On the fine-grained complexity of empirical risk minimization: Kernel methods and neural networks
© 2017 Neural information processing systems foundation. All rights reserved. Empirical risk minimization (ERM) is ubiquitous in machine learning and underlies most supervised learning methods. While there is a large body of work on algorithms for various ERM problems, the exact computational comple...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
2021
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| Online Access: | https://hdl.handle.net/1721.1/137640 |