Abstract: Systems and methods determine optimized hyperparameter values for one or more machine-learning models. A sample training data set from a larger corpus of training data is obtained. Initial hyperparameter values are then randomly selected. Using the sample training data set and the randomly chosen hyperparameter values, an initial set of performance metric values are obtained. Maximized hyperparameter values are then determined from the initial set of hyperparameter values based on the corresponding performance metric value. A larger corpus of training data is then evaluated using the maximized hyperparameter values and the corresponding machine-learning model, which yields another corresponding set of performance metric values. The maximized hyperparameter values and their corresponding set of performance metric values are then merged with the prior set of hyperparameter values.

Abstract: Embodiments of the disclosed technologies provide tree-based transfer learning of hyperparameters of a machine learning model or tunable parameters of a black box system. A similar reference task tree is selected from a set of reference task trees. Data is transferred from the similar reference task tree to a target task tree.

Abstract: Techniques for tuning model parameters to optimize online content are disclosed herein. In some embodiments, a computer system receives logged data for cohorts of users, where the logged data of each one of the plurality of cohorts comprises a number of impressions of online content to the cohort, parameter values applied to objective functions of a model used in selecting the online content for the impressions, contribution actions by the cohort directed towards the online content, and clicks by the cohort directed towards the online content. The computer system, for each cohort, selects one of the parameter values for each objective function based on the logged data. The computer system then selects at least one content item for display to a target user based on the model using the parameter values corresponding to the cohort of the target user.

Abstract: Techniques for automatically identifying a primary objective for a multi-objective optimization problem are provided. In one technique, an experiment is conduct and results of the experiment involving different values of a model parameter are tracked and stored. Multiple metrics are generated based on the results. For each metric, a maximum or minimum value of the metric given a particular value of the model parameter is determined and a variance associated with the metric is determined based on the maximum or minimum value. A metric that is associated with the lowest variance among the multiple metrics is identified. The identified metric is used as a primary metric in a multi-objective optimization problem.

Abstract: Systems and methods determine optimized hyperparameter values for one or more machine-learning models. A sample training data set from a larger corpus of training data is obtained. Initial hyperparameter values are then randomly selected. Using the sample training data set and the randomly chosen hyperparameter values, an initial set of performance metric values are obtained. Maximized hyperparameter values are then determined from the initial set of hyperparameter values based on the corresponding performance metric value. A larger corpus of training data is then evaluated using the maximized hyperparameter values and the corresponding machine-learning model, which yields another corresponding set of performance metric values. The maximized hyperparameter values and their corresponding set of performance metric values are then merged with the prior set of hyperparameter values.

Abstract: Techniques for improving the accuracy, scalability, and efficiency of machine-learning models for selecting digital content items for display within a graphical user interface are disclosed herein.