Publications with 10 authors or less

  1. Tibor Šimko, Kyle Cranmer, Michael R. Crusoe, Lukas Heinrich, Anton Khodak, Dinos Kousidis, and Diego Rodrı́guez. Search for computational workflow synergies in reproducible research data analyses in particle physics and life sciences. In 14th International Conference on e-Science. 10 2018. [ Bibtex ]
  2. Ben C. Allanach, Kyle Cranmer, Christopher G. Lester, and Arne M. Weber. Natural priors, CMSSM fits and LHC weather forecasts. JHEP, 08:023, 2007. arXiv:0705.0487, doi:10.1088/1126-6708/2007/08/023. [ Bibtex ]
  3. Pierre Baldi, Kyle Cranmer, Taylor Faucett, Peter Sadowski, and Daniel Whiteson. Parameterized neural networks for high-energy physics. Eur. Phys. J. C, 76(5):235, 2016. arXiv:1601.07913, doi:10.1140/epjc/s10052-016-4099-4. [ Bibtex ]
  4. R.Michael Barnett and K. Cranmer. Working Group Report: Communication with the General Public. In Community Summer Study 2013: Snowmass on the Mississippi. 8 2013. [ Bibtex ]
  5. Denis Boyda, Gurtej Kanwar, Sébastien Racanière, Danilo Jimenez Rezende, Michael S. Albergo, Kyle Cranmer, Daniel C. Hackett, and Phiala E. Shanahan. Sampling using $SU(N)$ gauge equivariant flows. 8 2020. arXiv:2008.05456. [ Bibtex ]
  6. Johann Brehmer and Kyle Cranmer. Flows for simultaneous manifold learning and density estimation. NeurIPS2020, 3 2020. arXiv:2003.13913. [ Bibtex ]
  7. Johann Brehmer and Kyle Cranmer. Simulation-based inference methods for particle physics. To appear in Artificial Intelligence for Particle Physics, World Scientific Publishing Co, 10 2020. arXiv:2010.06439. [ Bibtex ]
  8. Johann Brehmer, Kyle Cranmer, Irina Espejo, Alexander Held, Felix Kling, Gilles Louppe, and Juan Pavez. Constraining effective field theories with machine learning. EPJ Web Conf., 245:06026, 2020. doi:10.1051/epjconf/202024506026. [ Bibtex ]
  9. Johann Brehmer, Kyle Cranmer, Irina Espejo, Felix Kling, Gilles Louppe, and Juan Pavez. Effective LHC measurements with matrix elements and machine learning. J. Phys. Conf. Ser., 1525(1):012022, 2020. arXiv:1906.01578, doi:10.1088/1742-6596/1525/1/012022. [ Bibtex ]
  10. Johann Brehmer, Kyle Cranmer, and F. Kling. Improving inference with matrix elements and machine learning. Int. J. Mod. Phys. A, 35(15n16):2041008, 2020. doi:10.1142/S0217751X20410080. [ Bibtex ]
  11. Johann Brehmer, Kyle Cranmer, Felix Kling, and Tilman Plehn. Better Higgs boson measurements through information geometry. Phys. Rev. D, 95(7):073002, 2017. arXiv:1612.05261, doi:10.1103/PhysRevD.95.073002. [ Bibtex ]
  12. Johann Brehmer, Kyle Cranmer, Felix Kling, Tim M.P. Tait, and Tilman Plehn. Better Higgs Measurements through Information Geometry. In 53rd Rencontres de Moriond on QCD and High Energy Interactions, 15–18. 2018. [ Bibtex ]
