Large-Scale Empirical Study Compares 56 Optimization Algorithms for Black-Box Variational Inference
Researchers conducted a massive empirical evaluation of 56 stochastic optimization algorithms across 1,092 Bayesian inference problems to identify the best tuning-free methods for black-box variational inference. Black-box variational inference aims to automate posterior approximation but traditionally requires extensive problem-specific tuning of optimizers. The study found that no single algorithm dominates, but a selection of 5 algorithms reliably achieves near-optimal performance, providing a practical baseline for applications without expert tuning.
This arXiv preprint presents results from a comprehensive empirical study involving over 550,000 individual optimization runs and 15 core-years of computation. The researchers evaluated 56 stochastic gradient-based optimization algorithms on 1,092 Bayesian inference optimization problems spanning a wide range of difficulty, with posterior target dimensions from 1 to 10^4 and condition numbers from 1 to 10^8. The motivation stems from the tension between black-box variational inference's promise of automation and the practical reality that stochastic optimizers typically require extensive tuning. By systematically comparing recent adaptive optimization methods developed over the past decade, the study establishes the current state of the art in tuning-free optimization-based inference. The key finding—that a portfolio of 5 algorithms suffices for reliable near-optimal performance—offers both a practical baseline for practitioners and a benchmark for future algorithm development.
What's missing
The study's own limitations and open questions are not detailed in the abstract provided, such as computational constraints on the benchmark problems, whether results generalize to real-world applications beyond the tested problem classes, or specific guidance on which 5 algorithms constitute the recommended portfolio.
What different sources said
- arXiv cs.LGCenter
Large-scale empirical tuning and comparison of default optimizers for variational inference
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