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ORCIDKyle Cranmer
Person information
- affiliation (PhD 2005): University of Wisconsin-Madison, USA
- affiliation: New York University, USA
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Journal Articles
- 2023
[j6]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): 35042 (2023)- 2021
- [j5]Lukas Heinrich, Matthew Feickert, Giordon Stark, Kyle Cranmer:
pyhf: pure-Python implementation of HistFactory statistical models. J. Open Source Softw. 6(58): 2823 (2021) - 2020
- [j4]Johann Brehmer, Felix Kling, Irina Espejo, Kyle Cranmer:
MadMiner: Machine Learning-Based Inference for Particle Physics. Comput. Softw. Big Sci. 4(1) (2020) - 2016
- [j3]Gilles Louppe, Kyle Cranmer, Juan Pavez:
carl: a likelihood-free inference toolbox. J. Open Source Softw. 1(1): 11 (2016) - 2014
- [j2]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) - 2005
- [j1]Kyle Cranmer, R. Sean Bowman:
PhysicsGP: A Genetic Programming approach to event selection. Comput. Phys. Commun. 167(3): 165-176 (2005)
Conference and Workshop Papers
- 2021
- [c12]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: 2467-2475 - [c11]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: 2061-2071 - 2020
- [c10]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: 8083-8092 - [c9]Johann Brehmer, Kyle Cranmer:
Flows for simultaneous manifold learning and density estimation. NeurIPS 2020 - [c8]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 - [c7]Hadar Serviansky, Nimrod Segol, Jonathan Shlomi, Kyle Cranmer, Eilam Gross, Haggai Maron, Yaron Lipman:
Set2Graph: Learning Graphs From Sets. NeurIPS 2020 - 2019
- [c6]Gilles Louppe, Joeri Hermans, Kyle Cranmer:
Adversarial Variational Optimization of Non-Differentiable Simulators. AISTATS 2019: 1438-1447 - [c5]Gilles Louppe, Joeri Hermans, Kyle Cranmer:
Adversarial Variational Optimization of Non-Differentiable Simulators. BNAIC/BENELEARN 2019 - [c4]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:
Efficient Probabilistic Inference in the Quest for Physics Beyond the Standard Model. NeurIPS 2019: 5460-5473 - [c3]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:
Etalumis: bringing probabilistic programming to scientific simulators at scale. SC 2019: 29:1-29:24 - 2018
- [c2]Tibor Simko, Kyle Cranmer, Michael R. Crusoe, Lukas 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: 403-404 - 2017
- [c1]Gilles Louppe, Michael Kagan, Kyle Cranmer:
Learning to Pivot with Adversarial Networks. NIPS 2017: 981-990
Informal and Other Publications
- 2024
- [i39]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 abs/2401.08777 (2024) - [i38]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 abs/2405.06107 (2024) - 2023
- [i37]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 abs/2303.02101 (2023) - [i36]Philipp Berens, Kyle Cranmer, Neil D. Lawrence, Ulrike von Luxburg, Jessica Montgomery:
AI for Science: An Emerging Agenda. CoRR abs/2303.04217 (2023) - [i35]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 abs/2305.02402 (2023) - [i34]Kyle Cranmer, Gurtej Kanwar, Sébastien Racanière, Danilo J. Rezende, Phiala E. Shanahan:
Advances in machine-learning-based sampling motivated by lattice quantum chromodynamics. CoRR abs/2309.01156 (2023) - 2022
- [i33]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 abs/2202.11712 (2022) - [i32]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 abs/2207.08945 (2022) - [i31]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 abs/2211.07541 (2022) - [i30]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): 150-199 (2022) - 2021
