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  • Mar 2020
  • Conference Presentation

A New Analysis of Differential Privacy's Generalization Guarantees
By: Christopher Jung, Katrina Ligett, Seth Neel, Aaron Roth, Saeed Sharifi-Malvajerdi and Moshe Shenfeld
We give a new proof of the "transfer theorem" underlying adaptive data analysis: that any mechanism for answering adaptively chosen statistical queries that is differentially private and sample-accurate is also accurate out-of-sample. Our new proof is elementary and...  View Details
Keywords: Machine Learning; Transfer Theorem; Mathematical Methods
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  • Article

Strategy-Proofness of Worker-Optimal Matching with Continuously Transferable Utility
By: Ravi Jagadeesan, Scott Duke Kominers and Ross Rheingans-Yoo
We give a direct proof of one-sided strategy-proofness for worker-firm matching under continuously transferable utility. A new “Lone Wolf” theorem (Jagadeesan et al., 2017) for settings with transferable utility allows us to adapt the method of proving one-sided...  View Details
Keywords: Matching; Strategy-proofness; Lone Wolf Theorem; Rural Hospitals Theorem; Mechanism Design; Marketplace Matching
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