Learning to Assess the Reliability of Number-of-Runs Estimation in Stochastic Optimization

ArXi:2605.28309v1 Announce Type: new In large-scale benchmarking of stochastic optimization algorithms, the key challenge is no longer whether repeated runs are needed for reliability, but how to determine when sufficient evidence has been collected without incurring unnecessary computational cost. We study a learning-based extension of a recent empirical online heuristic that adaptively estimates the required number of runs using outlier handling and skewness-based symmetry checks.