arXiv Machine Learning November 2, 2026 By Lin Fu, Siwei Fu, Dong Ni

CAP: Learning to Optimize from Sparse Observations of Related Tasks

Read the original on arXiv Machine Learning →

The paper introduces CAP (Candidate Acquisition Policy), a method that learns a query policy from sparse historical records of related optimization tasks. CAP models shared structure and task-specific variation to generate interactive practice functions, then trains a feedback-conditioned policy that operates under a fixed evaluation budget. Experiments on synthetic task families and engineering simulation benchmarks (BOPTEST and SU2) show CAP achieving the lowest median final-budget values compared to other methods.

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