A Necessary and Sufficient Stability Notion for Adaptive Generalization

Part of Advances in Neural Information Processing Systems 32 (NeurIPS 2019)

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Moshe Shenfeld, Katrina Ligett


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.