Second PHYSTAT Workshop on "Statistics meets ML" in Particle Physics & Astrophysics - VERaIPHY
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 object classification, distance determination, and regression problems. It is used theoretically to enhance simulations and to emulate theoretical predictions that are expensive to compute. It is also increasingly used in simulation-based inference, both for finding informative summary statistics and for variational methods of inference.
PHYSTAT’s first “Statistics meets Machine Learning” aimed to address some of the statistical issues that arise in Particle Physics and Astronomy, with participation from Statistics and Machine Learning experts. These issues are critical, as ML approaches tend to outperform traditional ones in both precision and speed; 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 uses to achieve its results.
Since the first workshop, a coordinated community effort has emerged to better understand and validate the use of ML in the physical sciences. This is the VERaIPHY initiative (“Validation & Evaluation for Robust AI in PHYsics”), which unites researchers from particle physics, astrophysics and statistics to develop principled concepts for assessing the reliability and scientific validity of modern ML methods. Many of the challenges identified within VERaIPHY naturally align with the themes of this workshop. The second workshop will address topics identified in the first workshop and subsequently developed within the VERaIPHY initiative across its various working groups. These include
- Statistical Properties of Training & Generalization
- Uncertainty quantification
- Model robustness
- Inference & Parameter Estimation
- Generative Models & statistical validation
- Hypothesis Testing & Statistical Discovery
- Model Selection, Interpretability & Exp.
- Information Theory and Data Representation
- Symbolic ML
There will be plenty of time for discussion.
The meeting will be hybrid i.e. participation will be either in person or remote. In both cases, registration is necessary, as the Zoom link to the meeting will be sent only to registered participants a few days before the meeting.
In-person registration costs €50 to cover coffee and lunch. The limit for in-person participants is 80.
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/ .
