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Found 2 papers, 1 papers with code
Private PAC Learning May be Harder than Online Learning
We continue the study of the computational complexity of differentially private PAC learning and how it is situated within the foundations of machine learning.
From Soft Classifiers to Hard Decisions: How fair can we be?
We study the feasibility of achieving various fairness properties by post-processing calibrated scores, and then show that deferring post-processors allow for more fairness conditions to hold on the final decision.
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