mlsanityData to 7 Oct 2026

Mining gold from implicit models to improve likelihood-free inference

arXiv 1805.12244 ↗First posted 30 May 2018stat.ML cs.LG hep-phcited by 60

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Abstract (arXiv)

Simulators often provide the best description of real-world phenomena. However, they also lead to challenging inverse problems because the density they implicitly define is often intractable. We present a new suite of simulation-based inference techniques that go beyond the traditional Approximate Bayesian Computation approach, which struggles in a high-dimensional setting, and extend methods that use surrogate models based on neural networks. We show that additional information, such as the joint likelihood ratio and the joint score, can often be extracted from simulators and used to augment the training data for these surrogate models. Finally, we demonstrate that these new techniques are more sample efficient and provide higher-fidelity inference than traditional methods.

Key facts. Cited by 60 papers in the map (data through 7 Oct 2026). First posted on arXiv 30 May 2018.

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Citing papers per year: 2021: 14; 2022: 8; 2023: 11; 2024: 10; 2025: 6; 2026: 11.

60 papers.

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  1. 2021Benchmarking Simulation-Based Inferencecited by 160
  2. 2021A Living Review of Machine Learning for Particle Physicscited by 77
  3. 2021Machine Learning in the Search for New Fundamental Physicscited by 50
  4. 2024Neural Methods for Amortized Inferencecited by 36
  5. 2021Group equivariant neural posterior estimationcited by 16
  6. 2022New directions for surrogate models and differentiable programming for High Energy Physics detector simulationcited by 16
  7. 2021E Pluribus Unum Ex Machina: Learning from Many Collider Events at Oncecited by 15
  8. 2022Simulator-Based Inference with Waldo: Confidence Regions by Leveraging Prediction Algorithms and Posterior Estimators for Inverse Problemscited by 14
  9. 2023Leveraging Self-Consistency for Data-Efficient Amortized Bayesian Inferencecited by 14
  10. 2025Effortless, Simulation-Efficient Bayesian Inference using Tabular Foundation Modelscited by 13
  11. 2021A neural simulation-based inference approach for characterizing the Galactic Center γ-ray excesscited by 12
  12. 2024Amortized Bayesian Multilevel Modelscited by 12
38 more
  1. 2021A Cautionary Tale of Decorrelating Theory Uncertaintiescited by 11
  2. 2021Density estimation on smooth manifolds with normalizing flowscited by 10
  3. 2021Likelihood-Free Frequentist Inference: Bridging Classical Statistics and Machine Learning for Reliable Simulator-Based Inferencecited by 10
  4. 2021Unifying Likelihood-free Inference with Black-box Optimization and Beyondcited by 10
  5. 2023Neural Likelihood Surfaces for Spatial Processes with Computationally Intensive or Intractable Likelihoodscited by 10
  6. 2023Evidence Networks: simple losses for fast, amortized, neural Bayesian model comparisoncited by 10
  7. 2022Neural Posterior Estimation with Differentiable Simulatorscited by 9
  8. 2023Marginal Density Ratio for Off-Policy Evaluation in Contextual Banditscited by 9
  9. 2025The AI Cosmologist I: An Agentic System for Automated Data Analysiscited by 9
  10. 2022Differentiable Matrix Elements with MadJaxcited by 8
  11. 2022Interpretable Uncertainty Quantification in AI for HEPcited by 8
  12. 2022Accelerated Bayesian SED Modeling using Amortized Neural Posterior Estimationcited by 7
  13. 2022Contrastive Neural Ratio Estimation for Simulation-based Inferencecited by 7
  14. 2023Comparison of Affine and Rational Quadratic Spline Coupling and Autoregressive Flows through Robust Statistical Testscited by 5
  15. 2023ELSA -- Enhanced latent spaces for improved collider simulationscited by 5
  16. 2023On Estimating the Gradient of the Expected Information Gain in Bayesian Experimental Designcited by 5
  17. 2026Scaling laws for amplitude surrogatescited by 5
  18. 2021Inferring dark matter substructure with astrometric lensing beyond the power spectrumcited by 4
  19. 2021Hybrid Memoised Wake-Sleep: Approximate Inference at the Discrete-Continuous Interfacecited by 3
  20. 2023Designing Optimal Behavioral Experiments Using Machine Learningcited by 3
  21. 2025Amortized Bayesian Mixture Modelscited by 3
  22. 2022New Machine Learning Techniques for Simulation-Based Inference: InferoStatic Nets, Kernel Score Estimation, and Kernel Likelihood Ratio Estimationcited by 2
  23. 2023Kernel-Based Tests for Likelihood-Free Hypothesis Testingcited by 2
  24. 2024A Likelihood-Free Approach to Goal-Oriented Bayesian Optimal Experimental Designcited by 2
  25. 2024FAIR Universe HiggsML Uncertainty Dataset and Competitioncited by 2
  26. 2024Neural Quasiprobabilistic Likelihood Ratio Estimation with Negatively Weighted Datacited by 2
  27. 2024Full-waveform earthquake source inversion using simulation-based inferencecited by 2
  28. 2024Conformal Calibration of Statistical Confidence Setscited by 2
  29. 2025Neural simulation-based inference of the Higgs trilinear self-coupling via off-shell Higgs productioncited by 2
  30. 2025Simulation-based inference for Precision Neutrino Physics through Neural Monte Carlo tuningcited by 2
  31. 2026BRICKS: Compositional Neural Markov Kernels for Zero-Shot Radiation-Matter Simulationcited by 2
  32. 2024Classification under Nuisance Parameters and Generalized Label Shift in Likelihood-Free Inferencecited by 1
  33. 2024Dimension reduction via score ratio matchingcited by 1
  34. 2025BitHEP -- The Limits of Low-Precision ML in HEPcited by 1
  35. 2026Simulation-based Bayesian inference with ameliorative learned summary statistics -- Part Icited by 1
  36. 2026Artificial Intelligence and Symmetries: Learning, Encoding, and Discovering Structure in Physical Datacited by 1
  37. 2026Latent Target Score Matching, with an application to Simulation-Based Inferencecited by 1
  38. 2026An Introduction to Bayesian and Frequentist Simulation-Based Inference with Machine Learningcited by 1

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