Sas Institute Inc.

Steven Joseph Gardner

Joshua David Griffin

Yan Xu

Patrick Nathan Koch

Brett Alan Wujek

Yan Gao

Oleg Borisovich Golovidov

David Bruce Elsheimer

Varunraj Valsaraj

Scott Russell Pope

Pelin Cay

Natalia Summerville

Nabaruna Karmakar

Connie Stout Dunbar

Antony Nicholas Cooper

Gregory Scott Dunbar

A computing device selects new test configurations for testing software. (A) First test configurations are generated using a random seed value. (B) Software under test is executed with the first test configurations to generate a test result for each. (C) Second test configurations are generated from the first test configurations and the test results generated for each. (D) The software under test is executed with the second test configurations to generate the test result for each. (E) When a restart is triggered based on a distance metric value computed between the second test configurations, a next random seed value is selected as the random seed value and (A) through (E) are repeated. (F) When the restart is not triggered, (C) through (F) are repeated until a stop criterion is satisfied. (G) When the stop criterion is satisfied, the test result is output for each test configuration.

Automated machine learning test system

Tuned hyperparameter values are determined for training a machine learning model. When a selected hyperparameter configuration does not satisfy a linear constraint, if a projection of the selected hyperparameter configuration is included in a first cache that stores previously computed projections is determined. When the projection is included in the first cache, the projection is extracted from the first cache using the selected hyperparameter configuration, and the selected hyperparameter configuration is replaced with the extracted projection in the plurality of hyperparameter configurations. When the projection is not included in the first cache, a projection computation for the selected hyperparameter configuration is assigned to a session. A computed projection is received from the session for the selected hyperparameter configuration. The computed projection and the selected hyperparameter configuration are stored to the first cache, and the selected hyperparameter configuration is replaced with the computed projection.

Multi-objective distributed hyperparameter tuning system

Manufacturing processes can be optimized using machine learning models. For example, a system can execute an optimization model to identify a recommended set of values for configurable settings of a manufacturing process associated with an object. The optimization model can determine the recommended set of values by implementing an iterative process using an objective function. Each iteration of the iterative process can include selecting a current set of candidate values for the configurable settings from within a current region of a search space defined by the optimization model; providing the current set of candidate values as input to a trained machine learning model that can predict a value for a target characteristic of the object or the manufacturing process based on the current set of candidate values; and identifying a next region of the search space to use in a next iteration of the iterative process based on the value.

Optimizing manufacturing processes using one or more machine learning models

A computing device selects decision variable values. A lower boundary value and an upper boundary value is defined for a decision variable. (A) A plurality of decision variable configurations is determined using a search method. The value for the decision variable is between the lower boundary value and the upper boundary value. (B) A decision variable configuration is selected. (C) A model of the model type is trained using the decision variable configuration. (D) The model is scored to compute an objective function value. (E) The computed objective function value and the selected decision variable configuration are stored. (F) (B) through (E) is repeated for a plurality of decision variable configurations. (G) The lower boundary value and the upper boundary value are updated using the objective function value and the decision variable configuration stored. Repeat (A)-(F) with the lower boundary value and the upper boundary value updated in (G).

Distributed decision variable tuning system for machine learning

A computing device automatically selects hyperparameter values based on objective criteria to train a predictive model. Each session of a plurality of sessions executes training and scoring of a model type using an input dataset in parallel with other sessions of the plurality of sessions. Unique hyperparameter configurations are determined using a search method and assigned to each session. For each session of the plurality of sessions, training of a model of the model type is requested using a training dataset and the assigned hyperparameter configuration, scoring of the trained model using a validation dataset and the assigned hyperparameter configuration is requested to compute an objective function value, and the received objective function value and the assigned hyperparameter configuration are stored. A best hyperparameter configuration is identified based on an extreme value of the stored objective function values.

Distributed hyperparameter tuning system for machine learning

Growing community of inventors

Cary, NC, United States of America

Steven Joseph Gardner