Optimal regret bounds O(δ^{-1/2}√T) for convex and O(δ^{-1} log T) for strongly convex losses are achieved in distributed online convex optimization under compressed communication.
Federated learning of out-of-vocabulary words.arXiv preprint arXiv:1903.10635
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A new catalog classifying 35 data error types into missing, incorrect, and redundant categories for tabular data, with definitions and examples to improve data quality management.
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Distributed Online Convex Optimization with Compressed Communication: Optimal Regret and Applications
Optimal regret bounds O(δ^{-1/2}√T) for convex and O(δ^{-1} log T) for strongly convex losses are achieved in distributed online convex optimization under compressed communication.
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A Catalog of Data Errors
A new catalog classifying 35 data error types into missing, incorrect, and redundant categories for tabular data, with definitions and examples to improve data quality management.