  13. Johann Brehmer, Kyle Cranmer, Gilles Louppe, and Juan Pavez. A Guide to Constraining Effective Field Theories with Machine Learning. Phys. Rev. D, 98(5):052004, 2018. arXiv:1805.00020, doi:10.1103/PhysRevD.98.052004. [ Bibtex ]
  14. Johann Brehmer, Kyle Cranmer, Gilles Louppe, and Juan Pavez. Constraining Effective Field Theories with Machine Learning. Phys. Rev. Lett., 121(11):111801, 2018. arXiv:1805.00013, doi:10.1103/PhysRevLett.121.111801. [ Bibtex ]
  15. Johann Brehmer, Felix Kling, Irina Espejo, and Kyle Cranmer. MadMiner: Machine learning-based inference for particle physics. Comput. Softw. Big Sci., 4(1):3, 2020. arXiv:1907.10621, doi:10.1007/s41781-020-0035-2. [ Bibtex ]
  16. Johann Brehmer, Gilles Louppe, Juan Pavez, and Kyle Cranmer. Mining gold from implicit models to improve likelihood-free inference. Proc. Nat. Acad. Sci., 117(10):5242–5249, 2020. arXiv:1805.12244, doi:10.1073/pnas.1915980117. [ Bibtex ]
  17. Johann Brehmer, Sebastian Macaluso, Duccio Pappadopulo, and Kyle Cranmer. Hierarchical clustering in particle physics through reinforcement learning. In 34th Conference on Neural Information Processing Systems. 11 2020. arXiv:2011.08191. [ Bibtex ]
  18. Johann Brehmer, Siddharth Mishra-Sharma, Joeri Hermans, Gilles Louppe, and Kyle Cranmer. Mining for Dark Matter Substructure: Inferring subhalo population properties from strong lenses with machine learning. Astrophys. J., 886(1):49, 2019. arXiv:1909.02005, doi:10.3847/1538-4357/ab4c41. [ Bibtex ]
  19. Michael Bridges, Kyle Cranmer, Farhan Feroz, Mike Hobson, Roberto Ruiz de Austri, and Roberto Trotta. A Coverage Study of the CMSSM Based on ATLAS Sensitivity Using Fast Neural Networks Techniques. JHEP, 03:012, 2011. arXiv:1011.4306, doi:10.1007/JHEP03(2011)012. [ Bibtex ]
  20. Giuseppe Carleo, Ignacio Cirac, Kyle Cranmer, Laurent Daudet, Maria Schuld, Naftali Tishby, Leslie Vogt-Maranto, and Lenka Zdeborová. Machine learning and the physical sciences. Rev. Mod. Phys., 91(4):045002, 2019. arXiv:1903.10563, doi:10.1103/RevModPhys.91.045002. [ Bibtex ]
  21. Glen Cowan, Kyle Cranmer, Eilam Gross, and Ofer Vitells. Asymptotic formulae for likelihood-based tests of new physics. Eur. Phys. J. C, 71:1554, 2011. [Erratum: Eur.Phys.J.C 73, 2501 (2013)]. arXiv:1007.1727, doi:10.1140/epjc/s10052-011-1554-0. [ Bibtex ]
  22. Glen Cowan, Kyle Cranmer, Eilam Gross, and Ofer Vitells. Power-Constrained Limits. 5 2011. arXiv:1105.3166. [ Bibtex ]
  23. Glen Cowan, Kyle Cranmer, Eilam Gross, and Ofer Vitells. Asymptotic distribution for two-sided tests with lower and upper boundaries on the parameter of interest. 10 2012. arXiv:1210.6948. [ Bibtex ]
  24. K. Cranmer, Y. Fang, B. Mellado, S. Paganis, W. Quayle, and Lan Wu Sau. Analysis of VBF $H\rightarrow WW\rightarrow \nu \nu $. 3 2004. [ Bibtex ]
  25. K. Cranmer, Y.Q. Fang, B. Mellado, S. Paganis, W. Quayle, and Sau Lan Wu. Prospects for Higgs searches via VBF at the LHC with the ATLAS detector. In 3rd Les Houches Workshop on Physics at TeV Colliders. 1 2004. arXiv:hep-ph/0401148. [ Bibtex ]
