The DOI matches its Crossref record; 61 of the 61 references it cites were found in a registry or an index.
Robustness and generalization
Open accessAbstract
We derive generalization bounds for learning algorithms based on their robustness: the property that if a testing sample is “similar” to a training sample, then the testing error is close to the training error. This provides a novel approach, different from complexity or stability arguments, to study generalization of learning algorithms. One advantage of the robustness approach, compared to previous methods, is the geometric intuition it conveys. Consequently, robustness-based analysis is easy to extend to learning in non-standard setups such as Markovian samples or quantile loss. We further show that a weak notion of robustness is both sufficient and necessary for generalizability, which implies that robustness is a fundamental property that is required for learning algorithms to work.
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Cites
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Reinforcement Learning: An Introduction
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Zhang, Y., Sutton, C. A., Storkey, A., & Zoubin Ghahramani, A. (2012) · Cited by 12,653
Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond
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Cited by
Active Learning for Convolutional Neural Networks: A Core-Set Approach
Sener, O., & Savarese, S. (2017) · arXiv · doi:10.48550/arxiv.1708.00489 · Cited by 922
A Review of Domain Adaptation without Target Labels
Kouw, W. M., & Loog, M. (2021) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2019.2945942 · Cited by 613
Trustworthy AI: From Principles to Practices
Li, B., Qi, P., Liu, B., Di, S., Liu, J., Pei, J., Yi, J., & Zhou, B. (2023) · ACM Computing Surveys · doi:10.1145/3555803 · Cited by 612
“As demonstrated in Reference [362], an algorithm that is robust against small perturbations has better generalization.”
A Survey on Metric Learning for Feature Vectors and Structured Data
Bellet, A., Habrard, A., & Sebban, M. (2013) · arXiv (Cornell University) · doi:10.48550/arxiv.1306.6709 · Cited by 494
Binary Ensemble Neural Network: More Bits per Network or More Networks per Bit?
Zhu, S., Dong, X., & Su, H. (2019) · 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2019.00506 · Cited by 153
“Classical learning theory tells us that both robustness and stability are closely related to the generalization error of a model [62, 4].”
Background
Metadata
- Authors
- Xu, H., & Mannor, S.
- Venue
- Machine Learning
- Published
- 2011-11-15
- Volume
- 86
- Issue
- 3
- Pages
- 391-423
- ISSN
- 0885-6125 (ISSN-L)
- 1573-0565
- OpenAlex
- W1892947258
- Cited by
- 377
- References
- 61
- FWCI
- 5.91
- Type
- journal article
- Language
- en
- Open access
- bronze
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Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
