Scalable Variational

Scalable variational inference aims to efficiently perform Bayesian inference in high-dimensional and large-dataset settings, where traditional methods become computationally intractable. Current research focuses on developing novel variational families and inference algorithms, including those leveraging structured approximations, amortized inference, and plate amortization, to improve scalability and expressiveness while maintaining accuracy. These advancements are crucial for tackling complex problems in diverse fields such as deep generative modeling, causal discovery, and Bayesian neural networks, enabling more robust and efficient statistical analysis of large-scale data. The resulting improvements in computational efficiency and model flexibility are driving significant progress in various scientific domains and practical applications.