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|Title:||Scaling High-Dimensional Heuristic Search Techniques|
|Abstract:||The optimization problem of hyperparameter search is an important problem in tuning machine learning models. Crucially, even heuristic search techniques, like Bayesian optimization, fail in parameter spaces of large dimensionality. We discuss improvements to the complexity of Bayesian optimization and strategies to accelerate high-dimensional search. Tested on one particular high-dimensional function, our method brings the search 2.05% closer to the true optimum per extra second of computation time devoted to learning from previous observations of the black-box objective.|
|Type of Material:||Princeton University Senior Theses|
|Appears in Collections:||Computer Science, 1988-2016|
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