Following a successful initial iteration of this workshop at ICML 2022, our continued goal for this workshop series is to bring together Machine Learning researchers and domain experts in the field of Astrophysics to discuss the key open issues which hamper the use of Deep Learning for scientific discovery, and to present high-quality and cutting-edge work at the intersection between machine learning and astrophysics.
An important aspect to the success of Machine Learning in Astrophysics is to create a two-way interdisciplinary dialog in which concrete data-analysis challenges can spur the development of dedicated Machine Learning tools, which this workshop aims to facilitate. We expect this workshop to appeal to ICML audiences as an opportunity to connect their research interests to concrete and outstanding scientific challenges.
We welcome in particular submissions that target or report on the following non-exhaustive list of problems:
- Efficient high-dimensional inference
- Robustness to covariate shifts and model misspecification
- Anomaly and outlier detection, search for rare signals with ML
- Methods for accurate uncertainty quantification
- Methods for improving interpretability of models
- (Astro)-physics informed models, symmetry and equivariance-preserving models
- Methods of emulation / acceleration of simulation models
- Benchmarking and deployment of ML models for large-scale data analysis
We encourage both submissions on these topics with an astrophysics focus, as well as more methodologically oriented works with potential applications in the physical sciences.
Checkout the website: https://ml4astro.github.io/icml2023/
Abstract submission deadline: May 19th 25th