Speaker
Description
Machine learning is making its path into natural sciences. A key limitation in ML from a science perspective is the black-box nature of deep neural networks. An alternative is to learn succinct mathematical equations, thus interpretable models, directly from data, allowing for a deeper understanding and scientific reasoning, making the path toward new scientific discovery. Symbolic regression is the ML subfield to infer analytical models from data. It has gained a growing interest in recent years.
Most symbolic regression applications make use of synthetic data. However, recent applications used experimental data measured in high-energy physics and astronomy, showing promise in learning fundamental physics laws and phenomenological models in physics. We present a review of symbolic regression applications to high-energy physics data, highlight the main limitations of symbolic regression, and discuss new perspectives.
Artificial Intelligence Review 57 (1), 2
PNAS Nexus 3 (11) pgae467
Machine Learning: Science and Technology (accepted manuscript) DOI 10.1088/2632-2153/adb3ec, arXiv: 2501.07123
Oral talk at ACM KDD 2024, Barcelona
Oral talk at AAAI 2025, Philadelphia
| Would you like to be considered for an oral presentation? | Yes |
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