Distributionally Robust Learning under the Wasserstein Metric
This dissertation develops a comprehensive statistical learning framework that is robust to (distributional) perturbations in the data using Distributionally Robust Optimization (DRO) under the Wasserstein metric. The learning problems that are studied include: (i) Distributionally Robust Linear Reg...
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| Language: | en_US |
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2019
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| Online Access: | https://hdl.handle.net/2144/38236 |