  26. K. Cranmer, A. Farbin, and A. Shibata. EventView - The Design Behind an Analysis Framework. 9 2007. [ Bibtex ]
  27. K. Cranmer, P. McNamara, B. Mellado, Y. Pan, W. Quayle, and Lan Wu Sau. Neural Network Based Search for Higgs Boson Produced via VBF with $H \rightarrow W^+W^- \rightarrow l^+ l^- \sla p_T$ for $115 < M_H < 130 \gev $. 12 2002. [ Bibtex ]
  28. K. Cranmer, P. McNamara, B. Mellado, W. Quayle, and Lan Wu Sau. Confidence Level Calculations for $H\rightarrow W^+W^- \rightarrow l^+l^-\sla p_T$ for $115 < M_H < 130\,\gev $ Using Vector Boson Fusion. 12 2002. [ Bibtex ]
  29. K. Cranmer, P. McNamara, B. Mellado, W. Quayle, and Lan Wu Sau. Search for Higgs Bosons Decay $H\rightarrow W^+W^- \rightarrow l^+l^-\sla p_T$ for $115 < M_H < 130\,\gev $ Using Vector Boson Fusion. 11 2002. [ Bibtex ]
  30. K. Cranmer, B. Mellado, W. Quayle, and Lan Wu Sau. Statistical Methods to Assess the Combined Sensitivity of the ATLAS Detector to the Higgs Boson in the Standard Model. 2004. [ Bibtex ]
  31. K. Cranmer, J. Pavez, G. Louppe, and W.K. Brooks. Experiments using machine learning to approximate likelihood ratios for mixture models. J. Phys. Conf. Ser., 762(1):012034, 2016. doi:10.1088/1742-6596/762/1/012034. [ Bibtex ]
  32. K. Cranmer, Lan Wu Sau, W. Quayle, and B. Mellado. Application of K Factors in the $H\rightarrow ZZ^\star \rightarrow 4l$ Analysis at the LHC. 5 2003. [ Bibtex ]
  33. K.S. Cranmer. The ATLAS analysis architecture. Nucl. Phys. B Proc. Suppl., 177-178:126–130, 2008. doi:10.1016/j.nuclphysbps.2007.11.096. [ Bibtex ]
  34. K.S. Cranmer, B. Mellado, W. Quayle, and Sau Lan Wu. Challenges in moving the LEP Higgs statistics to the LHC. eConf, C030908:MODT004, 2003. arXiv:physics/0312050. [ Bibtex ]
  35. Kyle Cranmer. Statistical challenges for searches for new physics at the LHC. In Statistical Problems in Particle Physics, Astrophysics and Cosmology, 112–123. 2006. arXiv:physics/0511028, doi:10.1142/9781860948985_0026. [ Bibtex ]
  36. Kyle Cranmer. Higgs To Four Taus At ALEPH. In 45th Rencontres de Moriond on Electroweak Interactions and Unified Theories, 211–214. Paris, France, 2010. Moriond. [ Bibtex ]
  37. Kyle Cranmer. Combined searches for the Higgs boson with ATLAS and CMS. pages 100–108, 2011. doi:10.5170/CERN-2011-006.100. [ Bibtex ]
  38. Kyle Cranmer. Practical Statistics for the LHC. In 2011 European School of High-Energy Physics, 267–308. 2014. arXiv:1503.07622, doi:10.5170/CERN-2014-003.267. [ Bibtex ]
  39. Kyle Cranmer and R. Sean Bowman. PhysicsGP: A Genetic Programming Approach to Event Selection. Comput. Phys. Commun., 167:165–176, 2005. arXiv:physics/0402030, doi:10.1016/j.cpc.2004.12.006. [ Bibtex ]
  40. Kyle Cranmer, Johann Brehmer, and Gilles Louppe. The frontier of simulation-based inference. In 11 2019. arXiv:1911.01429. [ Bibtex ]
