Kyle Cranmer

64 papers A* 5A 4Journal 52Unranked 3
YearRankTypeTitle / Venue / Authors
2026 J jnl
Efficient estimation of unfactorizable systematic uncertainties.
Mach. Learn. Sci. Technol.
Alexis Romero, Kyle Cranmer, Daniel Whiteson
2025 J jnl
Multimodal Datasets with Controllable Mutual Information.
CoRR
Raheem Karim Hashmani, Garrett W. Merz, Helen Qu, Mariel Pettee, Kyle Cranmer
2025 J jnl
Neural quasiprobabilistic likelihood ratio estimation with negatively weighted data.
Mach. Learn. Sci. Technol.
Matthew Drnevich, Stephen Jiggins, Judith Katzy, Kyle Cranmer
2025 J jnl
Quasiprobabilistic Density Ratio Estimation with a Reverse Engineered Classification Loss Function.
CoRR
Matthew Drnevich, Stephen Jiggins, Kyle Cranmer
2025 J jnl
Self-supervised learning strategies for jet physics.
Mach. Learn. Sci. Technol.
Patrick Rieck, Kyle Cranmer, Etienne Dreyer, Eilam Gross, Nilotpal Kakati, Dmitrii Kobylianskii, Garrett W. Merz, Nathalie Soybelman
2024 J jnl
Neural Quasiprobabilistic Likelihood Ratio Estimation with Negatively Weighted Data.
CoRR
Matthew Drnevich, Stephen Jiggins, Judith Katzy, Kyle Cranmer
2024 J jnl
Robust Anomaly Detection for Particle Physics Using Multi-Background Representation Learning.
CoRR
Abhijith Gandrakota, Lily H. Zhang, Aahlad Manas Puli, Kyle Cranmer, Jennifer Ngadiuba, Rajesh Ranganath, Nhan Tran
2024 J jnl
Robust anomaly detection for particle physics using multi-background representation learning.
Mach. Learn. Sci. Technol.
Abhijith Gandrakota, Lily H. Zhang, Aahlad Manas Puli, Kyle Cranmer, Jennifer Ngadiuba, Rajesh Ranganath, Nhan Tran
2024 J jnl
Transforming the Bootstrap: Using Transformers to Compute Scattering Amplitudes in Planar N = 4 Super Yang-Mills Theory.
CoRR
Tianji Cai, Garrett W. Merz, François Charton, Niklas Nolte, Matthias Wilhelm, Kyle Cranmer, Lance J. Dixon
2024 J jnl
Transforming the bootstrap: using transformers to compute scattering amplitudes in planar N = 4 super Yang-Mills theory.
Mach. Learn. Sci. Technol.
Tianji Cai, Garrett W. Merz, François Charton, Niklas Nolte, Matthias Wilhelm, Kyle Cranmer, Lance J. Dixon
2023 J jnl
AI for Science: An Emerging Agenda.
CoRR
Philipp Berens, Kyle Cranmer, Neil D. Lawrence, Ulrike von Luxburg, Jessica Montgomery
2023 J jnl
Advances in machine-learning-based sampling motivated by lattice quantum chromodynamics.
CoRR
Kyle Cranmer, Gurtej Kanwar, Sébastien Racanière, Danilo J. Rezende, Phiala E. Shanahan
2023 J jnl
Configurable calorimeter simulation for AI applications.
CoRR
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
2023 J jnl
Configurable calorimeter simulation for AI applications.
Mach. Learn. Sci. Technol.
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
2023 J jnl
Normalizing flows for lattice gauge theory in arbitrary space-time dimension.
CoRR
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
2022 J jnl
Aspects of scaling and scalability for flow-based sampling of lattice QCD.
CoRR
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
2022 J jnl
Flow-based sampling in the lattice Schwinger model at criticality.
CoRR
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
2022 J jnl
Gauge-equivariant flow models for sampling in lattice field theories with pseudofermions.
CoRR
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
2022 J jnl
Machine Learning for Science: Bridging Data-Driven and Mechanistic Modelling (Dagstuhl Seminar 22382).
Dagstuhl Reports
Philipp Berens, Kyle Cranmer, Neil D. Lawrence, Ulrike von Luxburg, Jessica Montgomery
2021 J jnl
A neural simulation-based inference approach for characterizing the Galactic Center γ-ray excess.
CoRR
Siddharth Mishra-Sharma, Kyle Cranmer
2021 A conf
Cluster Trellis: Data Structures & Algorithms for Exact Inference in Hierarchical Clustering.
