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A Necessary and Sufficient Stability Notion for Adaptive Generalization
Moshe Shenfeld · Katrina Ligett

Wed Dec 11 10:45 AM -- 12:45 PM (PST) @ East Exhibition Hall B + C #2

We introduce a new notion of the stability of computations, which holds under post-processing and adaptive composition. We show that the notion is both necessary and sufficient to ensure generalization in the face of adaptivity, for any computations that respond to bounded-sensitivity linear queries while providing accuracy with respect to the data sample set. The stability notion is based on quantifying the effect of observing a computation's outputs on the posterior over the data sample elements. We show a separation between this stability notion and previously studied notion and observe that all differentially private algorithms also satisfy this notion.

Author Information

Moshe Shenfeld (Hebrew University of Jerusalem)
Katrina Ligett (Hebrew University)

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