Andrey Ustyuzhanin (Yandex School of Data Analysis (RU))
Ben Hamner (Kaggle)
Overview and development on collaborative aspects of machine learning competitions
Tim Head (Ecole Polytechnique Federale de Lausanne (CH))
Machine learning is used at all stages of the LHCb experiment. It is routinely used: in the process of deciding which data to record and which to reject forever, as part of the reconstruction algorithms (feature engineering), and in the extraction of physics results from our data. This talk will highlight current use cases, as well as ideas for ambitious future applications, and how we...
Michal Wojcik (Avast)
Alexander Gramolin (Budker Institute of Nuclear Physics)
Alexander Guschin, Vlad Mironov
Marcin Chrzaszcz (Universitaet Zuerich (CH), Institute of Nuclear Physics (PL))
Daniel Whiteson (University of California Irvine (US))
Machine Learning tools have revolutionized data analysis in high-energy physics (HEP). But the problems posed by HEP are unique in many aspects, presenting novel challenges and requiring novel solutions. I will describe recent progress in tackling these open problems and describe current outstanding issues.
Dr Gilles Louppe (New York University (US)), Tim Head (Ecole Polytechnique Federale de Lausanne (CH))
10. Flavours of Physics: Identifying Tau to Three Muon Decay Events at the LHCb Using a Combination of Hand-Crafted and Automatic Feature Engineering and Ensemble Algorithms
Arjun Subramaniam, Rishab Gargeya
Balázs Kégl (CNRS / Université Paris-Saclay)
We will develop a constructive criticism of the data challenge format practiced today. It will be illustrated by our story of the HiggsML challenge, but our conclusions will go beyond. In a nutshell, challenges are long job interviews for participants, publicity for organizers, and benchmarking and teaching aids for the data science community. What are they not? They will not deliver a...
Prof. Jürgen Schmidhuber
In recent years, our deep artificial neural networks (including recurrent ones) have won numerous contests in pattern recognition and machine learning. They are now widely used in industry. I will briefly review deep supervised / unsupervised / reinforcement learning, and discuss the latest state of the art results in numerous applications.
Luke Percival De Oliveira (SLAC National Accelerator Laboratory (US))
Building on the notion of a particle physics detector as a camera we investigate the potential of deep learning architectures to identify highly boosted W bosons. We develop techniques for visualizing features learned deep networks and what additional information is used to improve performance. Our study of physically-motivated features and learning algorithms is general and can be used to...
Adil Omari (Computer Science Dept. at Universidad Autonoma de Madrid), Juan Jose Choquehuanca-Zevallos, Roberto Dıaz-Morales
Machine learning algorithms have offered solutions to a wide range of problems, and some of the tasks found in the high energy physics field are one of them. However, given the nature of problems that are being faced in this field, machine estimates have to meet certain conditions (for instance, the Cramer-von Mises and Kolmogorov-Smirnov tests). Even more, when ensemble of classifiers is used...
18. Building a Robust Detector Algorithm: Application of Bayesian, Nonparametric, and Poisson Methods to Improve Photon Denoising
Kyle Stuart Cranmer (New York University (US))
The field of particle physics has the luxury of very predictive models of the data based on quantum field theory; however, the simulation of a complicated experimental apparatus makes it impractical to directly evaluate the likelihood for a given observation. A popular approach to this class of problems is Approximate Bayesian Computation (ABC). I will describe an alternative technique for...
J Michael Williams (Massachusetts Inst. of Technology (US))
Over the past decade, the use of machine learning algorithms to classify event types has become commonplace in particle physics. However, in many cases it's not obvious how to teach the machine what the physicist wants it to learn. I will discuss some modified classifiers developed for use in such cases, and then reflect on the questions: What is it that physicists actually do when analyzing...