- [i29]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 abs/2101.08176 (2021) - [i28]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 abs/2104.07061 (2021) - [i27]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 abs/2106.05934 (2021) - [i26]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 abs/2107.00734 (2021) - [i25]Siddharth Mishra-Sharma, Kyle Cranmer:
A neural simulation-based inference approach for characterizing the Galactic Center γ-ray excess. CoRR abs/2110.06931 (2021) - [i24]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:
Simulation Intelligence: Towards a New Generation of Scientific Methods. CoRR abs/2112.03235 (2021) - [i23]Sebastian Macaluso, Kyle Cranmer:
The Quantum Trellis: A classical algorithm for sampling the parton shower with interference effects. CoRR abs/2112.12795 (2021) - 2020
- [i22]Danilo Jimenez Rezende, George Papamakarios, Sébastien Racanière, Michael S. Albergo, Gurtej Kanwar, Phiala E. Shanahan, Kyle Cranmer:
Normalizing Flows on Tori and Spheres. CoRR abs/2002.02428 (2020) - [i21]Hadar Serviansky, Nimrod Segol, Jonathan Shlomi, Kyle Cranmer, Eilam Gross, Haggai Maron, Yaron Lipman:
Set2Graph: Learning Graphs From Sets. CoRR abs/2002.08772 (2020) - [i20]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 abs/2002.11661 (2020) - [i19]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 abs/2003.06413 (2020) - [i18]Johann Brehmer, Kyle Cranmer:
Flows for simultaneous manifold learning and density estimation. CoRR abs/2003.13913 (2020) - [i17]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 abs/2006.11287 (2020) - [i16]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 abs/2008.05456 (2020) - [i15]Johann Brehmer, Sebastian Macaluso, Duccio Pappadopulo, Kyle Cranmer:
Hierarchical clustering in particle physics through reinforcement learning. CoRR abs/2011.08191 (2020) - 2019
- [i14]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:
Etalumis: Bringing Probabilistic Programming to Scientific Simulators at Scale. CoRR abs/1907.03382 (2019) - [i13]Alvaro Sanchez-Gonzalez, Victor Bapst, Kyle Cranmer, Peter W. Battaglia:
Hamiltonian Graph Networks with ODE Integrators. CoRR abs/1909.12790 (2019) - [i12]Kyle Cranmer, Johann Brehmer, Gilles Louppe:
The frontier of simulation-based inference. CoRR abs/1911.01429 (2019) - 2018
- [i11]Johann Brehmer, Gilles Louppe, Juan Pavez, Kyle Cranmer:
Mining gold from implicit models to improve likelihood-free inference. CoRR abs/1805.12244 (2018) - [i10]Siavash Golkar, Kyle Cranmer:
Backdrop: Stochastic Backpropagation. CoRR abs/1806.01337 (2018) - [i9]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:
Machine Learning in High Energy Physics Community White Paper. CoRR abs/1807.02876 (2018) - [i8]Atilim Gunes Baydin, Lukas 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 abs/1807.07706 (2018) - [i7]Markus Stoye, Johann Brehmer, Gilles Louppe, Juan Pavez, Kyle Cranmer:
Likelihood-free inference with an improved cross-entropy estimator. CoRR abs/1808.00973 (2018) - 2017
- [i6]Gilles Louppe, Kyle Cranmer:
Adversarial Variational Optimization of Non-Differentiable Simulators. CoRR abs/1707.07113 (2017) - [i5]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:
Improvements to Inference Compilation for Probabilistic Programming in Large-Scale Scientific Simulators. CoRR abs/1712.07901 (2017) - 2016
- [i4]Pierre Baldi, Kyle Cranmer, Taylor Faucett, Peter J. Sadowski, Daniel Whiteson:
Parameterized Machine Learning for High-Energy Physics. CoRR abs/1601.07913 (2016) - [i3]Gilles Louppe, Michael Kagan, Kyle Cranmer:
Learning to Pivot with Adversarial Networks. CoRR abs/1611.01046 (2016) - 2014
- [i2]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 abs/1401.2134 (2014) - 2012
- [i1]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 abs/1205.4667 (2012)
Coauthor Index
Danilo Jimenez Rezende
aka: Danilo J. Rezende
aka: Danilo J. Rezende
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