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
Variational Inference for Additive Main and Multiplicative Interaction Effects Models
AntÔnia A. L. Dos Santos, Rafael A. Moral, Danilo A. Sarti, Andrew C. Parnell
Hidden Parameter Recurrent State Space Models For Changing Dynamics Scenarios
Vaisakh Shaj, Dieter Buchler, Rohit Sonker, Philipp Becker, Gerhard Neumann
Doubly Reparameterized Importance Weighted Structure Learning for Scene Graph Generation
Daqi Liu, Miroslaw Bober, Josef Kittler
Cold Posteriors through PAC-Bayes
Konstantinos Pitas, Julyan Arbel
Variational Causal Dynamics: Discovering Modular World Models from Interventions
Anson Lei, Bernhard Schölkopf, Ingmar Posner