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Found 3 papers, 1 papers with code
Enhancing Watermarked Language Models to Identify Users
A major challenge for black-box reductions is the lack of a unified abstraction for robustness -- that marked text is detectable after edits.
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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