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Citing papers per year: 2021: 8; 2022: 6; 2023: 10; 2024: 12; 2025: 10; 2026: 8.

54 papers.

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  1. 2021Low-Light Image Enhancement with Normalizing Flowcited by 184
  2. 2021VCNet and Functional Targeted Regularization For Learning Causal Effects of Continuous Treatmentscited by 59
  3. 2023High Fidelity Image Counterfactuals with Probabilistic Causal Modelscited by 53
  4. 2022Probabilistic Conformal Prediction Using Conditional Random Samplescited by 47
  5. 2023Counterfactual Identifiability of Bijective Causal Modelscited by 42
  6. 2022Rhino: Deep Causal Temporal Relationship Learning With History-dependent Noisecited by 30
  7. 2023Conditional Optimal Transport on Function Spacescited by 26
  8. 2024Benchmarking Counterfactual Image Generationcited by 22
  9. 2022Normalizing Flows for Interventional Density Estimationcited by 21
  10. 2022Improvement-Focused Causal Recourse (ICR)cited by 20
  11. 2023Bounds on Representation-Induced Confounding Bias for Treatment Effect Estimationcited by 20
  12. 2024Conformal Prediction for Causal Effects of Continuous Treatmentscited by 19
38 more
  1. 2023Reliable Off-Policy Learning for Dosage Combinationscited by 15
  2. 2024Mitigating attribute amplification in counterfactual image generationcited by 12
  3. 2024Quantifying Aleatoric Uncertainty of the Treatment Effect: A Novel Orthogonal Learnercited by 10
  4. 2023A generative flow for conditional sampling via optimal transportcited by 7
  5. 2022Conditional Measurement Density Estimation in Sequential Monte Carlo via Normalizing Flowcited by 6
  6. 2022Short-Term Density Forecasting of Low-Voltage Load using Bernstein-Polynomial Normalizing Flowscited by 6
  7. 2024A Kernel-Based Conditional Two-Sample Test Using Nearest Neighbors (with Applications to Calibration, Regression Curves, and Simulation-Based Inference)cited by 5
  8. 2023Efficient SAGE Estimation via Causal Structure Learningcited by 4
  9. 2024Conditional Density Estimation with Histogram Treescited by 4
  10. 2026Generative models for decision-making under distributional shiftcited by 4
  11. 2021Flow-based Spatio-Temporal Structured Prediction of Motion Dynamicscited by 3
  12. 2021Deconvolutional Density Network: Modeling Free-Form Conditional Distributionscited by 3
  13. 2024TutteNet: Injective 3D Deformations by Composition of 2D Mesh Deformationscited by 3
  14. 2021Uncertainty estimation under model misspecification in neural network regressioncited by 2
  15. 2021Graph network for learning bi-directional physicscited by 2
  16. 2023Conditional Kernel Imitation Learning for Continuous State Environmentscited by 2
  17. 2024Generative Modeling: A Reviewcited by 2
  18. 2025Accelerate TarFlow Sampling with GS-Jacobi Iterationcited by 2
  19. 2025Adaptive Bayesian Single-Shot Quantum Sensingcited by 2
  20. 2025Simulation-based inference for Precision Neutrino Physics through Neural Monte Carlo tuningcited by 2
  21. 2021Semi-supervised Learning with Missing Values Imputationcited by 1
  22. 2021Viscos Flows: Variational Schur Conditional Sampling With Normalizing Flowscited by 1
  23. 2024Probabilistic Forecasting of Irregular Time Series via Conditional Flowscited by 1
  24. 2025GDR-learners: Orthogonal Learning of Generative Models for Potential Outcomescited by 1
  25. 2025ActiveCQ: Active Estimation of Causal Quantitiescited by 1
  26. 2026Harnessing Synthetic Data from Generative AI for Statistical Inferencecited by 1
  27. 2026GGMPs: Generalized Gaussian Mixture Processescited by 1
  28. 2026UQ-SHRED: uncertainty quantification of shallow recurrent decoder networks for sparse sensing via engressioncited by 1
  29. 2023Weakly Supervised Label Learning Flowscited by 0
  30. 2023On Certified Generalization in Structured Predictioncited by 0
  31. 2024LoLiSRFlow: Joint Single Image Low-light Enhancement and Super-resolution via Cross-scale Transformer-based Conditional Flowcited by 0
  32. 2024Generative modeling of density regression through tree flowscited by 0
  33. 2024Learning state and proposal dynamics in state-space models using differentiable particle filters and neural networkscited by 0
  34. 2025CEC-MMR: Cross-Entropy Clustering Approach to Multi-Modal Regressioncited by 0
  35. 2025ExOSITO: Explainable Off-Policy Learning with Side Information for Intensive Care Unit Blood Test Orderscited by 0
  36. 2025Inherited or produced? Inferring protein production kinetics when protein counts are shaped by a cell's division historycited by 0
  37. 2025Smoothing-Based Conformal Prediction for Balancing Efficiency and Interpretabilitycited by 0
  38. 2025Addressing prior dependence in hierarchical Bayesian modeling for PTA data analysis I: Methodology and implementationcited by 0

50 most cited of 54 All 54 (JSON)