PhysicsGP: A Genetic Programming approach to event selection. - Kyle Cranmer; R. Sean Bowman (Comput. Phys. Commun., 2005)

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; Eduard Avetisyan; Olof Bärring; James Beacham; Matthew Bellis; Gregorio Bernardi; Siegfried Bethke; Amber Boehnlein; Travis Brooks; Thomas Browder; et al. (CoRR, 2012)

10 Simple Rules for the Care and Feeding of Scientific Data. - Alyssa Goodman; Alberto Pepe; Alexander W. Blocker; Christine L. Borgman; Kyle Cranmer; Mercè Crosas; Rosanne Di Stefano; Yolanda Gil; Paul Groth; Margaret L. Hedstrom; David W. Hogg; Vinay L. Kashyap; et al. (CoRR, 2014)

Ten Simple Rules for the Care and Feeding of Scientific Data. - Alyssa Goodman; Alberto Pepe; Alexander W. Blocker; Christine L. Borgman; Kyle Cranmer; Mercè Crosas; Rosanne Di Stefano; Yolanda Gil; Paul Groth; Margaret L. Hedstrom; David W. Hogg; Vinay L. Kashyap; et al. (PLoS Comput. Biol., 2014)

Parameterized Machine Learning for High-Energy Physics. - Pierre Baldi; Kyle Cranmer; Taylor Faucett; Peter J. Sadowski; Daniel Whiteson (CoRR, 2016)

carl: a likelihood-free inference toolbox. - Gilles Louppe; Kyle Cranmer; Juan Pavez (J. Open Source Softw., 2016)

Learning to Pivot with Adversarial Networks. - Gilles Louppe; Michael Kagan; Kyle Cranmer (NIPS, 2017)

Adversarial Variational Optimization of Non-Differentiable Simulators. - Gilles Louppe; Kyle Cranmer (CoRR, 2017)

Improvements to Inference Compilation for Probabilistic Programming in Large-Scale Scientific Simulators. - Mario Lezcano Casado; Atilim Gunes Baydin; David Martínez-Rubio; Tuan Anh Le 001; Frank D. Wood; Lukas Heinrich; Gilles Louppe; Kyle Cranmer; Karen Ng; Wahid Bhimji; Prabhat (CoRR, 2017)

Search for Computational Workflow Synergies in Reproducible Research Data Analyses in Particle Physics and Life Sciences. - Tibor Simko; Kyle Cranmer; Michael R. Crusoe; Lukas Heinrich; Anton Khodak; Dinos Kousidis; Diego Rodriguez (eScience, 2018)

Mining gold from implicit models to improve likelihood-free inference. - Johann Brehmer; Gilles Louppe; Juan Pavez; Kyle Cranmer (CoRR, 2018)

Backdrop: Stochastic Backpropagation. - Siavash Golkar; Kyle Cranmer (CoRR, 2018)

Machine Learning in High Energy Physics Community White Paper. - Kim Albertsson; Piero Altoe; Dustin Anderson; Michael Andrews; Juan Pedro Araque Espinosa; Adam Aurisano; Laurent Basara; Adrian Bevan; Wahid Bhimji; Daniele Bonacorsi; Paolo Calafiura; Mario Campanelli; et al. (CoRR, 2018)

Efficient Probabilistic Inference in the Quest for Physics Beyond the Standard Model. - Atilim Gunes Baydin; Lukas Heinrich; Wahid Bhimji; Bradley Gram-Hansen; Gilles Louppe; Lei Shao; Prabhat; Kyle Cranmer; Frank D. Wood (CoRR, 2018)

Likelihood-free inference with an improved cross-entropy estimator. - Markus Stoye; Johann Brehmer; Gilles Louppe; Juan Pavez; Kyle Cranmer (CoRR, 2018)

Adversarial Variational Optimization of Non-Differentiable Simulators. - Gilles Louppe; Joeri Hermans; Kyle Cranmer (AISTATS, 2019)

Efficient Probabilistic Inference in the Quest for Physics Beyond the Standard Model. - Atilim Gunes Baydin; Lei Shao; Wahid Bhimji; Lukas Heinrich; Saeid Naderiparizi; Andreas Munk 001; Jialin Liu 002; Bradley Gram-Hansen; Gilles Louppe; Lawrence Meadows; Philip H. S. Torr; Victor W. Lee; et al. (NeurIPS, 2019)

