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Does your learning algorithm assume direct access to data? In practice:

Learning Updatable Classifiers from Remote Data. Does your learning algorithm assume direct access to data? In practice: Data is too large to ship to a centralized location In certain domains, such as personalized medicine, privacy regulations may explicitly prohibit direct access to data.

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Does your learning algorithm assume direct access to data? In practice:

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  1. Learning Updatable Classifiers from Remote Data • Does your learning algorithm assume direct access to data? • In practice: • Data is too large to ship to a centralized location • In certain domains, such as personalized medicine,privacy regulations may explicitly prohibit direct access to data. • Even in cases where data can be shipped to a centralized location, the local copy of the dataset may quickly become out of date due to frequent updates to the data. • Needed: Approaches for learning from data without direct access,in settings where the data can be accessed only through statistical queries

  2. Problem Formulation

  3. General Framework • Query Formulation:poses the relevant statistical queries • Hypothesis Generation: uses the resulting statistics to update or refine a partial model (and if necessary, further invoke the statistical query component)

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