Kyle Cranmer

Publication Activity (10 Years)

Years Active: 2005-2024
Publications (10 Years): 53

Top Topics

Particle Physics

Probabilistic Inference

Hierarchical Clustering

Top Venues

J. Open Source Softw.

Publications

Abhijith Gandrakota, Lily H. Zhang, Aahlad Manas Puli, Kyle Cranmer, Jennifer Ngadiuba, Rajesh Ranganath, Nhan Tran
Robust Anomaly Detection for Particle Physics Using Multi-Background Representation Learning. CoRR (2024)
Tianji Cai, Garrett W. Merz, François Charton, Niklas Nolte, Matthias Wilhelm, Kyle Cranmer, Lance J. Dixon
Transforming the Bootstrap: Using Transformers to Compute Scattering Amplitudes in Planar N = 4 Super Yang-Mills Theory. CoRR (2024)
Philipp Berens, Kyle Cranmer, Neil D. Lawrence, Ulrike von Luxburg, Jessica Montgomery
AI for Science: An Emerging Agenda. CoRR (2023)
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
Configurable calorimeter simulation for AI applications. Mach. Learn. Sci. Technol. 4 (3) (2023)
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
Normalizing flows for lattice gauge theory in arbitrary space-time dimension. CoRR (2023)
Francesco Armando Di Bello, Anton Charkin-Gorbulin, Kyle Cranmer, Etienne Dreyer, Sanmay Ganguly, Eilam Gross, Lukas Heinrich, Lorenzo Santi, Marumi Kado, Nilotpal Kakati, Patrick Rieck, Matteo Tusoni
Configurable calorimeter simulation for AI applications. CoRR (2023)
Kyle Cranmer, Gurtej Kanwar, Sébastien Racanière, Danilo J. Rezende, Phiala E. Shanahan
Advances in machine-learning-based sampling motivated by lattice quantum chromodynamics. CoRR (2023)
Philipp Berens, Kyle Cranmer, Neil D. Lawrence, Ulrike von Luxburg, Jessica Montgomery
Machine Learning for Science: Bridging Data-Driven and Mechanistic Modelling (Dagstuhl Seminar 22382). Dagstuhl Reports 12 (9) (2022)
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
Aspects of scaling and scalability for flow-based sampling of lattice QCD. CoRR (2022)
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
Flow-based sampling in the lattice Schwinger model at criticality. CoRR (2022)
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
Gauge-equivariant flow models for sampling in lattice field theories with pseudofermions. CoRR (2022)
Daniel C. Hackett, Chung-Chun Hsieh, Michael S. Albergo, Denis Boyda, Jiunn-Wei Chen, Kai-Feng Chen, Kyle Cranmer, Gurtej Kanwar, Phiala E. Shanahan
Flow-based sampling for multimodal distributions in lattice field theory. CoRR (2021)
Sebastian Macaluso, Craig S. Greenberg, Nicholas Monath, Ji Ah Lee, Patrick Flaherty, Kyle Cranmer, Andrew McGregor, Andrew McCallum
Cluster Trellis: Data Structures & Algorithms for Exact Inference in Hierarchical Clustering. AISTATS (2021)
Siddharth Mishra-Sharma, Kyle Cranmer
A neural simulation-based inference approach for characterizing the Galactic Center γ-ray excess. CoRR (2021)
Michael S. Albergo, Gurtej Kanwar, Sébastien Racanière, Danilo J. Rezende, Julian M. Urban, Denis Boyda, Kyle Cranmer, Daniel C. Hackett, Phiala E. Shanahan
Flow-based sampling for fermionic lattice field theories. CoRR (2021)
Sebastian Macaluso, Kyle Cranmer
The Quantum Trellis: A classical algorithm for sampling the parton shower with interference effects. CoRR (2021)
Craig S. Greenberg, Sebastian Macaluso, Nicholas Monath, Avinava Dubey, Patrick Flaherty, Manzil Zaheer, Amr Ahmed, Kyle Cranmer, Andrew McCallum
Exact and approximate hierarchical clustering using A. UAI (2021)
