Using machine learning to improve stochastic optimization
David H. Wolpert, Dev Rajnarayan · National Conference on Artificial Intelligence · 2013
In many stochastic optimization algorithms there is a hyperparameter that controls how the next sampling distribution is determined from the current data set of samples of the objective function. This hyperparameter controls the exploration /exploitation trade-off of the next sample. Typically heuristic rules of thumb are used to set that hyperparameter, e.g., a pre-fixed annealing schedule. We show how machine learning provides more principled alternatives to (adaptively) set that hyperparameter, and demonstrate that these alternatives can substantially improve optimization performance.