Kyle Cranmer

According to our database, Kyle Cranmer authored at least 49 papers between 2005 and 2024.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of three.

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

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, Kyle Cranmer, Lance J. Dixon

Mach. Learn. Sci. Technol., 2024

Neural Quasiprobabilistic Likelihood Ratio Estimation with Negatively Weighted Data.

Matthew Drnevich, Stephen Jiggins, Judith Katzy, Kyle Cranmer

CoRR, 2024

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., September, 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

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, Phiala E. Shanahan, Julian M. Urban

CoRR, 2023

AI for Science: An Emerging Agenda.

Philipp Berens, Kyle Cranmer, Neil D. Lawrence, Ulrike von Luxburg, Jessica Montgomery

CoRR, 2023

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, September, 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, 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

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

2021

pyhf: pure-Python implementation of HistFactory statistical models.

Lukas Heinrich, Matthew Feickert, Giordon Stark, Kyle Cranmer

J. Open Source Softw., 2021

The Quantum Trellis: A classical algorithm for sampling the parton shower with interference effects.

Sebastian Macaluso, 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, Erik Peterson, Peter L. McMahon, Jakob H. Macke, Kyle Cranmer, Jiaxin Zhang, Haruko M. Wainwright, Adi Hanuka, Manuela Veloso, Samuel Assefa, Stephan Zheng, Avi Pfeffer

CoRR, 2021

A neural simulation-based inference approach for characterizing the Galactic Center γ-ray excess.

Siddharth Mishra-Sharma, Kyle Cranmer

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

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

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

Exact and approximate hierarchical clustering using A.

Craig S. Greenberg, Sebastian Macaluso, Nicholas Monath, Avinava Dubey, Patrick Flaherty, Manzil Zaheer, Amr Ahmed, Kyle Cranmer, Andrew McCallum

Proceedings of the Thirty-Seventh Conference on Uncertainty in Artificial Intelligence, 2021

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, Andrew McCallum

Proceedings of the 24th International Conference on Artificial Intelligence and Statistics, 2021

2020

MadMiner: Machine Learning-Based Inference for Particle Physics.

Johann Brehmer, Felix Kling, Irina Espejo, Kyle Cranmer

Comput. Softw. Big Sci., December, 2020

Hierarchical clustering in particle physics through reinforcement learning.

Johann Brehmer, Sebastian Macaluso, Duccio Pappadopulo, Kyle Cranmer

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

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

Compact Representation of Uncertainty in Hierarchical Clustering.

Craig S. Greenberg, Sebastian Macaluso, Nicholas Monath, Ji Ah Lee, Patrick Flaherty, Kyle Cranmer, Andrew McGregor, Andrew McCallum

CoRR, 2020

Set2Graph: Learning Graphs From Sets.

Hadar Serviansky, Nimrod Segol, Jonathan Shlomi, Kyle Cranmer, Eilam Gross, Haggai Maron, Yaron Lipman

Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 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

Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

Flows for simultaneous manifold learning and density estimation.

Johann Brehmer, Kyle Cranmer

Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

Normalizing Flows on Tori and Spheres.

Danilo Jimenez Rezende, George Papamakarios, Sébastien Racanière, Michael S. Albergo, Gurtej Kanwar, Phiala E. Shanahan, Kyle Cranmer

Proceedings of the 37th International Conference on Machine Learning, 2020

2019

The frontier of simulation-based inference.

Kyle Cranmer, Johann Brehmer, Gilles Louppe

CoRR, 2019

Hamiltonian Graph Networks with ODE Integrators.

Alvaro Sanchez-Gonzalez, Victor Bapst, Kyle Cranmer, Peter W. Battaglia

CoRR, 2019

Etalumis: bringing probabilistic programming to scientific simulators at scale.

Atilim Günes Baydin, Lei Shao, Wahid Bhimji, Lukas Heinrich, Lawrence Meadows, Jialin Liu, Andreas Munk, Saeid Naderiparizi, Bradley Gram-Hansen, Gilles Louppe, Mingfei Ma, Xiaohui Zhao, Philip H. S. Torr, Victor W. Lee, Kyle Cranmer, Prabhat, Frank Wood

Proceedings of the International Conference for High Performance Computing, 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, Jialin Liu, Bradley Gram-Hansen, Gilles Louppe, Lawrence Meadows, Philip H. S. Torr, Victor W. Lee, Kyle Cranmer, Prabhat, Frank Wood

Proceedings of the Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, 2019

Adversarial Variational Optimization of Non-Differentiable Simulators.

