Even More Guarantees for Variational Inference in the Presence of Symmetries
Researchers have expanded theoretical guarantees for variational inference, a method used to approximate complex probability distributions. The new work broadens the applicability of these guarantees to a wider range of divergence measures and removes the prior assumption of a log-concave target distribution. This theoretical advancement allows for more robust approximation of target means and correlation matrices, even for multi-modal distributions, and provides practical guidelines for choosing variational families and parameters in experiments. AI
IMPACT Provides theoretical underpinnings for more robust model approximation in machine learning.