Abstract
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.
| Original language | English |
|---|---|
| Journal | Advances in Neural Information Processing Systems |
| Volume | 32 |
| State | Published - 2019 |
| Event | 33rd Annual Conference on Neural Information Processing Systems, NeurIPS 2019 - Vancouver, Canada Duration: 8 Dec 2019 → 14 Dec 2019 |
Bibliographical note
Publisher Copyright:© 2019 Neural information processing systems foundation. All rights reserved.