Publications (29)
Parameterized Machine Learning for High-Energy Physics
Pierre Baldi, Kyle Cranmer, Taylor Faucett +2
Natural Priors, CMSSM Fits and LHC Weather Forecasts
Ben C Allanach, Kyle Cranmer, Christopher G Lester +1
Modeling Smooth Backgrounds and Generic Localized Signals with Gaussian Processes
Meghan Frate, Kyle Cranmer, Saarik Kalia +2
Approximating Likelihood Ratios with Calibrated Discriminative Classifiers
Kyle Cranmer, Juan Pavez, Gilles Louppe
Inferring the quantum density matrix with machine learning
Kyle Cranmer, Siavash Golkar, Duccio Pappadopulo
PhysicsGP: A Genetic Programming Approach to Event Selection
Kyle Cranmer, R. Sean Bowman
Statistical Challenges for Searches for New Physics at the LHC
Kyle Cranmer
The RooStats Project
Lorenzo Moneta, Kevin Belasco, Kyle Cranmer +6
Asymptotic distribution for two-sided tests with lower and upper boundaries on the parameter of interest
Glen Cowan, Kyle Cranmer, Eilam Gross +1
Constraining Effective Field Theories with Machine Learning
Johann Brehmer, Kyle Cranmer, Gilles Louppe +1
Likelihood-free inference with an improved cross-entropy estimator
Markus Stoye, Johann Brehmer, Gilles Louppe +2
Yadage and Packtivity - analysis preservation using parametrized workflows
Kyle Cranmer, Lukas Heinrich
Machine Learning in High Energy Physics Community White Paper
Kim Albertsson, Piero Altoe, Dustin Anderson +125
Asymptotic formulae for likelihood-based tests of new physics
Glen Cowan, Kyle Cranmer, Eilam Gross +1
Power-Constrained Limits
Glen Cowan, Kyle Cranmer, Eilam Gross +1
Maximum Significance at the LHC and Higgs Decays to Muons
Kyle Cranmer, Tilman Plehn
A Guide to Constraining Effective Field Theories with Machine Learning
Johann Brehmer, Kyle Cranmer, Gilles Louppe +1
Better Higgs Measurements Through Information Geometry
Johann Brehmer, Kyle Cranmer, Felix Kling +1
HEP Software Foundation Community White Paper Working Group - Data Analysis and Interpretation
Lothar Bauerdick, Riccardo Maria Bianchi, Brian Bockelman +26
Backdrop: Stochastic Backpropagation
Siavash Golkar, Kyle Cranmer
RECAST: Extending the Impact of Existing Analyses
Kyle Cranmer, Itay Yavin
Practical Statistics for the LHC
Kyle Cranmer
Deep Learning and its Application to LHC Physics
Dan Guest, Kyle Cranmer, Daniel Whiteson
Decoupling Theoretical Uncertainties from Measurements of the Higgs Boson
Kyle Cranmer, Sven Kreiss, David Lopez-Val +1
Improvements to Inference Compilation for Probabilistic Programming in Large-Scale Scientific Simulators
Mario Lezcano Casado, Atilim Gunes Baydin, David Martinez Rubio +8
Status Report of the DPHEP Study Group: Towards a Global Effort for Sustainable Data Preservation in High Energy Physics
Z. Akopov, Silvia Amerio, David Asner +87
Observing Ultra-High Energy Cosmic Rays with Smartphones
Daniel Whiteson, Michael Mulhearn, Chase Shimmin +3
Statistical Challenges of Global SUSY Fits
Roberto Trotta, Kyle Cranmer
10 Simple Rules for the Care and Feeding of Scientific Data
Alyssa Goodman, Alberto Pepe, Alexander W. Blocker +12