  41. Kyle Cranmer, Siavash Golkar, and Duccio Pappadopulo. Inferring the quantum density matrix with machine learning. 4 2019. arXiv:1904.05903. [ Bibtex ]
  42. Kyle Cranmer and Lukas Heinrich. Yadage and Packtivity - analysis preservation using parametrized workflows. J. Phys. Conf. Ser., 898(10):102019, 2017. arXiv:1706.01878, doi:10.1088/1742-6596/898/10/102019. [ Bibtex ]
  43. Kyle Cranmer and Lukas Heinrich. Analysis Preservation and Systematic Reinterpretation within the ATLAS experiment. J. Phys. Conf. Ser., 1085(4):042011, 2018. doi:10.1088/1742-6596/1085/4/042011. [ Bibtex ]
  44. Kyle Cranmer, Lukas Heinrich, Roger Jones, and David M. South. Analysis Preservation in ATLAS. J. Phys. Conf. Ser., 664(3):032013, 2015. doi:10.1088/1742-6596/664/3/032013. [ Bibtex ]
  45. Kyle Cranmer, Sven Kreiss, David Lopez-Val, and Tilman Plehn. Decoupling Theoretical Uncertainties from Measurements of the Higgs Boson. Phys. Rev. D, 91(5):054032, 2015. arXiv:1401.0080, doi:10.1103/PhysRevD.91.054032. [ Bibtex ]
  46. Kyle Cranmer, George Lewis, Lorenzo Moneta, Akira Shibata, and Wouter Verkerke. HistFactory: A tool for creating statistical models for use with RooFit and RooStats. 6 2012. [ Bibtex ]
  47. Kyle Cranmer and Gilles Louppe. Unifying generative models and exact likelihood-free inference with conditional bijections. 12 2016. doi:10.5281/zenodo.198541. [ Bibtex ]
  48. Kyle Cranmer, Bruce Mellado, William Quayle, and Sau Lan Wu. Application of K factors in the H —\ensuremath > ZZ* —\ensuremath > 4l analysis at the LHC. 7 2003. arXiv:hep-ph/0307242. [ Bibtex ]
  49. Kyle Cranmer, Bruce Mellado, William Quayle, and Sau Lan Wu. Search for Higgs bosons decay H —\ensuremath > gamma gamma using vector boson fusion. 1 2004. arXiv:hep-ph/0401088. [ Bibtex ]
  50. Kyle Cranmer, Juan Pavez, and Gilles Louppe. Approximating Likelihood Ratios with Calibrated Discriminative Classifiers. 6 2015. arXiv:1506.02169. [ Bibtex ]
  51. Kyle Cranmer and Tilman Plehn. Maximum significance at the LHC and Higgs decays to muons. Eur. Phys. J. C, 51:415–420, 2007. arXiv:hep-ph/0605268, doi:10.1140/epjc/s10052-007-0309-4. [ Bibtex ]
  52. Kyle Cranmer and Itay Yavin. RECAST: Extending the Impact of Existing Analyses. JHEP, 04:038, 2011. arXiv:1010.2506, doi:10.1007/JHEP04(2011)038. [ Bibtex ]
  53. Kyle S Cranmer. Multivariate analysis and the search for new particles. Acta Phys. Polon. B, 34:6049–6068, 2003. [ Bibtex ]
  54. Kyle S. Cranmer. Kernel estimation in high-energy physics. Comput. Phys. Commun., 136:198–207, 2001. arXiv:hep-ex/0011057, doi:10.1016/S0010-4655(00)00243-5. [ Bibtex ]
  55. Kyle S. Cranmer. Frequentist hypothesis testing with background uncertainty. eConf, C030908:WEMT004, 2003. arXiv:physics/0310108. [ Bibtex ]
  56. Kyle S. Cranmer. Multivariate analysis from a statistical point of view. eConf, C030908:WEJT002, 2003. arXiv:physics/0310110. [ Bibtex ]
