Abstract: Optimization process for automated machine learning with a combination of different optimizers. In an embodiment, optimization is performed by, for each of a plurality of machine-learning algorithms, executing a Bayesian optimization algorithm to produce a plurality of trialed models, wherein each of the plurality of trialed models is associated with the machine-learning algorithm and a set of hyperparameters. A subset of best-performing machine-learning algorithms is selected, and, for each machine-learning algorithm in the subset, a best-performing model from the plurality of trialed models associated with that machine-learning algorithm is selected, and a local search algorithm is executed starting from the set of hyperparameters associated with the selected best-performing model to identify an improved model that has better performance than the selected best-performing model.

Abstract: Time-based and accuracy-estimate-based trial selection for selection of subsequent machine-learning models in automated machine learning. In an embodiment, a batch of trials are generated for a plurality of machine-learning algorithms. During execution of the trials, a training time is estimated for at least a portion of the models represented in the batch of trials, and a subset of models are selected for evaluation based, at least in part, on their estimated training times. A graphical user interface is updated to reflect the evaluation results of the subset of models, even before the evaluation results for other models become available.

Abstract: An efficient computer-implemented technique is described herein for allowing a searcher to access result items by taking into account the perspective of a beneficiary entity. In some cases, the beneficiary entity may correspond to another person besides the searcher who submits a query, on whose behalf the searcher is performing the search. The technique operates by leveraging the activity of a group of relevant users who have a similar demographic profile to the beneficiary entity, with respect to a topic of the query under consideration.