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cs.LG2023
Computing Marginal and Conditional Divergences between Decomposable Models with Applications
Loong Kuan Lee, Geoffrey I. Webb, Daniel F. Schmidt +1
The ability to compute the exact divergence between two high-dimensional distributions is useful in many applications but doing so naively is intractable. Computing the alpha-beta…
cs.LG2023★ 1 cited
Little is Enough: Boosting Privacy by Sharing Only Hard Labels in Federated Semi-Supervised Learning
Amr Abourayya, Jens Kleesiek, Kanishka Rao +4
In many critical applications, sensitive data is inherently distributed and cannot be centralized due to privacy concerns. A wide range of federated learning approaches have been p…