1. A system for optimizing performance of a machine learning system, the system comprising: at least one computer hardware processor; and at least one non-transitory computer-readable storage medium storing processor-executable instructions that, when executed by the at least one computer hardware processor, cause the at least one computer hardware processor to perform: identifying at least a first set of hyper-parameter values at which to evaluate an objective function that relates hyper-parameter values of the machine learning system to values providing a measure of performance of the machine learning system, the identifying performed at least in part by using an integrated acquisition utility function and a probabilistic model of the objective function, wherein the integrated acquisition utility function is obtained at least in part by integrating an initial acquisition utility function with respect to at least one parameter of the probabilistic model; evaluating the objective function at least at the identified first set of hyper-parameter values, at least in part by executing the machine learning system when configured with the first set of hyper-parameter values, to obtain a corresponding first value providing a measure of performance of the machine learning system when operated using the first set of hyper-parameter values; and updating the probabilistic model of the objective function using results of the evaluating to obtain an updated probabilistic model of the objective function.