Kyle Cranmer

发表

Mining for Dark Matter Substructure: Inferring Subhalo Population Properties from Strong Lenses with Machine Learning

Gilles Louppe, K. Cranmer, J. Brehmer, 2019, The Astrophysical Journal.

Learning to Pivot with Adversarial Networks

Gilles Louppe, Kyle Cranmer, Michael Kagan, 2016, NIPS.

The frontier of simulation-based inference

Gilles Louppe, K. Cranmer, J. Brehmer, 2019, Proceedings of the National Academy of Sciences.

Approximating Likelihood Ratios with Calibrated Discriminative Classifiers

Gilles Louppe, K. Cranmer, J. Pavez, 2015, 1506.02169.

Simulation-Based Inference Methods for Particle Physics

K. Cranmer, J. Brehmer, Kyle Cranmer, 2020, Artificial Intelligence for High Energy Physics.

MadMiner: Machine Learning-Based Inference for Particle Physics

K. Cranmer, J. Brehmer, F. Kling, 2019, Computing and Software for Big Science.

Constraining Effective Field Theories with Machine Learning.

Gilles Louppe, Kyle Cranmer, Juan Pavez, 2018, Physical review letters.

Machine Learning in High Energy Physics Community White Paper pdf

Eli Upfal, Gilles Louppe, Stefan Wunsch, 2018, Journal of Physics: Conference Series.

Toward Machine Learning Optimization of Experimental Design

Gilles Louppe, J. Donini, T. Dorigo, 2021 .

Mining gold from implicit models to improve likelihood-free inference

Gilles Louppe, J. Brehmer, J. Pavez, 2018, Proceedings of the National Academy of Sciences.

A guide to constraining effective field theories with machine learning

Gilles Louppe, K. Cranmer, J. Brehmer, 2018, Physical Review D.

Constraining effective field theories with machine learning

Gilles Louppe, Kyle Cranmer, Felix Kling, 2020, EPJ Web of Conferences.

Machine learning and the physical sciences

Naftali Tishby, L. Daudet, Lenka Zdeborov'a, 2019, Reviews of Modern Physics.

Neural Message Passing for Jet Physics

Kyunghyun Cho, Joan Bruna, Gilles Louppe, 2017 .

QCD-aware recursive neural networks for jet physics

Kyunghyun Cho, Gilles Louppe, K. Cranmer, 2017, Journal of High Energy Physics.

Adversarial Variational Optimization of Non-Differentiable Simulators pdf

Gilles Louppe, Kyle Cranmer, Gilles Louppe, 2017, BNAIC/BENELEARN.

Flows for simultaneous manifold learning and density estimation

K. Cranmer, J. Brehmer, Kyle Cranmer, 2020, NeurIPS.

Mining gold: Improving simulation-based inference with latent information

Gilles Louppe, Kyle Cranmer, Felix Kling, 2019 .

NOTAGAN: Flows for the data manifold

K. Cranmer, J. Brehmer, Kyle Cranmer, 2020 .

Identifying hadronically-decaying vector bosons and top quarks in ATLAS

Gilles Louppe, Kyle Cranmer, Felix Kling, 2019, Journal of Physics: Conference Series.

Likelihood-free inference with an improved cross-entropy estimator pdf

Gilles Louppe, Kyle Cranmer, Markus Stoye, 2018, ArXiv.

Unifying generative models and exact likelihood-free inference with conditional bijections

Gilles Louppe, Kyle Cranmer, Gilles Louppe, 2016 .

Carl: a Likelihood-free Inference Toolbox

Gilles Louppe, Kyle Cranmer, Juan Pavez, 2016, J. Open Source Softw..

MadMiner: Machine Learning-Based Inference for Particle Physics

K. Cranmer, J. Brehmer, F. Kling, 2020, Computing and Software for Big Science.

Improving inference with matrix elements and machine learning

Gilles Louppe, K. Cranmer, J. Brehmer, 2020 .

Lawrence Berkeley National Laboratory Recent Work Title Machine Learning in High Energy Physics Community White Paper Permalink

Rob, Dorian, E. Upfal, 2019 .

Machine Learning in High Energy Physics Community White Paper May 8 , 2019

Dorian, E. Upfal, K. Lannon, 2019 .

Mining gold from implicit models to improve likelihood-free inference

Gilles Louppe, Kyle Cranmer, Juan Pavez, 2018, Proceedings of the National Academy of Sciences.