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Remote Data Learning for Updatable Classifiers

Learn to develop classifiers without direct data access using statistical queries in restricted access settings. Adapt to dynamic data environments and comply with privacy regulations.

rafael-vance
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Remote Data Learning for Updatable Classifiers

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Presentation Transcript

  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 • Even in cases where data can be shipped to a centralized location, the local copy of the dataset may quickly become out of date due to frequent updates to the data.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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