AISTATS
Sebastian Macaluso, Craig S. Greenberg, Nicholas Monath, Ji Ah Lee, Patrick Flaherty, Kyle Cranmer, Andrew McGregor, Andrew McCallum
2021 J jnl
Exact and Approximate Hierarchical Clustering Using A.
CoRR
Craig S. Greenberg, Sebastian Macaluso, Nicholas Monath, Avinava Dubey, Patrick Flaherty, Manzil Zaheer, Amr Ahmed, Kyle Cranmer, Andrew McCallum
2021 A conf
Exact and approximate hierarchical clustering using A.
UAI
Craig S. Greenberg, Sebastian Macaluso, Nicholas Monath, Avinava Dubey, Patrick Flaherty, Manzil Zaheer, Amr Ahmed, Kyle Cranmer, Andrew McCallum
2021 J jnl
Flow-based sampling for fermionic lattice field theories.
CoRR
Michael S. Albergo, Gurtej Kanwar, Sébastien Racanière, Danilo J. Rezende, Julian M. Urban, Denis Boyda, Kyle Cranmer, Daniel C. Hackett, Phiala E. Shanahan
2021 J jnl
Flow-based sampling for multimodal distributions in lattice field theory.
CoRR
Daniel C. Hackett, Chung-Chun Hsieh, Michael S. Albergo, Denis Boyda, Jiunn-Wei Chen, Kai-Feng Chen, Kyle Cranmer, Gurtej Kanwar, Phiala E. Shanahan
2021 J jnl
Introduction to Normalizing Flows for Lattice Field Theory.
CoRR
Michael S. Albergo, Denis Boyda, Daniel C. Hackett, Gurtej Kanwar, Kyle Cranmer, Sébastien Racanière, Danilo Jimenez Rezende, Phiala E. Shanahan
2021 J jnl
Simulation Intelligence: Towards a New Generation of Scientific Methods.
CoRR
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
2021 J jnl
The Quantum Trellis: A classical algorithm for sampling the parton shower with interference effects.
CoRR
Sebastian Macaluso, Kyle Cranmer
2021 J jnl
pyhf: pure-Python implementation of HistFactory statistical models.
J. Open Source Softw.
Lukas Heinrich, Matthew Feickert, Giordon Stark, Kyle Cranmer
2020 J jnl
Compact Representation of Uncertainty in Hierarchical Clustering.
CoRR
Craig S. Greenberg, Sebastian Macaluso, Nicholas Monath, Ji Ah Lee, Patrick Flaherty, Kyle Cranmer, Andrew McGregor, Andrew McCallum
2020 A* conf
Discovering Symbolic Models from Deep Learning with Inductive Biases.
NeurIPS
Miles D. Cranmer, Alvaro Sanchez-Gonzalez, Peter W. Battaglia, Rui Xu, Kyle Cranmer, David N. Spergel, Shirley Ho
2020 J jnl
Discovering Symbolic Models from Deep Learning with Inductive Biases.
CoRR
Miles D. Cranmer, Alvaro Sanchez-Gonzalez, Peter W. Battaglia, Rui Xu, Kyle Cranmer, David N. Spergel, Shirley Ho
2020 J jnl
Equivariant flow-based sampling for lattice gauge theory.
CoRR
Gurtej Kanwar, Michael S. Albergo, Denis Boyda, Kyle Cranmer, Daniel C. Hackett, Sébastien Racanière, Danilo Jimenez Rezende, Phiala E. Shanahan
2020 A* conf
Flows for simultaneous manifold learning and density estimation.
NeurIPS
Johann Brehmer, Kyle Cranmer
2020 J jnl
Flows for simultaneous manifold learning and density estimation.
CoRR
Johann Brehmer, Kyle Cranmer
2020 J jnl
Hierarchical clustering in particle physics through reinforcement learning.
CoRR
Johann Brehmer, Sebastian Macaluso, Duccio Pappadopulo, Kyle Cranmer
2020 J jnl
MadMiner: Machine Learning-Based Inference for Particle Physics.
Comput. Softw. Big Sci.
Johann Brehmer, Felix Kling, Irina Espejo, Kyle Cranmer
2020 A* conf
Normalizing Flows on Tori and Spheres.
ICML
Danilo Jimenez Rezende, George Papamakarios, Sébastien Racanière, Michael S. Albergo, Gurtej Kanwar, Phiala E. Shanahan, Kyle Cranmer
2020 J jnl
Normalizing Flows on Tori and Spheres.
CoRR
Danilo Jimenez Rezende, George Papamakarios, Sébastien Racanière, Michael S. Albergo, Gurtej Kanwar, Phiala E. Shanahan, Kyle Cranmer
2020 J jnl
Sampling using SU(N) gauge equivariant flows.
