Cost-aware Bayesian Optimization
This addresses the issue of optimizing expensive functions under variable costs, such as in neural network hyperparameter tuning, but is incremental as it builds on existing BO methods.
The paper tackled the problem of Bayesian optimization (BO) where evaluation costs vary across the search space, by introducing Cost Apportioned BO (CArBO) to minimize objective functions with minimal cost. It showed that CArBO finds significantly better hyperparameter configurations than competing methods on 20 black-box function optimization problems given the same cost budget.
Bayesian optimization (BO) is a class of global optimization algorithms, suitable for minimizing an expensive objective function in as few function evaluations as possible. While BO budgets are typically given in iterations, this implicitly measures convergence in terms of iteration count and assumes each evaluation has identical cost. In practice, evaluation costs may vary in different regions of the search space. For example, the cost of neural network training increases quadratically with layer size, which is a typical hyperparameter. Cost-aware BO measures convergence with alternative cost metrics such as time, energy, or money, for which vanilla BO methods are unsuited. We introduce Cost Apportioned BO (CArBO), which attempts to minimize an objective function in as little cost as possible. CArBO combines a cost-effective initial design with a cost-cooled optimization phase which depreciates a learned cost model as iterations proceed. On a set of 20 black-box function optimization problems we show that, given the same cost budget, CArBO finds significantly better hyperparameter configurations than competing methods.