Microsoft Technology Licensing, LLC

Josh Fleming

Bo Long

Bee-Chung Chen

Kinjal Basu

Yiming Ma

Chengming Jiang

Yunbo Ouyang

Alex Shelkovnykov

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. The foregoing operations are performed iteratively until it is determined that the hyperparameter values are converging to a particular value.

Large-scale automated hyperparameter tuning

In an example embodiment, a warm-start training solution is used to dramatically reduce the computational resources needed to train when retraining a generalized additive mixed-effect (GAME) model. The problem of retraining time is particularly applicable to GAME models, since these models take much longer to train as the data grows. In the past, the strategy to reduce computational resources during retraining was to use less training data, but this affects the model quality, especially for GAME models, which rely on fine-grained sub-models at, for example, member or item levels. The present solution addresses the computational resources issues without sacrificing GAME model accuracy.

Warm start generalized additive mixed-effect (game) framework

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Atlanta, GA, United States of America

Josh Fleming