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Workshop: OPT 2023: Optimization for Machine Learning

DIRECT Optimisation with Bayesian Insights: Assessing Reliability Under Fixed Computational Budgets

Fu Wang · Zeyu Fu · Xiaowei Huang · Wenjie Ruan


We introduce a method for probabilistically evaluating the reliability of direct optimisation under a constrained computational budget, a context frequently encountered in various applications. By interpreting the slope data gathered during the optimisation process as samples from the objective function's derivative, we utilise Bayesian posterior prediction to derive a confidence score for the optimisation outcomes. We validated our approach using numerical experiments on four multi-dimensional test functions, and the results highlight the practicality and efficacy of our approach.

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