Probabilistic Model
Probabilistic models are mathematical frameworks used to represent and reason under uncertainty, aiming to quantify the likelihood of different outcomes. Current research focuses on improving the efficiency and accuracy of these models across diverse applications, including generative AI (e.g., diffusion models, sum-product networks), uncertainty quantification in large language models, and robust inference in Bayesian networks. This work is significant because it enhances the reliability and interpretability of AI systems, leading to improved decision-making in various fields such as healthcare, finance, and scientific discovery.
Papers
Estimators of Entropy and Information via Inference in Probabilistic Models
Feras A. Saad, Marco Cusumano-Towner, Vikash K. Mansinghka
Partitioned Variational Inference: A Framework for Probabilistic Federated Learning
Matthew Ashman, Thang D. Bui, Cuong V. Nguyen, Stratis Markou, Adrian Weller, Siddharth Swaroop, Richard E. Turner
Loss as the Inconsistency of a Probabilistic Dependency Graph: Choose Your Model, Not Your Loss Function
Oliver E Richardson