no code implementations • 8 Feb 2024 • Luca Masserano, Alex Shen, Michele Doro, Tommaso Dorigo, Rafael Izbicki, Ann B. Lee
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.
no code implementations • 23 Feb 2023 • Luca Masserano, Syama Sundar Rangapuram, Shubham Kapoor, Rajbir Singh Nirwan, Youngsuk Park, Michael Bohlke-Schneider
We present an adaptive sampling strategy that selects the part of the time series history that is relevant for forecasting.
1 code implementation • 31 May 2022 • Luca Masserano, Tommaso Dorigo, Rafael Izbicki, Mikael Kuusela, Ann B. Lee
We also illustrate how our approach can correct overly confident posterior regions computed with normalizing flows.
2 code implementations • 8 Jul 2021 • Niccolò Dalmasso, Luca Masserano, David Zhao, Rafael Izbicki, Ann B. Lee
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.