Craig S. Greenberg, Sebastian Macaluso, Nicholas Monath, Avinava Dubey, Patrick Flaherty, Manzil Zaheer, Amr Ahmed, Kyle Cranmer, Andrew McCallum
Exact and Approximate Hierarchical Clustering Using A. CoRR (2021)
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 Macke, Kyle Cranmer, Jiaxin Zhang, Haruko M. Wainwright, Adi Hanuka, Manuela Veloso, Samuel Assefa, Stephan Zheng, Avi Pfeffer
Simulation Intelligence: Towards a New Generation of Scientific Methods. CoRR (2021)
Michael S. Albergo, Denis Boyda, Daniel C. Hackett, Gurtej Kanwar, Kyle Cranmer, Sébastien Racanière, Danilo Jimenez Rezende, Phiala E. Shanahan
Introduction to Normalizing Flows for Lattice Field Theory. CoRR (2021)
Lukas Alexander Heinrich, Matthew Feickert, Giordon Stark, Kyle Cranmer
pyhf: pure-Python implementation of HistFactory statistical models. J. Open Source Softw. 6 (58) (2021)
Johann Brehmer, Felix Kling, Irina Espejo, Kyle Cranmer
MadMiner: Machine Learning-Based Inference for Particle Physics. Comput. Softw. Big Sci. 4 (1) (2020)
Miles D. Cranmer, Alvaro Sanchez-Gonzalez, Peter W. Battaglia, Rui Xu, Kyle Cranmer, David N. Spergel, Shirley Ho
Discovering Symbolic Models from Deep Learning with Inductive Biases. NeurIPS (2020)
Hadar Serviansky, Nimrod Segol, Jonathan Shlomi, Kyle Cranmer, Eilam Gross, Haggai Maron, Yaron Lipman
Set2Graph: Learning Graphs From Sets. CoRR (2020)
Denis Boyda, Gurtej Kanwar, Sébastien Racanière, Danilo Jimenez Rezende, Michael S. Albergo, Kyle Cranmer, Daniel C. Hackett, Phiala E. Shanahan
Sampling using SU(N) gauge equivariant flows. CoRR (2020)
Johann Brehmer, Sebastian Macaluso, Duccio Pappadopulo, Kyle Cranmer
Hierarchical clustering in particle physics through reinforcement learning. CoRR (2020)
Craig S. Greenberg, Sebastian Macaluso, Nicholas Monath, Ji Ah Lee, Patrick Flaherty, Kyle Cranmer, Andrew McGregor, Andrew McCallum
Compact Representation of Uncertainty in Hierarchical Clustering. CoRR (2020)
Danilo Jimenez Rezende, George Papamakarios, Sébastien Racanière, Michael S. Albergo, Gurtej Kanwar, Phiala E. Shanahan, Kyle Cranmer
Normalizing Flows on Tori and Spheres. CoRR (2020)
Danilo Jimenez Rezende, George Papamakarios, Sébastien Racanière, Michael S. Albergo, Gurtej Kanwar, Phiala E. Shanahan, Kyle Cranmer
Normalizing Flows on Tori and Spheres. ICML (2020)
Hadar Serviansky, Nimrod Segol, Jonathan Shlomi, Kyle Cranmer, Eilam Gross, Haggai Maron, Yaron Lipman
Set2Graph: Learning Graphs From Sets. NeurIPS (2020)
Johann Brehmer, Kyle Cranmer
Flows for simultaneous manifold learning and density estimation. NeurIPS (2020)
Gurtej Kanwar, Michael S. Albergo, Denis Boyda, Kyle Cranmer, Daniel C. Hackett, Sébastien Racanière, Danilo Jimenez Rezende, Phiala E. Shanahan
Equivariant flow-based sampling for lattice gauge theory. CoRR (2020)
Johann Brehmer, Kyle Cranmer
Flows for simultaneous manifold learning and density estimation. CoRR (2020)
Miles D. Cranmer, Alvaro Sanchez-Gonzalez, Peter W. Battaglia, Rui Xu, Kyle Cranmer, David N. Spergel, Shirley Ho
Discovering Symbolic Models from Deep Learning with Inductive Biases. CoRR (2020)
Atilim Günes Baydin, Lei Shao, Wahid Bhimji, Lukas Alexander 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
Etalumis: Bringing Probabilistic Programming to Scientific Simulators at Scale. CoRR (2019)
Gilles Louppe, Joeri Hermans, Kyle Cranmer
Adversarial Variational Optimization of Non-Differentiable Simulators. AISTATS (2019)
Atilim Günes Baydin, Lei Shao, Wahid Bhimji, Lukas Alexander 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
