699 citations · 3.4k across the 113 of their papers we have counts for
6 papers · 1 filter
Trustworthy scientific inference with generative models
James Carzon, Luca Masserano, Joshua D. Ingram +7
Generative artificial intelligence (AI) excels at producing complex data structures (text, images, videos) by learning patterns from training examples. Across scientific discipline…
Classification under Nuisance Parameters and Generalized Label Shift in Likelihood-Free Inference
Luca Masserano, Alex Shen, Michele Doro +3
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 o…
Simulator-Based Inference with Waldo: Confidence Regions by Leveraging Prediction Algorithms and Posterior Estimators for Inverse Problems
Luca Masserano, Tommaso Dorigo, Rafael Izbicki +2
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, especia…
Dealing with Nuisance Parameters using Machine Learning in High Energy Physics: a Review
Tommaso Dorigo, Pablo de Castro
In this work we discuss the impact of nuisance parameters on the effectiveness of machine learning in high-energy physics problems, and provide a review of techniques that allow to…
INFERNO: Inference-Aware Neural Optimisation
Pablo de Castro, Tommaso Dorigo
Complex computer simulations are commonly required for accurate data modelling in many scientific disciplines, making statistical inference challenging due to the intractability of…
The Inverse Bagging Algorithm: Anomaly Detection by Inverse Bootstrap Aggregating
Pietro Vischia, Tommaso Dorigo
For data sets populated by a very well modeled process and by another process of unknown probability density function (PDF), a desired feature when manipulating the fraction of the…