mlsanityData to 7 Oct 2026

Neural Density Estimation and Likelihood-free Inference

arXiv 1910.13233 ↗First posted 29 Oct 2019stat.ML cs.LGcited by 23

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Abstract (arXiv)

I consider two problems in machine learning and statistics: the problem of estimating the joint probability density of a collection of random variables, known as density estimation, and the problem of inferring model parameters when their likelihood is intractable, known as likelihood-free inference. The contribution of the thesis is a set of new methods for addressing these problems that are based on recent advances in neural networks and deep learning.

Key facts. Cited by 23 papers in the map (data through 7 Oct 2026). First posted on arXiv 29 Oct 2019.

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Citing papers per year: 2021: 5; 2022: 3; 2023: 6; 2024: 4; 2025: 3; 2026: 2.

23 papers.

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  1. 2021Solving Schr"odinger Bridges via Maximum Likelihoodcited by 140
  2. 2023Efficient Neural Network Approaches for Conditional Optimal Transport with Applications in Bayesian Inferencecited by 19
  3. 2025Position: The Future of Bayesian Prediction Is Prior-Fittedcited by 19
  4. 2023Squared Neural Families: A New Class of Tractable Density Modelscited by 12
  5. 2021Differentially Private Normalizing Flows for Privacy-Preserving Density Estimationcited by 10
  6. 2022m^ast of two-dimensional electron gas: a neural canonical transformation studycited by 7
  7. 2024Unifying Simulation and Inference with Normalizing Flowscited by 6
  8. 2024Paired Autoencoders for Likelihood-free Estimation in Inverse Problemscited by 6
  9. 2021Unsupervised tree boosting for learning probability distributionscited by 5
  10. 2022Efficient identification of informative features in simulation-based inferencecited by 5
  11. 2023Deep Calibration of Market Simulations using Neural Density Estimators and Embedding Networkscited by 4
  12. 2022Machine-Learned Exclusion Limits without Binningcited by 3
11 more
  1. 2023Approximation properties of slice-matching operatorscited by 3
  2. 2024Deep Optimal Experimental Design for Parameter Estimation Problemscited by 2
  3. 2025Data-Driven High-Dimensional Statistical Inference with Generative Modelscited by 2
  4. 2023Who Should I Engage with At What Time? A Missing Event Aware Temporal Graph Neural Networkcited by 1
  5. 2024Deep Modeling of Non-Gaussian Aleatoric Uncertaintycited by 1
  6. 2021Feature Alignment as a Generative Processcited by 0
  7. 2021Identification of Vehicle Dynamics Parameters Using Simulation-based Inferencecited by 0
  8. 2023Universal Approximation and the Topological Neural Networkcited by 0
  9. 2025Dark Energy Survey Year 3 results: Simulation-based wCDM inference from weak lensing and galaxy clustering maps with deep learning: Analysis designcited by 0
  10. 2026Temporal-Conditioned Normalizing Flows for Multivariate Time Series Anomaly Detectioncited by 0
  11. 2026A Convex Approximation Framework for Neural Likelihood-Based Bayesian Inverse Problemscited by 0