Etalumis: bringing probabilistic programming to scientific simulators at scale. - Atilim Günes Baydin; Lei Shao; Wahid Bhimji; Lukas Heinrich; Lawrence Meadows; Jialin Liu 002; Andreas Munk 001; Saeid Naderiparizi; Bradley Gram-Hansen; Gilles Louppe; Mingfei Ma; Xiaohui Zhao; et al. (SC, 2019)

Hamiltonian Graph Networks with ODE Integrators. - Alvaro Sanchez-Gonzalez; Victor Bapst; Kyle Cranmer; Peter W. Battaglia (CoRR, 2019)

The frontier of simulation-based inference. - Kyle Cranmer; Johann Brehmer; Gilles Louppe (CoRR, 2019)

Normalizing Flows on Tori and Spheres. - Danilo Jimenez Rezende; George Papamakarios; Sébastien Racanière; Michael S. Albergo; Gurtej Kanwar; Phiala E. Shanahan; Kyle Cranmer (ICML, 2020)

Flows for simultaneous manifold learning and density estimation. - Johann Brehmer; Kyle Cranmer (NeurIPS, 2020)

Discovering Symbolic Models from Deep Learning with Inductive Biases. - Miles D. Cranmer; Alvaro Sanchez-Gonzalez; Peter W. Battaglia; Rui Xu; Kyle Cranmer; David N. Spergel; Shirley Ho (NeurIPS, 2020)

Set2Graph: Learning Graphs From Sets. - Hadar Serviansky; Nimrod Segol; Jonathan Shlomi; Kyle Cranmer; Eilam Gross; Haggai Maron; Yaron Lipman (NeurIPS, 2020)

Compact Representation of Uncertainty in Hierarchical Clustering. - Craig S. Greenberg; Sebastian Macaluso; Nicholas Monath; Ji Ah Lee; Patrick Flaherty; Kyle Cranmer; Andrew McGregor 001; Andrew McCallum (CoRR, 2020)

Equivariant flow-based sampling for lattice gauge theory. - Gurtej Kanwar; Michael S. Albergo; Denis Boyda; Kyle Cranmer; Daniel C. Hackett; Sébastien Racanière; Danilo Jimenez Rezende; Phiala E. Shanahan (CoRR, 2020)

Sampling using SU(N) gauge equivariant flows. - Denis Boyda; Gurtej Kanwar; Sébastien Racanière; Danilo Jimenez Rezende; Michael S. Albergo; Kyle Cranmer; Daniel C. Hackett; Phiala E. Shanahan (CoRR, 2020)

Hierarchical clustering in particle physics through reinforcement learning. - Johann Brehmer; Sebastian Macaluso; Duccio Pappadopulo; Kyle Cranmer (CoRR, 2020)

MadMiner: Machine Learning-Based Inference for Particle Physics. - Johann Brehmer; Felix Kling; Irina Espejo; Kyle Cranmer (Comput. Softw. Big Sci., 2020)

Cluster Trellis: Data Structures & Algorithms for Exact Inference in Hierarchical Clustering. - Sebastian Macaluso; Craig S. Greenberg; Nicholas Monath; Ji Ah Lee; Patrick Flaherty; Kyle Cranmer; Andrew McGregor 001; Andrew McCallum (AISTATS, 2021)

Exact and approximate hierarchical clustering using A. - Craig S. Greenberg; Sebastian Macaluso; Nicholas Monath; Avinava Dubey; Patrick Flaherty; Manzil Zaheer; Amr Ahmed 001; Kyle Cranmer; Andrew McCallum (UAI, 2021)

Introduction to Normalizing Flows for Lattice Field Theory. - Michael S. Albergo; Denis Boyda; Daniel C. Hackett; Gurtej Kanwar; Kyle Cranmer; Sébastien Racanière; Danilo Jimenez Rezende; Phiala E. Shanahan (CoRR, 2021)

Flow-based sampling for fermionic lattice field theories. - Michael S. Albergo; Gurtej Kanwar; Sébastien Racanière; Danilo J. Rezende; Julian M. Urban; Denis Boyda; Kyle Cranmer; Daniel C. Hackett; Phiala E. Shanahan (CoRR, 2021)

Flow-based sampling for multimodal distributions in lattice field theory. - Daniel C. Hackett; Chung-Chun Hsieh; Michael S. Albergo; Denis Boyda; Jiunn-Wei Chen; Kai-Feng Chen; Kyle Cranmer; Gurtej Kanwar; Phiala E. Shanahan (CoRR, 2021)