  57. Kyle S. Cranmer. Potential for Higgs physics at the LHC and super-LHC. In 2005 International Linear Collider Physics and Detector Workshop and 2nd ILC Accelerator Workshop. 12 2005. arXiv:hep-ph/0512154. [ Bibtex ]
  58. Kyle S. Cranmer. Searching for new physics: Contributions to LEP and the LHC. PhD thesis, U. Wisconsin, Madison (main), 2005. [ Bibtex ]
  59. Kyle S. Cranmer. Statistics for the LHC: Progress, challenges, and future. In PHYSTAT-LHC Workshop on Statistical Issues for LHC Physics, 47–60. 2007. doi:10.5170/CERN-2008-001.47. [ Bibtex ]
  60. Miles Cranmer, Alvaro Sanchez-Gonzalez, Peter Battaglia, Rui Xu, Kyle Cranmer, David Spergel, and Shirley Ho. Discovering Symbolic Models from Deep Learning with Inductive Biases. NeurIPS2020, 6 2020. arXiv:2006.11287. [ Bibtex ]
  61. Farhan Feroz, Kyle Cranmer, Mike Hobson, Roberto Ruiz de Austri, and Roberto Trotta. Challenges of Profile Likelihood Evaluation in Multi-Dimensional SUSY Scans. JHEP, 06:042, 2011. arXiv:1101.3296, doi:10.1007/JHEP06(2011)042. [ Bibtex ]
  62. Meghan Frate, Kyle Cranmer, Saarik Kalia, Alexander Vandenberg-Rodes, and Daniel Whiteson. Modeling Smooth Backgrounds and Generic Localized Signals with Gaussian Processes. 9 2017. arXiv:1709.05681. [ Bibtex ]
  63. Siavash Golkar and Kyle Cranmer. Backdrop: Stochastic Backpropagation. 6 2018. arXiv:1806.01337. [ Bibtex ]
  64. Craig S. Greenberg, Sebastian Macaluso, Nicholas Monath, Ji-Ah Lee, Patrick Flaherty, Kyle Cranmer, Andrew McGregor, and Andrew McCallum. Data Structures \& Algorithms for Exact Inference in Hierarchical Clustering. 2 2020. arXiv:2002.11661. [ Bibtex ]
  65. Dan Guest, Kyle Cranmer, and Daniel Whiteson. Deep Learning and its Application to LHC Physics. Ann. Rev. Nucl. Part. Sci., 68:161–181, 2018. arXiv:1806.11484, doi:10.1146/annurev-nucl-101917-021019. [ Bibtex ]
  66. Christian Gumpert, Lorenzo Moneta, Kyle Cranmer, Sven Kreiss, and Wouter Verkerke. Software for statistical data analysis used in Higgs searches. J. Phys. Conf. Ser., 490:012229, 2014. doi:10.1088/1742-6596/490/1/012229. [ Bibtex ]
  67. RWL. Jones, DM. South, and KS. Cranmer. ATLAS Data Preservation. J. Phys. Conf. Ser., 664(3):032017, 2015. doi:10.1088/1742-6596/664/3/032017. [ Bibtex ]
  68. Gurtej Kanwar, Michael S. Albergo, Denis Boyda, Kyle Cranmer, Daniel C. Hackett, Sébastien Racanière, Danilo Jimenez Rezende, and Phiala E. Shanahan. Equivariant flow-based sampling for lattice gauge theory. Phys. Rev. Lett., 125(12):121601, 2020. arXiv:2003.06413, doi:10.1103/PhysRevLett.125.121601. [ Bibtex ]
  69. Gilles Louppe, Kyunghyun Cho, Cyril Becot, and Kyle Cranmer. QCD-Aware Recursive Neural Networks for Jet Physics. JHEP, 01:057, 2019. arXiv:1702.00748, doi:10.1007/JHEP01(2019)057. [ Bibtex ]
