Memoization-Aware Bayesian Optimization for AI Pipelines with Unknown Costs

Date of Award


Document Type


Degree Name

Master of Science in Machine Learning


Machine Learning

First Advisor

Dr. Qirong Ho

Second Advisor

Dr. Mohsen Guizani


Bayesian optimization (BO) is an effective approach for optimizing black-box functions via potentially noisy function evaluations. However, few BO techniques address the costaware setting, in which training costs are sensible to parameter values, particularly when costs are initially unknown. This thesis explores cost awareness in tuning multi-stage AI pipelines (such as language model training/tuning), and especially explores caching techniques to store and reuse early-stage outputs in favor of optimizing later stages, without incurring the costs of re-running the full pipeline. To take advantage of caching, we propose the Expected-Expected Improvement Per Unit (EEIPU) acquisition function that adapts to black-box costs in the multi-stage pipeline setting. EEIPU incorporates earlystage memoization, allowing it to optimize later stages of the pipeline while only incurring a fraction of the pipeline cost. We ran EEIPU against state-of-the-art cost-aware and multi-stage BO methods, on a series of synthetic and real experiments. Our method reports strong results, generating an average of 148% more BO iterations within the same optimization budget than comparable methods on synthetic experiments, and 103% more BO iterations when tested on real pipelines. After warm-up iterations, EEIPU improves on the objective value by 58% and 108% over the average baseline in synthetic and real experiments, respectively.


Thesis submitted to the Deanship of Graduate and Postdoctoral Studies

In partial fulfilment of the requirements for the M.Sc degree in Machine Learning

Advisors:Qirong HO,Mohsen Guizani

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