Variational Inference
Variational inference (VI) is a powerful family of approximate Bayesian inference methods aiming to efficiently estimate complex probability distributions, often encountered in machine learning and scientific modeling. Current research focuses on improving VI's scalability and accuracy through novel algorithms like stochastic variance reduction, amortized inference, and the use of advanced model architectures such as Gaussian processes, Bayesian neural networks, and mixture models, often within the context of specific applications like anomaly detection and inverse problems. The resulting advancements in VI are significantly impacting various fields, enabling more robust uncertainty quantification, improved model interpretability, and efficient solutions to previously intractable problems in areas ranging from 3D scene modeling to causal 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