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
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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