Tommaso Dorigo — accepted AI-conference papers

2024

Classification under Nuisance Parameters and Generalized Label Shift in Likelihood-Free Inference

Luca Masserano, Alexander Shen, Michele Doro, Tommaso Dorigo, Rafael Izbicki, Ann B. Lee

ICML 2024poster

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. We refer to this type of dist…

Cited by 2 Source ↗PDF ↗code ↗Scholar ↗

2023

Simulator-Based Inference with WALDO: Confidence Regions by Leveraging Prediction Algorithms and Posterior Estimators for Inverse Problems

Luca Masserano, Tommaso Dorigo, Rafael Izbicki, Mikael Kuusela, Ann Lee

AISTATS 2023poster

Prediction algorithms, such as deep neural networks (DNNs), are used in many domain sciences to directly estimate internal parameters of interest in simulator-based models, especially in settings where the observations include images or complex high-dimensional data. In parallel, modern neural densi…

Cited by 16 Source ↗PDF ↗code ↗Scholar ↗