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
Self-Attention through Kernel-Eigen Pair Sparse Variational Gaussian Processes
Yingyi Chen, Qinghua Tao, Francesco Tonin, Johan A. K. Suykens
A Differentiable Partially Observable Generalized Linear Model with Forward-Backward Message Passing
Chengrui Li, Weihan Li, Yule Wang, Anqi Wu
Bayesian Deep Learning for Remaining Useful Life Estimation via Stein Variational Gradient Descent
Luca Della Libera, Jacopo Andreoli, Davide Dalle Pezze, Mirco Ravanelli, Gian Antonio Susto