Microsoft Technology Licensing, LLC

Songxiang Gu

Joel Daniel Young

Chang-Ming Tsai

Eric Huang

Fei Chen

Sumit Rangwala

Xu Miao

David J Stein

Da Teng

Xuebin Yan

Andris Birkmanis

Lance M Wall

The disclosed embodiments provide a system for processing data. During operation, the system obtains a function call for a function that calculates an attribute associated with a machine learning model. For each argument of the function call, the system identifies a parameter type of the argument, wherein the parameter type represents a type of data used with the machine learning model. The system also obtains a value accessor for retrieving features specific to the parameter type and obtains a value represented by the argument using the value accessor. The system then calculates the attribute by applying the function to the value and uses the attribute to execute the machine learning model.

Unified parameter and feature access in machine learning models

The disclosed embodiments provide a system for processing data. During operation, the system obtains a hierarchical representation containing a set of namespaces of a set of features shared by a set of statistical models. Next, the system uses the hierarchical representation to obtain, from one or more execution environments, a subset of the features for use in calculating the derived feature. The system then applies a formula from the hierarchical representation to the subset of the features to produce the derived feature. Finally, the system provides the derived feature for use by one or more of the statistical models.

Common feature protocol for collaborative machine learning

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Sunnyvale, CA, United States of America

Songxiang Gu