Search Results for author:
Found 7 papers, 4 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.
TomOpt: Differential optimisation for task- and constraint-aware design of particle detectors in the context of muon tomography
We describe a software package, TomOpt, developed to optimise the geometrical layout and specifications of detectors designed for tomography by scattering of cosmic-ray muons.
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.
Advances in Multi-Variate Analysis Methods for New Physics Searches at the Large Hadron Collider
Between the years 2015 and 2019, members of the Horizon 2020-funded Innovative Training Network named "AMVA4NewPhysics" studied the customization and application of advanced multivariate analysis methods and statistical learning tools to high-energy physics problems, as well as developed entirely new ones.
Dealing with Nuisance Parameters using Machine Learning in High Energy Physics: a Review
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 include their effect and reduce their impact in the search for optimal selection criteria and variable transformations.
INFERNO: Inference-Aware Neural Optimisation
Complex computer simulations are commonly required for accurate data modelling in many scientific disciplines, making statistical inference challenging due to the intractability of the likelihood evaluation for the observed data.
The Inverse Bagging Algorithm: Anomaly Detection by Inverse Bootstrap Aggregating
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 unknown process (either for enhancing it or suppressing it) consists in avoiding to modify the kinematic distributions of the well modeled one.
