Abstract
We present a study of generalization for data-dependent hypothesis sets. We give a general learning guarantee for data-dependent hypothesis sets based on a notion of transductive Rademacher complexity. Our main result is a generalization bound for data-dependent hypothesis sets expressed in terms of a notion of hypothesis set stability and a notion of Rademacher complexity for data-dependent hypothesis sets that we introduce. This bound admits as special cases both standard Rademacher complexity bounds and algorithm-dependent uniform stability bounds. We also illustrate the use of these learning bounds in the analysis of several scenarios.
| Original language | English (US) |
|---|---|
| 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: Dec 8 2019 → Dec 14 2019 |
ASJC Scopus subject areas
- Computer Networks and Communications
- Information Systems
- Signal Processing
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Foster, D. J., Greenberg, S., Kale, S., Luo, H., Mohri, M., & Sridharan, K. (2019). Hypothesis set stability and generalization. Advances in Neural Information Processing Systems, 32.