  70. Gilles Louppe, Kyle Cranmer, and Juan Pavez. carl: a likelihood-free inference toolbox. J. Open Source Softw., 1(1):11, 2016. doi:10.21105/joss.00011. [ Bibtex ]
  71. Gilles Louppe, Joeri Hermans, and Kyle Cranmer. Adversarial Variational Optimization of Non-Differentiable Simulators. 7 2017. arXiv:1707.07113. [ Bibtex ]
  72. Gilles Louppe, Michael Kagan, and Kyle Cranmer. Learning to Pivot with Adversarial Networks. 11 2016. arXiv:1611.01046. [ Bibtex ]
  73. David Malon, Peter van Gemmeren, Arthur Schaffer, Sebastian Binet, Marcin Nowak, Scott Snyder, and Kyle Cranmer. Explicit state representation and the ATLAS event data model: Theory and practice. J. Phys. Conf. Ser., 119:042024, 2008. doi:10.1088/1742-6596/119/4/042024. [ Bibtex ]
  74. Siddharth Mishra-Sharma and Kyle Cranmer. Semi-parametric γ-ray modeling with Gaussian processes and variational inference. In 34th Conference on Neural Information Processing Systems. 10 2020. arXiv:2010.10450. [ Bibtex ]
  75. Lorenzo Moneta, Kevin Belasco, Kyle S. Cranmer, S. Kreiss, Alfio Lazzaro, Danilo Piparo, Gregory Schott, Wouter Verkerke, and Matthias Wolf. The RooStats Project. PoS, ACAT2010:057, 2010. arXiv:1009.1003, doi:10.22323/1.093.0057. [ Bibtex ]
  76. Danilo Jimenez Rezende, George Papamakarios, Sébastien Racanière, Michael S. Albergo, Gurtej Kanwar, Phiala E. Shanahan, and Kyle Cranmer. Normalizing Flows on Tori and Spheres. ICML 2020, 2 2020. arXiv:2002.02428. [ Bibtex ]
  77. Alvaro Sanchez-Gonzalez, Victor Bapst, Kyle Cranmer, and Peter Battaglia. Hamiltonian Graph Networks with ODE Integrators. 9 2019. arXiv:1909.12790. [ Bibtex ]
  78. Alex Schuy, Lukas Heinrich, Kyle Cranmer, and Shih-Chieh Hsu. Extending RECAST for Truth-Level Reinterpretations. In Meeting of the Division of Particles and Fields of the American Physical Society. 10 2019. arXiv:1910.10289. [ Bibtex ]
  79. Hadar Serviansky, Nimrod Segol, Jonathan Shlomi, Kyle Cranmer, Eilam Gross, Haggai Maron, and Yaron Lipman. Set2Graph: Learning Graphs From Sets. NeurIPS2020, 2 2020. arXiv:2002.08772. [ Bibtex ]
  80. Jonathan Shlomi, Sanmay Ganguly, Eilam Gross, Kyle Cranmer, Yaron Lipman, Hadar Serviansky, Haggai Maron, and Nimrod Segol. Secondary Vertex Finding in Jets with Neural Networks. 8 2020. arXiv:2008.02831. [ Bibtex ]
  81. Markus Stoye, Johann Brehmer, Gilles Louppe, Juan Pavez, and Kyle Cranmer. Likelihood-free inference with an improved cross-entropy estimator. 8 2018. arXiv:1808.00973. [ Bibtex ]
  82. Roberto Trotta and Kyle Cranmer. Statistical Challenges of Global SUSY Fits. In PHYSTAT 2011, 170–176. 2011. arXiv:1105.5244, doi:10.5170/CERN-2011-006.170. [ Bibtex ]
  83. Daniel Whiteson, Michael Mulhearn, Chase Shimmin, Kyle Cranmer, Kyle Brodie, and Dustin Burns. Searching for ultra-high energy cosmic rays with smartphones. Astropart. Phys., 79:1–9, 2016. arXiv:1410.2895, doi:10.1016/j.astropartphys.2016.02.002. [ Bibtex ]