Cheap Thrills: Effective Amortized Optimization Using Inexpensive Labels

2026-03-05 · Source: Machine Learning · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Optimization · Depth: Advanced, quick

Summary

A novel three-stage machine learning framework is proposed to efficiently solve optimization and simulation problems by leveraging inexpensive, imperfect labels. This approach addresses limitations of prior supervised and self-supervised methods, which often require costly high-quality labels or navigate complex optimization landscapes. The framework involves initial collection of "cheap" imperfect labels, followed by supervised pretraining, and then self-supervised refinement to enhance overall performance. Theoretical analysis indicates that labeled data only needs to position the model within a basin of attraction, requiring modest numbers of inexact labels and training epochs. Empirical validation across nonconvex constrained optimization, power-grid operation, and stiff dynamical systems demonstrates faster convergence, improved accuracy, feasibility, and optimality, alongside up to 59x reductions in total offline cost.

Key takeaway

For AI Researchers developing solutions for complex optimization and simulation, consider adopting this three-stage framework. Your team can significantly reduce offline training costs by up to 59x while improving accuracy and convergence, by strategically using inexpensive, imperfect labels for initial model guidance before self-supervised refinement.

Key insights

Inexpensive, imperfect labels can effectively pretrain models for complex optimization, followed by self-supervised refinement.

Principles

• Labels need only guide models into an attraction basin.
• Modest inexact labels suffice for effective pretraining.

Method

Collect cheap, imperfect labels; perform supervised pretraining; then refine the model using self-supervised learning.

In practice

• Apply to nonconvex constrained optimization.
• Optimize power-grid operations.
• Solve stiff dynamical systems.

Topics

• Amortized Optimization
• Machine Learning Surrogates
• Self-supervised Learning
• Supervised Pretraining
• Cost Reduction

Best for: AI Researcher, AI Scientist, Research Scientist

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