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We present a machine learning approach for model-independent new physics searches.
The Large-Sample Distribution of the Likelihood Ratio for Testing Composite Hypotheses
S. S. Wilks · 1938
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Tests of statistical hypotheses concerning several parameters when the number of observations is large
Abraham Wald · 1943
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Search for new physics in e μ \mu X data at DØ using SLEUTH: A quasi-model-independent search strategy for new physics
B. Abbott et al · 2000
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A Quasi model independent search for new physics at large transverse momentum
V. M. Abazov et al · 2001
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A General search for new phenomena in ep scattering at HERA
A. Aktas et al · 2004
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Universal kernels
Charles A. Micchelli, Yuesheng Xu, and Haizhang Zhang · 2006
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Model-Independent and Quasi-Model-Independent Search for New Physics at CDF
T. Aaltonen et al · 2008
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Support vector machines
Andreas Christmann and Ingo Steinwart · 2008
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A General Search for New Phenomena at HERA
F. D. Aaron et al · 2009
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Global Search for New Physics with 2.0 fb -1
T. Aaltonen et al · 2009
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The Elements of Statistical Learning: Data Mining, Inference, and Prediction
T. Hastie, R. Tibshirani, and J.H. Friedman · 2009
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On hypothesis testing, trials factor, hypertests and the BumpHunter
Georgios Choudalakis · 2011
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Asymptotic formulae for likelihood-based tests of new physics
Glen Cowan, Kyle Cranmer, Eilam Gross, and Ofer Vitells · 2013
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Sharp analysis of low-rank kernel matrix approximations, 2013
Francis Bach · 2013
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Understanding machine learning: From theory to algorithms
Shai Shalev-Shwartz and Shai Ben-David · 2014
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Searching for Exotic Particles in High-Energy Physics with Deep Learning
Pierre Baldi, Peter Sadowski, and Daniel Whiteson · 2014
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Machine learning and multivariate goodness of fit
Constantin Weisser and Mike Williams · 2016
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Less is more: Nyström computational regularization, 2016
Alessandro Rudi, Raffaello Camoriano, and Lorenzo Rosasco · 2016
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Digging Deeper for New Physics in the LHC Data
Pouya Asadi, Matthew R. Buckley, Anthony DiFranzo, Angelo Monteux, and David Shih · 2017
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Anomaly Detection for Resonant New Physics with Machine Learning
Jack H. Collins, Kiel Howe, and Benjamin Nachman · 2018
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But how does it work in theory? linear svm with random features
Yitong Sun, Anna Gilbert, and Ambuj Tewari · 2018
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Falkon: An optimal large scale kernel method
Alessandro Rudi, Luigi Carratino, and Lorenzo Rosasco · 2018
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Learning New Physics from a Machine
Raffaele Tito D’Agnolo and Andrea Wulzer · 2019
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Variational Autoencoders for New Physics Mining at the Large Hadron Collider
Olmo Cerri, Thong Q. Nguyen, Maurizio Pierini, Maria Spiropulu, and Jean-Roch Vlimant · 2019
Learning the latent structure of collider events
B. M. Dillon, D. A. Faroughy, J. F. Kamenik, and M. Szewc · 2020
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Variational Autoencoders for Anomalous Jet Tagging
Taoli Cheng, Jean-François Arguin, Julien Leissner-Martin, Jacinthe Pilette, and Tobias Golling · 2020
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Anomaly Awareness
Charanjit K. Khosa and Veronica Sanz · 2020
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Anomaly Detection for Physics Analysis and Less than Supervised Learning
Benjamin Nachman · 2020
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Quasi Anomalous Knowledge: Searching for new physics with embedded knowledge
Sang Eon Park, Dylan Rankin, Silviu-Marian Udrescu, Mikaeel Yunus, and Philip Harris · 2020
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Kernel methods through the roof: handling billions of points efficiently, 2020
Giacomo Meanti, Luigi Carratino, Lorenzo Rosasco, and Alessandro Rudi · 2020
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Guiding New Physics Searches with Unsupervised Learning
Andrea De Simone and Thomas Jacques · 2019
Cited alongside, same era.
Adversarially-trained autoencoders for robust unsupervised new physics searches
Andrew Blance, Michael Spannowsky, and Philip Waite · 2019
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QCD or What?
Theo Heimel, Gregor Kasieczka, Tilman Plehn, and Jennifer M. Thompson · 2019
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Extending the search for new resonances with machine learning
Jack H. Collins, Kiel Howe, and Benjamin Nachman · 2019
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Statistical and computational trade-offs in kernel k-means, 2019
Daniele Calandriello and Lorenzo Rosasco · 2019
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Globally convergent newton methods for ill-conditioned generalized self-concordant losses
Ulysse Marteau-Ferey, Francis Bach, and Alessandro Rudi · 2019
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Learning multivariate new physics
Raffaele Tito D’Agnolo, Gaia Grosso, Maurizio Pierini, Andrea Wulzer, and Marco Zanetti · 2021
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Tag N’ Train: a technique to train improved classifiers on unlabeled data
Oz Amram and Cristina Mantilla Suarez · 2021
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Bump Hunting in Latent Space
Blaž Bortolato, Barry M. Dillon, Jernej F. Kamenik, and Aleks Smolkovič · 2021
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Autoencoders for unsupervised anomaly detection in high energy physics
Thorben Finke, Michael Krämer, Alessandro Morandini, Alexander Mück, and Ivan Oleksiyuk · 2021
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High-dimensional Anomaly Detection with Radiative Return in e + e − e^{+}e^{-} Collisions
Julia Gonski, Jerry Lai, Benjamin Nachman, and Inês Ochoa · 2021
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Classifying Anomalies THrough Outer Density Estimation (CATHODE)
Anna Hallin, Joshua Isaacson, Gregor Kasieczka, Claudius Krause, Benjamin Nachman, Tobias Quadfasel, Matthias Schlaffer, David Shih, and Manuel Sommerhalder · 2021
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Deep Set Auto Encoders for Anomaly Detection in Particle Physics
Bryan Ostdiek · 2021
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Model-Independent Detection of New Physics Signals Using Interpretable Semi-Supervised Classifier Tests
Purvasha Chakravarti, Mikael Kuusela, Jing Lei, and Larry Wasserman · 2021
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Learning New Physics from an Imperfect Machine
Raffaele Tito d’Agnolo, Gaia Grosso, Maurizio Pierini, Andrea Wulzer, and Marco Zanetti · 2021
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Generalization properties of learning with random features, 2021
Alessandro Rudi and Lorenzo Rosasco · 2021
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Towards a unified analysis of random fourier features, 2021
Zhu Li, Jean-Francois Ton, Dino Oglic, and Dino Sejdinovic · 2021
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