Likelihood Free Inference

Likelihood-Free Frequentist Inference: Bridging Classical Statistics and Machine Learning for Reliable Simulator-Based Inference
We introduce Likelihood-Free Frequentist Inference (LF2I), a framework that bridges classical statistics and machine learning for valid confidence sets in complex, likelihood-free settings. LF2I provides confidence sets with near finite-sample validity and offers practical diagnostics for empirical coverage, ensuring reliable scientific inference without costly Monte Carlo or bootstrap methods.
N. Dalmasso, L. Masserano, D. Zhao, Rafael Izbicki, A. B. Lee
June, 2024 Electronic Journal of Statistics
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Likelihood-Free Frequentist Inference: Bridging Classical Statistics and Machine Learning for Reliable Simulator-Based Inference
Classification under Nuisance Parameters and Generalized Label Shift in Likelihood-Free Inference
The paper introduces a novel approach to classifying events under generalized label shift by framing classification as hypothesis testing, which leads to valid uncertainty quantification. This is demonstrated through applications in biology and astroparticle physics.
L. Masserano, A. Shen, M. Doro, T. Dorigo, Rafael Izbicki, A. B. Lee
May, 2024 Proceedings of Machine Learning Research (ICML Track)
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Classification under Nuisance Parameters and Generalized Label Shift in Likelihood-Free Inference
Classification under Prior Probability Shift in Simulator-Based Inference: Application to Atmospheric Cosmic-Ray Showers
A. Shen, L. Masserano, Rafael Izbicki, T. Dorigo, M. Doro, A. B. Lee
March, 2023 NeurIPS (Machine Learning and the Physical Sciences Workshop; Best Poster Award)
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Simulation-Based Inference with Waldo: Confidence Regions by Leveraging Prediction Algorithms or Posterior Estimators for Inverse Problems
L. Masserano, T. Dorigo, Rafael Izbicki, M. Kuusela, A. B. Lee
February, 2023 Proceedings of Machine Learning Research (AISTATS track; Finalist at the ASA SPES and Q&P Student Paper Competition)
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