Statistics meets ML

9 Sept 2024, 14:00 → 12 Sept 2024, 18:00 Europe/London

Imperial College, London

Louis Lyons (Imperial College (GB)), Lydia Brenner (Nikhef National institute for subatomic physics (NL)), Nicholas Wardle (Imperial College (GB)), Olaf Behnke (DESY), Sara Algeri (University of Minnesota)

PHYSTAT Workshop on "Statistics meets ML" in Particle Physics & Astrophysics.

Please note that the Indico page for this meeting is now https://indico.cern.ch/event/1407421/

 

THEME OF MEETING:
 
In recent years, ML has become more and more integrated into many stages of our analyses.  In Particle Physics, it includes data collection and processing (triggering, tracking, etc), classification of different particle types, unfolding, parameter determination, anomaly detection, and even end-to-end processing.  In astronomy its use is increasingly widespread in areas such as classification of objects, distance determination, and regression problems.  It is used theoretically to enhance simulations, and in emulation of theoretical predictions that are expensive to compute.  It is also increasingly used in simulation-based inference, both in finding informative summary statistics, and in variational methods of inference.
 
PHYSTAT’s “Statistics meets Machine Learning” aims to address some of the statistical issues that arise in these applications in Particle Physics and in Astronomy, with participation also of Statistics and Machine Learning experts . These issues are particularly important as the ML approaches tend to outperform traditional ones in terms of precision; the question is whether they are also more accurate. A problem is that in general it is hard to understand the procedure the ML method is adopting to achieve its result. 
 
There will be plenty of time for discussion, and in the context of “PHYSTAT for the Future” preference for Contributed Talks and Posters will be given to younger researchers.
 
 
 
SOME OF THE ISSUES:

1: UNCERTAINTY QUANTIFICATION
Statistical/Systematic v Aleatoric/Epistemic Uncertainties
Specific uncertainty from ML procedure.

2: INTERPRETABLE AI
Is it necessary?
How to achieve it?

3: TRAINING SAMPLES
Do they cover the data in n-D space?
Mismodelling: The ‘friendly v enemy tanks’ effect
Does ML concentrate on poorly modelled features?

4: GENERATIVE METHODS
Limitations on large sample of learnt simulation, from small full simulation

5: STATISTICAL CHECKS
What checks are needed for our various ML procedures?

 

PHYSTAT

The PHYSTAT series of Workshops started in 2000. They were the first meetings devoted solely to the statistical issues that occur in analyses in Particle Physics and neighbouring fields.The homepage of PHYSTAT with a list of all Workshops, Seminars and Informal Reviews is at https://phystat.github.io/Website/ 

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