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Found 4 papers, 2 papers with code
Classification under Nuisance Parameters and Generalized Label Shift in Likelihood-Free Inference
An open scientific challenge is how to classify events with reliable measures of uncertainty, when we have a mechanistic model of the data-generating process but the distribution over both labels and latent nuisance parameters is different between train and target data.
Adaptive Sampling for Probabilistic Forecasting under Distribution Shift
We present an adaptive sampling strategy that selects the part of the time series history that is relevant for forecasting.
Simulator-Based Inference with Waldo: Confidence Regions by Leveraging Prediction Algorithms and Posterior Estimators for Inverse Problems
We also illustrate how our approach can correct overly confident posterior regions computed with normalizing flows.
Likelihood-Free Frequentist Inference: Bridging Classical Statistics and Machine Learning for Reliable Simulator-Based Inference
In this work, we propose a unified and modular inference framework that bridges classical statistics and modern machine learning providing (i) a practical approach to the Neyman construction of confidence sets with frequentist finite-sample coverage for any value of the unknown parameters; and (ii) interpretable diagnostics that estimate the empirical coverage across the entire parameter space.
