Abstract
The traditional notion of generalization-i.e., learning a hypothesis whose empirical error is close to its true error-is surprisingly brittle. As has recently been noted (Dwork et al., 2015c), even if several algorithms have this guarantee in isolation, the guarantee need not hold if the algorithms are composed adaptively. In this paper, we study three notions of generalization-increasing in strength-that are robust to postprocessing and amenable to adaptive composition, and examine the relationships between them. We call the weakest such notion Robust Generalization. A second, intermediate, notion is the stability guarantee known as differential privacy. The strongest guarantee we consider we call Perfect Generalization. We prove that every hypothesis class that is PAC learnable is also PAC learnable in a robustly generalizing fashion, with almost the same sample complexity. It was previously known that differentially private algorithms satisfy robust generalization. In this paper, we show that robust generalization is a strictly weaker concept, and that there is a learning task that can be carried out subject to robust generalization guarantees, yet cannot be carried out subject to differential privacy. We also show that perfect generalization is a strictly stronger guarantee than differential privacy, but that, nevertheless, many learning tasks can be carried out subject to the guarantees of perfect generalization.
Original language | English (US) |
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Pages (from-to) | 772-814 |
Number of pages | 43 |
Journal | Proceedings of Machine Learning Research |
Volume | 49 |
Issue number | June |
State | Published - Jun 6 2016 |
Externally published | Yes |
Event | 29th Conference on Learning Theory, COLT 2016 - New York, United States Duration: Jun 23 2016 → Jun 26 2016 |
Bibliographical note
Publisher Copyright:© 2016 R. Cummings, K. Ligett, K. Nissim, A. Roth & Z.S. Wu.
Keywords
- Adaptive learning
- Composition
- Compression schemes
- Generalizations