CoRR
Denis Boyda, Gurtej Kanwar, Sébastien Racanière, Danilo Jimenez Rezende, Michael S. Albergo, Kyle Cranmer, Daniel C. Hackett, Phiala E. Shanahan
2020 A* conf
Set2Graph: Learning Graphs From Sets.
NeurIPS
Hadar Serviansky, Nimrod Segol, Jonathan Shlomi, Kyle Cranmer, Eilam Gross, Haggai Maron, Yaron Lipman
2020 J jnl
Set2Graph: Learning Graphs From Sets.
CoRR
Hadar Serviansky, Nimrod Segol, Jonathan Shlomi, Kyle Cranmer, Eilam Gross, Haggai Maron, Yaron Lipman
2019 A conf
Adversarial Variational Optimization of Non-Differentiable Simulators.
AISTATS
Gilles Louppe, Joeri Hermans, Kyle Cranmer
2019 — conf
Adversarial Variational Optimization of Non-Differentiable Simulators.
BNAIC/BENELEARN
Gilles Louppe, Joeri Hermans, Kyle Cranmer
2019 A* conf
Efficient Probabilistic Inference in the Quest for Physics Beyond the Standard Model.
NeurIPS
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
2019 J jnl
Etalumis: Bringing Probabilistic Programming to Scientific Simulators at Scale.
CoRR
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
2019 A conf
Etalumis: bringing probabilistic programming to scientific simulators at scale.
SC
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
2019 J jnl
Hamiltonian Graph Networks with ODE Integrators.
CoRR
Alvaro Sanchez-Gonzalez, Victor Bapst, Kyle Cranmer, Peter W. Battaglia
2019 J jnl
The frontier of simulation-based inference.
CoRR
Kyle Cranmer, Johann Brehmer, Gilles Louppe
2018 J jnl
Backdrop: Stochastic Backpropagation.
CoRR
Siavash Golkar, Kyle Cranmer
2018 J jnl
Efficient Probabilistic Inference in the Quest for Physics Beyond the Standard Model.
CoRR
Atilim Gunes Baydin, Lukas Heinrich, Wahid Bhimji, Bradley Gram-Hansen, Gilles Louppe, Lei Shao, Prabhat, Kyle Cranmer, Frank D. Wood
2018 J jnl
Likelihood-free inference with an improved cross-entropy estimator.
CoRR
Markus Stoye, Johann Brehmer, Gilles Louppe, Juan Pavez, Kyle Cranmer
2018 J jnl
Mining gold from implicit models to improve likelihood-free inference.
CoRR
Johann Brehmer, Gilles Louppe, Juan Pavez, Kyle Cranmer
2018 — conf
Search for Computational Workflow Synergies in Reproducible Research Data Analyses in Particle Physics and Life Sciences.
eScience
Tibor Simko, Kyle Cranmer, Michael R. Crusoe, Lukas Heinrich, Anton Khodak, Dinos Kousidis, Diego Rodriguez
2017 J jnl
Adversarial Variational Optimization of Non-Differentiable Simulators.
CoRR
Gilles Louppe, Kyle Cranmer
2017 J jnl
Improvements to Inference Compilation for Probabilistic Programming in Large-Scale Scientific Simulators.
CoRR
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
2017 — conf
Learning to Pivot with Adversarial Networks.
NIPS
Gilles Louppe, Michael Kagan, Kyle Cranmer
2016 J jnl
Learning to Pivot with Adversarial Networks.
CoRR
Gilles Louppe, Michael Kagan, Kyle Cranmer
2016 J jnl
Parameterized Machine Learning for High-Energy Physics.
CoRR
Pierre Baldi, Kyle Cranmer, Taylor Faucett, Peter J. Sadowski, Daniel Whiteson
2016 J jnl
carl: a likelihood-free inference toolbox.
J. Open Source Softw.
Gilles Louppe, Kyle Cranmer, Juan Pavez
2014 J jnl
10 Simple Rules for the Care and Feeding of Scientific Data.
CoRR
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, Ashish Mahabal, Aneta Siemiginowska, Aleksandra B. Slavkovic
2014 J jnl
Ten Simple Rules for the Care and Feeding of Scientific Data.
PLoS Comput. Biol.
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, Ashish Mahabal, Aneta Siemiginowska, Aleksandra B. Slavkovic
2012 J jnl
Status Report of the DPHEP Study Group: Towards a Global Effort for Sustainable Data Preservation in High Energy Physics
CoRR
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
2005 J jnl
PhysicsGP: A Genetic Programming approach to event selection.
Comput. Phys. Commun.
Kyle Cranmer, R. Sean Bowman