Large-scale empirical tuning and comparison of default optimizers for variational inference

2026-06-05 · Source: Machine Learning · Field: Technology & Digital — Artificial Intelligence & Machine Learning · Depth: Expert, quick

Summary

A large-scale empirical evaluation investigated the performance of 56 stochastic gradient-based optimization algorithms for black-box variational inference (BBVI), a posterior approximation method often hindered by extensive problem-specific tuning. This study, involving over 550,000 optimization runs and 15 core-years of compute, applied these algorithms to 1092 Bayesian inference problems. These problems varied significantly in difficulty, covering posterior target dimensions from 1 to 10^4, condition numbers from 1 to 10^8, and diverse variational families. The findings indicate that no single optimization method consistently outperforms others across all scenarios. However, the research established that utilizing a selection of 5 specific algorithms is sufficient to reliably achieve performance close to the observed optimum. This provides a robust baseline for applications where expert tuning is impractical and for benchmarking novel stochastic optimization algorithms.

Key takeaway

For Machine Learning Engineers implementing black-box variational inference (BBVI) or developing new stochastic optimizers, you should recognize that no single algorithm is universally superior. Instead of extensive problem-specific tuning, adopt the empirically validated selection of 5 diverse optimization algorithms. This approach reliably achieves near best-possible performance, significantly reducing development overhead and providing a strong baseline for comparing your novel methods.

Key insights

A selection of 5 adaptive stochastic optimizers reliably approximates optimal performance for black-box variational inference without extensive tuning.

Principles

• Black-box variational inference often requires extensive tuning.
• Adaptive optimizers can minimize problem-specific tuning.
• No single optimizer universally dominates BBVI tasks.

Method

Empirically evaluate 56 stochastic optimizers across 1092 Bayesian inference problems, then identify a minimal set of 5 algorithms that collectively achieve near-optimal performance without extensive tuning.

In practice

• Employ a 5-algorithm selection for robust BBVI.
• Benchmark new optimizers against the established baseline.

Topics

• Variational Inference
• Stochastic Optimization
• Bayesian Inference
• Algorithm Tuning
• Empirical Evaluation

Best for: Research Scientist, AI Scientist, Machine Learning Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by Machine Learning.