Etalumis: bringing probabilistic programming to scientific simulators at scale. SC (2019)
Gilles Louppe, Joeri Hermans, Kyle Cranmer
Adversarial Variational Optimization of Non-Differentiable Simulators. BNAIC/BENELEARN (2019)
Kyle Cranmer, Johann Brehmer, Gilles Louppe
The frontier of simulation-based inference. CoRR (2019)
Atilim Gunes Baydin, Lei Shao, Wahid Bhimji, Lukas Alexander 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
Efficient Probabilistic Inference in the Quest for Physics Beyond the Standard Model. NeurIPS (2019)
Alvaro Sanchez-Gonzalez, Victor Bapst, Kyle Cranmer, Peter W. Battaglia
Hamiltonian Graph Networks with ODE Integrators. CoRR (2019)
Atilim Gunes Baydin, Lukas Alexander Heinrich, Wahid Bhimji, Bradley Gram-Hansen, Gilles Louppe, Lei Shao, Prabhat, Kyle Cranmer, Frank D. Wood
Efficient Probabilistic Inference in the Quest for Physics Beyond the Standard Model. CoRR (2018)
Siavash Golkar, Kyle Cranmer
Backdrop: Stochastic Backpropagation. CoRR (2018)
Tibor Simko, Kyle Cranmer, Michael R. Crusoe, Lukas Alexander Heinrich, Anton Khodak, Dinos Kousidis, Diego Rodriguez
Search for Computational Workflow Synergies in Reproducible Research Data Analyses in Particle Physics and Life Sciences. eScience (2018)
Markus Stoye, Johann Brehmer, Gilles Louppe, Juan Pavez, Kyle Cranmer
Likelihood-free inference with an improved cross-entropy estimator. CoRR (2018)
Johann Brehmer, Gilles Louppe, Juan Pavez, Kyle Cranmer
Mining gold from implicit models to improve likelihood-free inference. CoRR (2018)
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 Gligorov, Tobias Golling, Jonas Graw, Lindsey Gray, Dick Greenwood, Thomas Hacker, John Harvey, Benedikt Hegner, Lukas Alexander 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 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 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
Machine Learning in High Energy Physics Community White Paper. CoRR (2018)
Mario Lezcano Casado, Atilim Gunes Baydin, David Martínez-Rubio, Tuan Anh Le, Frank D. Wood, Lukas Alexander Heinrich, Gilles Louppe, Kyle Cranmer, Karen Ng, Wahid Bhimji, Prabhat
Improvements to Inference Compilation for Probabilistic Programming in Large-Scale Scientific Simulators. CoRR (2017)
Gilles Louppe, Michael Kagan, Kyle Cranmer
Learning to Pivot with Adversarial Networks. NIPS (2017)
Gilles Louppe, Kyle Cranmer
Adversarial Variational Optimization of Non-Differentiable Simulators. CoRR (2017)
Pierre Baldi, Kyle Cranmer, Taylor Faucett, Peter J. Sadowski, Daniel Whiteson
Parameterized Machine Learning for High-Energy Physics. CoRR (2016)
Gilles Louppe, Michael Kagan, Kyle Cranmer
Learning to Pivot with Adversarial Networks. CoRR (2016)
Gilles Louppe, Kyle Cranmer, Juan Pavez
carl: a likelihood-free inference toolbox. J. Open Source Softw. 1 (1) (2016)
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
Ten Simple Rules for the Care and Feeding of Scientific Data. PLoS Comput. Biol. 10 (4) (2014)
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
10 Simple Rules for the Care and Feeding of Scientific Data. CoRR (2014)
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
Status Report of the DPHEP Study Group: Towards a Global Effort for Sustainable Data Preservation in High Energy Physics CoRR (2012)
Kyle Cranmer, R. Sean Bowman
PhysicsGP: A Genetic Programming approach to event selection. Comput. Phys. Commun. 167 (3) (2005)