Frontiers in Probabilistic Inference: learning meets Sampling

Tara Akhound-Sadegh, Marta Skreta, Yuanqi Du, Sarthak Mittal, Joey Bose, Alexander Tong, Kirill Neklyudov, Max Welling, Michael M. Bronstein, Arnaud Doucet, Aapo Hyvarinen

Keywords: Sampling, Probabilistic Inference, Generative AI, AI for scientific discovery

TL;DR: We propose a workshop, Frontiers in Probabilistic Inference: learning meets Sampling (FIP), to foster collaboration between communities working on sampling and learning-based inference.

Abstract: Probabilistic inference, particularly through the use of sampling-based methods, is a cornerstone for modeling across diverse fields, from machine learning and statistics to natural sciences such as physics, biology, and chemistry. However, many challenges exist, including scaling, which has resulted in the development of new machine learning methods. In response to these rapid developments, we propose a workshop, *Frontiers in Probabilistic Inference: learning meets Sampling* (FIP), to foster collaboration between communities working on sampling and learning-based inference. The workshop aims to center community discussions on (i) key challenges in sampling, (ii) new sampling methods, and (iii) their applications to natural sciences and uncertainty estimation. We have assembled an exciting speaker list with diverse perspectives; our goal is that attendees leave with a deeper understanding of the latest advances in sampling methods, practical insights into their applications, and new connections to collaborate on future research endeavors.

Submission Number: 48