Gilles Louppe, Joeri Hermans, Kyle Cranmer

Proceedings of the 22nd International Conference on Artificial Intelligence and Statistics, 2019

2018

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

Markus Stoye, Johann Brehmer, Gilles Louppe, Juan Pavez, Kyle Cranmer

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

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, Louis Capps, Federico Carminati, Stefano Carrazza, Taylor Childers, Elias Coniavitis, Kyle Cranmer, Claire David, Douglas Davis, Javier M. Duarte, Martin Erdmann, Jonas Eschle, Amir Farbin, Matthew Feickert, Nuno Filipe Castro, Conor Fitzpatrick, Michele Floris, Alessandra Forti, Jordi Garra-Tico, Jochen Gemmler, Maria Girone, Paul Glaysher, Sergei Gleyzer, Vladimir V. Gligorov, Tobias Golling, Jonas Graw, Lindsey Gray, Dick Greenwood, Thomas Hacker, John Harvey, Benedikt Hegner, Lukas Heinrich, Ben Hooberman, Johannes Junggeburth, Michael Kagan, Meghan Kane, Konstantin Kanishchev, Przemyslaw Karpinski, Zahari Kassabov, Gaurav Kaul, Dorian Kcira, Thomas Keck, Alexei Klimentov, Jim Kowalkowski, Luke Kreczko, Alexander Kurepin, Rob Kutschke, Valentin Kuznetsov, Nicolas Köhler, Igor Lakomov, Kevin Lannon, Mario Lassnig, Antonio Limosani, Gilles Louppe, Aashrita Mangu, Pere Mato, Narain Meenakshi, Helge Meinhard, Dario Menasce, Lorenzo Moneta, Seth Moortgat, Mark S. Neubauer, Harvey B. Newman, Hans Pabst, Michela Paganini, Manfred Paulini, Gabriel N. Perdue, Uzziel Perez, Attilio Picazio, Jim Pivarski, Harrison Prosper, Fernanda Psihas, Alexander Radovic, Ryan Reece, Aurelius Rinkevicius, Eduardo Rodrigues, Jamal Rorie, David Rousseau, Aaron Sauers, Steven Schramm, Ariel Schwartzman, Horst Severini, Paul Seyfert, Filip Siroky, Konstantin Skazytkin, Mike Sokoloff, Graeme Andrew Stewart, Bob Stienen, Ian Stockdale, Giles Chatham Strong, Savannah Thais, Karen Tomko, Eli Upfal, Emanuele Usai, Andrey Ustyuzhanin, Martin Vala, Sofia Vallecorsa, Mauro Verzetti, Xavier Vilasís-Cardona, Jean-Roch Vlimant, Ilija Vukotic, Sean-Jiun Wang, Gordon Watts, Michael Williams, Wenjing Wu, Stefan Wunsch, Omar Zapata

CoRR, 2018

Backdrop: Stochastic Backpropagation.

Siavash Golkar, Kyle Cranmer

CoRR, 2018

Mining gold from implicit models to improve likelihood-free inference.

Johann Brehmer, Gilles Louppe, Juan Pavez, Kyle Cranmer

CoRR, 2018

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

Proceedings of the 14th IEEE International Conference on e-Science, 2018

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, Frank D. Wood, Lukas Heinrich, Gilles Louppe, Kyle Cranmer, Karen Ng, Wahid Bhimji, Prabhat

CoRR, 2017

Adversarial Variational Optimization of Non-Differentiable Simulators.

Gilles Louppe, Kyle Cranmer

CoRR, 2017

Learning to Pivot with Adversarial Networks.

Gilles Louppe, Michael Kagan, Kyle Cranmer

Proceedings of the Advances in Neural Information Processing Systems 30: Annual Conference on Neural Information Processing Systems 2017, 2017

2016

carl: a likelihood-free inference toolbox.

Gilles Louppe, Kyle Cranmer, Juan Pavez

J. Open Source Softw., 2016

Parameterized Machine Learning for High-Energy Physics.

Pierre Baldi, Kyle Cranmer, Taylor Faucett, Peter J. Sadowski, Daniel Whiteson

CoRR, 2016

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 Kashyap, Ashish Mahabal, Aneta Siemiginowska, Aleksandra B. Slavkovic

PLoS Comput. Biol., 2014

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 Kashyap, Ashish Mahabal, Aneta Siemiginowska, Aleksandra B. Slavkovic

CoRR, 2014

2012

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, Rene Brun, Concetta Cartaro, Marco Cattaneo, Gang Chen, David Corney, Kyle Cranmer, Ray Culbertson, Suenje Dallmeier-Tiessen, Dmitri Denisov, Cristinel Diaconu, Vitaliy Dodonov, Tony Doyle, Gregory P. Dubois-Felsmann, Michael Ernst, Martin Gasthuber, Achim Geiser, Fabiola Gianotti, Paolo Giubellino, Andrey Golutvin, John Gordon, Volker Guelzow, Takanori Hara, Hisaki Hayashii, Andreas Heiss, Frederic Hemmer, Fabio Hernandez, Graham Heyes, André G. Holzner, Peter Igo-Kemenes, Toru Iijima, Joe Incandela, Roger Jones, Yves Kemp, Kerstin Kleese van Dam, Juergen Knobloch, David Kreincik, Kati Lassila-Perini, Francois Le Diberder

CoRR, 2012

2005

PhysicsGP: A Genetic Programming approach to event selection.

Kyle Cranmer, R. Sean Bowman

Comput. Phys. Commun., 2005