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Bayesian Optimization with Unknown Search Space
Huong Ha · Santu Rana · Sunil Gupta · Thanh Nguyen-Tang · Hung The Tran · Svetha Venkatesh

Thu Dec 12 05:00 PM -- 07:00 PM (PST) @ East Exhibition Hall B + C #209

Applying Bayesian optimization in problems wherein the search space is unknown is challenging. To address this problem, we propose a systematic volume expansion strategy for the Bayesian optimization. We devise a strategy to guarantee that in iterative expansions of the search space, our method can find a point whose function value within epsilon of the objective function maximum. Without the need to specify any parameters, our algorithm automatically triggers a minimal expansion required iteratively. We derive analytic expressions for when to trigger the expansion and by how much to expand. We also provide theoretical analysis to show that our method achieves epsilon-accuracy after a finite number of iterations. We demonstrate our method on both benchmark test functions and machine learning hyper-parameter tuning tasks and demonstrate that our method outperforms baselines.

Author Information

Huong Ha (Deakin University)
Santu Rana (Deakin University)
Sunil Gupta (Deakin University)
Thanh Nguyen-Tang (Deakin University)
Hung The Tran (Deakin University)
Svetha Venkatesh (Deakin University)

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