arXiv:2507.12453v5 Announce Type: replace Abstract: In automated machine learning, scientific discovery, and other applications of Bayesian optimization, deciding when to stop evaluating expensive black-box functions in a cost-aware manner is an important but underexplored practical consideration. A natural performance metric for this purpose is the cost-adjusted simple regret, which explicitly captures the trade-off between solution quality and cumulative evaluation cost. Existing stopping rules for Bayesian optimization are either heuristic, or are theoretically grounded but designed to opti
The increasing cost and complexity of AI model training, along with the drive for more efficient automated machine learning systems, is highlighting the need for smarter resource allocation.
Optimizing the stopping criteria for Bayesian optimization directly impacts the efficiency and cost-effectiveness of developing and deploying advanced AI systems, especially in resource-constrained environments.
This research provides a more theoretically grounded and cost-aware framework for deciding when to cease expensive black-box function evaluations, moving beyond heuristic approaches.
- · AI developers
- · Machine learning researchers
- · Cloud computing providers (through more efficient use)
- · Companies adopting AutoML
- · Inefficient AI development pipelines
- · Organizations with high compute waste
More efficient and less costly AI model development and hyperparameter tuning.
Accelerated discovery cycles in scientific research and automated machine learning by reducing experimentation costs.
Potentially democratizes access to advanced AI development by lowering the economic barriers associated with extensive computational resources.
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Read at arXiv cs.LG