New Paper Shows When Momentum Acceleration Works—and Fails—in Gaussian Variational Inference
ErnestRyu · x · 2026-10-19
A new stat.ML paper by Jaesung R. Park, Uijeong Jang, and Yifan Chen examines whether classical momentum-based acceleration extends from convex optimization to Gaussian variational inference (GVI).
- With exact Gaussian expectations, a convex composite formulation of GVI achieves the classical √κ accelerated convergence rate for strongly log-concave, log-smooth targets.
- However, when only pointwise derivatives are available, the authors construct a family of strongly log-concave targets where any randomized algorithm requires κ^(1-o(1)) queries for fixed accuracy—even with an initial gap independent of κ.
- Conclusion: curvature bounds alone cannot guarantee uniform accelerated total-query complexity; the paper also identifies additional structure that permits acceleration.
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