Smoothness and Adaptivity in Nonlinear Optimization for Machine Learning Applications
Nonlinear optimization has become the workhorse of machine learning. However, our theoretical understanding of optimization in machine learning is still limited. For example, classical optimization theory relies on assumptions like bounded Lipschitz smoothness of the loss function which are rarely m...
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| Format: | Thesis |
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Massachusetts Institute of Technology
2024
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| Online Access: | https://hdl.handle.net/1721.1/156294 |