Common feature protocol for collaborative machine learning
First Claim
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1. A method, comprising:
- obtaining a hierarchical representation comprising a set of namespaces of a set of features shared by a set of statistical models; and
calculating, by one or more computer systems, a derived feature from the set of features by;
using the hierarchical representation to obtain, from one or more execution environments among a set of execution environments, a subset of the set of features for use in calculating the derived feature; and
applying a formula from the hierarchical representation to the subset of the set of features to produce the derived feature; and
providing the derived feature for use by one or more of the statistical models in the set of execution environments, thereby promoting sharing and reusing common features by the set of execution environments during collaborative machine learning.
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Abstract
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.
31 Citations
20 Claims
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1. A method, comprising:
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obtaining a hierarchical representation comprising a set of namespaces of a set of features shared by a set of statistical models; and calculating, by one or more computer systems, a derived feature from the set of features by; using the hierarchical representation to obtain, from one or more execution environments among a set of execution environments, a subset of the set of features for use in calculating the derived feature; and applying a formula from the hierarchical representation to the subset of the set of features to produce the derived feature; and providing the derived feature for use by one or more of the statistical models in the set of execution environments, thereby promoting sharing and reusing common features by the set of execution environments during collaborative machine learning. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8, 9, 10)
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11. An apparatus, comprising:
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one or more processors; and memory storing instructions that, when executed by the one or more processors, cause the apparatus to; obtain a hierarchical representation comprising a set of namespaces of a set of features shared by a set of statistical models; use the hierarchical representation to obtain, from one or more execution environments among a set of execution environments, a subset of the set of features for use in calculating a derived feature; apply a formula from the hierarchical representation to the subset of the set of features to produce the derived feature; and provide the derived feature for use by one or more of the statistical models in the set of execution environments, thereby promoting sharing and reusing common features by the set of execution environments during collaborative machine learning. - View Dependent Claims (12, 13, 14, 15, 16, 17, 18)
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19. A system, comprising:
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a namespace manager comprising a non-transitory computer-readable medium comprising instructions that, when executed, cause the system to provide a hierarchical representation comprising a set of namespaces of a set of features shared by a set of statistical models; and an interpreter comprising a non-transitory computer-readable medium comprising instructions that, when executed, cause the system to; use the hierarchical representation to obtain, from one or more execution environments among a set of execution environments, a subset of the set of features for use in calculating a derived feature; apply a formula from the hierarchical representation to the subset of the set of features to produce the derived feature; and provide the derived feature for use by one or more of the statistical models in the set of execution environments, thereby promoting sharing and reusing common features by the set of execution environments during collaborative machine learning. - View Dependent Claims (20)
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Specification