Abstract: A black box evaluator is accessed and a surrogate machine learning model that provides estimates for the optimization of categorical values for the black box evaluator is generated, the surrogate machine learning model being based upon observations from previous executions of the black box evaluator. The black box evaluator is optimized by selecting, by an acquisition function executing on a computing device, a new candidate point for the categorical values. The black box evaluator is executed with the new candidate point for the categorical values.

Abstract: Aspects of the present invention disclose a method, computer program product, and system for optimizing a result for a combinatorial optimization problem. The method includes one or more processors receiving a black-box model. The method further includes one or more processors learning a multilinear polynomial surrogate model employing an exponential weight update rule. The method further includes one or more processors optimizing the learnt multilinear polynomial surrogate model. The method further includes one or more processors applying the black-box model to the optimized solution found by the multilinear polynomial surrogate model. In an additional aspect, the method of learning an optimized multilinear polynomial surrogate model employing an exponential weight update rule further includes one or more processors calculating utilizing data from the black-box model, an update of the coefficients of the multilinear polynomial surrogate model.

Abstract: Certain embodiments involve determining and outputting correlations between metrics in large-scale web analytics datasets. For example, a processor identifies pairs of data metrics in a web analytics data set and determines a Maximal Information Coefficient (MIC) score for each pair of data metrics that indicates a strength of a correlation between the pair of data metrics. The processor generates an interactive user interface that graphically displays each pair of correlated data metrics having an MIC score above a threshold and the interactive user interface indicates the strength of the correlation between each displayed pair of correlated data metrics. The processor receives user input indicating an adjustment to the threshold and modifies the interactive user interface in response to receiving the user input by adding pairs of correlated data metrics to, or removing pairs of correlated metrics from, the interactive user interface based on the adjustment to the threshold.