A neural simulation-based inference approach for characterizing the Galactic Center γ-ray excess. - Siddharth Mishra-Sharma; Kyle Cranmer (CoRR, 2021)

Simulation Intelligence: Towards a New Generation of Scientific Methods. - Alexander Lavin; Hector Zenil; Brooks Paige; David Krakauer; Justin Gottschlich; Tim Mattson; Anima Anandkumar; Sanjay Choudry; Kamil Rocki; Atilim Günes Baydin; Carina Prunkl; Olexandr Isayev; et al. (CoRR, 2021)

The Quantum Trellis: A classical algorithm for sampling the parton shower with interference effects. - Sebastian Macaluso; Kyle Cranmer (CoRR, 2021)

pyhf: pure-Python implementation of HistFactory statistical models. - Lukas Heinrich; Matthew Feickert; Giordon Stark; Kyle Cranmer (J. Open Source Softw., 2021)

Flow-based sampling in the lattice Schwinger model at criticality. - Michael S. Albergo; Denis Boyda; Kyle Cranmer; Daniel C. Hackett; Gurtej Kanwar; Sébastien Racanière; Danilo J. Rezende; Fernando Romero-López; Phiala E. Shanahan; Julian M. Urban (CoRR, 2022)

Gauge-equivariant flow models for sampling in lattice field theories with pseudofermions. - Ryan Abbott; Michael S. Albergo; Denis Boyda; Kyle Cranmer; Daniel C. Hackett; Gurtej Kanwar; Sébastien Racanière; Danilo J. Rezende; Fernando Romero-López; Phiala E. Shanahan; Betsy Tian; Julian M. Urban (CoRR, 2022)

Aspects of scaling and scalability for flow-based sampling of lattice QCD. - Ryan Abbott; Michael S. Albergo; Aleksandar Botev; Denis Boyda; Kyle Cranmer; Daniel C. Hackett; Alexander G. de G. Matthews; Sébastien Racanière; Ali Razavi; Danilo J. Rezende; Fernando Romero-López; Phiala E. Shanahan; et al. (CoRR, 2022)

Machine Learning for Science: Bridging Data-Driven and Mechanistic Modelling (Dagstuhl Seminar 22382). - Philipp Berens; Kyle Cranmer; Neil D. Lawrence; Ulrike von Luxburg; Jessica Montgomery (Dagstuhl Reports, 2022)

AI for Science: An Emerging Agenda. - Philipp Berens; Kyle Cranmer; Neil D. Lawrence; Ulrike von Luxburg; Jessica Montgomery (CoRR, 2023)

Normalizing flows for lattice gauge theory in arbitrary space-time dimension. - Ryan Abbott; Michael S. Albergo; Aleksandar Botev; Denis Boyda; Kyle Cranmer; Daniel C. Hackett; Gurtej Kanwar; Alexander G. de G. Matthews; Sébastien Racanière; Ali Razavi; Danilo J. Rezende; Fernando Romero-López; et al. (CoRR, 2023)

Advances in machine-learning-based sampling motivated by lattice quantum chromodynamics. - Kyle Cranmer; Gurtej Kanwar; Sébastien Racanière; Danilo J. Rezende; Phiala E. Shanahan (CoRR, 2023)

Configurable calorimeter simulation for AI applications. - Anton Charkin-Gorbulin; Kyle Cranmer; Francesco Armando Di Bello; Etienne Dreyer; Sanmay Ganguly; Eilam Gross; Lukas Heinrich; Marumi Kado; Nilotpal Kakati; Patrick Rieck; Lorenzo Santi; Matteo Tusoni (Mach. Learn. Sci. Technol., 2023)

Neural Quasiprobabilistic Likelihood Ratio Estimation with Negatively Weighted Data. - Matthew Drnevich; Stephen Jiggins; Judith Katzy; Kyle Cranmer (CoRR, 2024)

Transforming the bootstrap: using transformers to compute scattering amplitudes in planar N = 4 super Yang-Mills theory. - Tianji Cai; Garrett W. Merz; François Charton; Niklas Nolte; Matthias Wilhelm 003; Kyle Cranmer; Lance J. Dixon (Mach. Learn. Sci. Technol., 2024)

Robust anomaly detection for particle physics using multi-background representation learning. - Abhijith Gandrakota; Lily H. Zhang; Aahlad Manas Puli; Kyle Cranmer; Jennifer Ngadiuba; Rajesh Ranganath; Nhan Tran (Mach. Learn. Sci. Technol., 2024)