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- W36822746 abstract "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." @default.
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- W36822746 date "2013-01-01" @default.
- W36822746 modified "2023-09-24" @default.
- W36822746 title "Using machine learning to improve stochastic optimization" @default.
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