[2603.20929] Stability of Sequential and Parallel Coordinate Ascent Variational Inference

Statistics > Machine Learning arXiv:2603.20929 (stat) [Submitted on 21 Mar 2026] Title:Stability of Sequential and Parallel Coordinate Ascent Variational Inference Authors:Debdeep Pati View a PDF of the paper titled Stability of Sequential and Parallel Coordinate Ascent Variational Inference, by Debdeep Pati View PDF HTML (experimental) Abstract:We highlight a striking difference in behavior between two widely used variants of coordinate ascent variational inference: the sequential and parallel algorithms. While such differences were known in the numerical analysis literature in simpler settings, they remain largely unexplored in the optimization-focused literature on variational inference in more complex models. Focusing on the moderately high-dimensional linear regression problem, we show that the sequential algorithm, although typically slower, enjoys convergence guarantees under more relaxed conditions than the parallel variant, which is often employed to facilitate block-wise updates and improve computational efficiency. Comments: Subjects: Machine Learning (stat.ML); Machine Learning (cs.LG); Statistics Theory (math.ST); Computation (stat.CO) Cite as: arXiv:2603.20929 [stat.ML]   (or arXiv:2603.20929v1 [stat.ML] for this version)   https://doi.org/10.48550/arXiv.2603.20929 Focus to learn more arXiv-issued DOI via DataCite (pending registration) Submission history From: Debdeep Pati [view email] [v1] Sat, 21 Mar 2026 20:09:16 UTC (38 KB) Full-text links: Access Paper:...