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Experimental physics actively develops frontiers of our knowledge of the Universe and ranges from macroscopic objects observed through telescopes to micro-world of particle interaction. In each field of study scientists go from raw measurements (celestial objects spectra or energies of detected particles inside collider detectors) to higher levels of the representation that are more suitable for further analysis and to human perception. Each measurement can be used for supporting or refuting certain theory that compete for predictive power and completeness.
In many areas of physical experiments it assimilated computational paradigms a long time ago: both simulators and semi-automatic data analysis techniques have been applied widely for decades. In particular, nonparametric classification and regression are now routinely used as parts of the reconstruction (inference) chain. More recently, state-of-the-art budgeted learning techniques have also started to be used for real-time event selection on LHC. Nevertheless, most of these applications went largely unnoticed by the machine learning (ML) community.
Our primary goal is to bring the Physics and ML communities together to initiate discussions on Physics-motivated problems and applications in ML. It is not unknown that the ML community is still largely untouched by the numerous learning challenges coming from Physics. We hope that as a result of this workshop (as well as a result of the Flavours of Physics challenge organized before the workshop and the new dataset that we shared in its scope that are to be discussed at the workshop), these problems will attract more attention from ML researchers.
Fri 5:30 a.m. - 6:00 a.m.
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Flavors of Physics Challenge
(Talk)
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Andrey Ustyuzhanin 🔗 |
Fri 6:00 a.m. - 6:20 a.m.
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Wonders and Woes of ML Competitions
(Talk)
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Ben Hamner 🔗 |
Fri 6:20 a.m. - 6:50 a.m.
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Data Science at LHCb
(Talk)
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Tim Head 🔗 |
Fri 8:10 a.m. - 8:50 a.m.
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Open ML Problems in High Energy Physics
(Talk)
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Daniel Whiteson 🔗 |
Fri 11:00 a.m. - 11:40 a.m.
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The HiggsML Story
(Talk)
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Balázs Kégl 🔗 |
Fri 11:40 a.m. - 1:00 p.m.
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Deep Learning RNNaissance
(Talk)
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Jürgen Schmidhuber 🔗 |
Fri 12:00 p.m. - 12:30 p.m.
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Flavors of Physics: third place solution
(Talk)
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🔗 |
Fri 12:30 p.m. - 1:00 p.m.
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Identifying Tau to Three Muon Decay Events at the LHCb Using a Combination of Hand-Crafted and Automatic Feature Engineering and Ensemble Algorithms
(Talk)
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Rishab Gargeya 🔗 |
Fri 1:00 p.m. - 1:20 p.m.
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Jet Images
(Talk)
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Luke P. de Oliveira 🔗 |
Fri 1:20 p.m. - 1:45 p.m.
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Output Correction in HEP Using DPGMM
(Talk)
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Adil Omari 🔗 |
Fri 1:45 p.m. - 1:50 p.m.
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Building a Robust Detector Algorithm
(Talk)
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Prateek Tandon 🔗 |
Fri 1:50 p.m. - 2:25 p.m.
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An alternative to ABC for likelihood-free inference
(Talk)
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Kyle Cranmer 🔗 |
Fri 2:30 p.m. - 3:00 p.m.
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Machine Learning in HEP
(Talk)
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Mike Williams 🔗 |
Author Information
Pavel Serdyukov (Yandex)
Andrey Ustyuzhanin (YDF, YSDA)
Marcin Chrząszcz (University of Zurich, Institute of Nuclear Physics PAN)
Francesco Dettori (CERN)
Marc-Olivier Bettler (CERN)
More from the Same Authors
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2019 : Morning Coffee Break & Poster Session »
Eric Metodiev · Keming Zhang · Markus Stoye · Randy Churchill · Soumalya Sarkar · Miles Cranmer · Johann Brehmer · Danilo Jimenez Rezende · Peter Harrington · AkshatKumar Nigam · Nils Thuerey · Lukasz Maziarka · Alvaro Sanchez Gonzalez · Atakan Okan · James Ritchie · N. Benjamin Erichson · Harvey Cheng · Peihong Jiang · Seong Ho Pahng · Samson Koelle · Sami Khairy · Adrian Pol · Rushil Anirudh · Jannis Born · Benjamin Sanchez-Lengeling · Brian Timar · Rhys Goodall · Tamás Kriváchy · Lu Lu · Thomas Adler · Nathaniel Trask · Noëlie Cherrier · Tomohiko Konno · Muhammad Kasim · Tobias Golling · Zaccary Alperstein · Andrei Ustyuzhanin · James Stokes · Anna Golubeva · Ian Char · Ksenia Korovina · Youngwoo Cho · Chanchal Chatterjee · Tom Westerhout · Gorka Muñoz-Gil · Juan Zamudio-Fernandez · Jennifer Wei · Brian Lee · Johannes Kofler · Bruce Power · Nikita Kazeev · Andrey Ustyuzhanin · Artem Maevskiy · Pascal Friederich · Arash Tavakoli · Willie Neiswanger · Bohdan Kulchytskyy · sindhu hari · Paul Leu · Paul Atzberger -
2019 : Catered Lunch and Poster Viewing (in Workshop Room) »
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2019 Poster: Sequence Modeling with Unconstrained Generation Order »
Dmitrii Emelianenko · Elena Voita · Pavel Serdyukov -
2018 : TrackML, a Particle Physics Tracking Machine Learning Challenge, Jean-Roch Vlimant (Caltech), Vincenzo Innocente, Andreas Salzburger (CERN), Isabelle Guyon (ChaLearn), Sabrina Amrouche, Tobias Golling, Moritz Kiehn (Geneva University),David Rousseau∗, Yet »
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2016 Poster: Efficient High-Order Interaction-Aware Feature Selection Based on Conditional Mutual Information »
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2015 : Flavors of Physics Challenge »
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