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Machine learning for phenomenology

3–6 Apr 2018

IPPP Durham

Europe/London timezone

Contribution List

1. Introduction

Ben Nachman (LBNL)

03/04/2018, 14:00

General overview
2. How to figure out what information the machine is learning from

Bryan Ostdiek (University of Oregon)

03/04/2018, 15:30

General overview
16. Generative models introduction

Michela Paganini (Yale)

03/04/2018, 16:15

General overview
3. Discriminating gluon fusion and VBF in Higgs production with machine learning

Nicolas Greiner (University of Zurich)

04/04/2018, 09:30

Jets
4. PDFs from NN to ML

Stefano Forte (Milan University)

04/04/2018, 10:00

Jets
14. Machine learnng: new ideas for PDF determination

Stefano Carrazza

04/04/2018, 10:30

Jets
5. Generative models

Michela Paganini (Yale)

04/04/2018, 11:30

Jets
6. Identification of Hadronically Decaying W Bosons and Top Quarks Using Machine Learning in ATLAS

Ece Akilli

04/04/2018, 14:00

b tagging
7. Mauro Verzetti

04/04/2018, 14:45

b tagging
17. Use of Machine Learning Techniques for improved Monte Carlo Integration

Josh Bendavid (CERN)

04/04/2018, 16:00

b tagging
8. Zihao Jiang

05/04/2018, 09:30

Analysis-level ML
10. Kristian Gregeren

05/04/2018, 10:15

Analysis-level ML
12. Chase Shimmin

05/04/2018, 14:45

New ML methods
13. Clustering techniques for parameter space scanning in BSM

Dorigo Tommaso

05/04/2018, 16:00

New ML methods
11. Weak supervision

Bryan Ostdiek (University of Oregon)

05/04/2018, 16:45

New ML methods
9. David Wardrope

Analysis-level ML
15. Machine Learning for Boosted W-tagging in CMS

Thea Årrestad

New prospects