Distributionally Robust Learning under the Wasserstein Metric

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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Bibliographic Details
Main Author: Chen, Ruidi
Other Authors: Paschalidis, Ioannis Ch.
Language:en_US
Published: 2019
Subjects:
Statistics
Distributionally robust optimization
Grouped variable selection
Health informatics
Multivariate linear regression
Regression
Classification
Wasserstein metric
Online Access:https://hdl.handle.net/2144/38236

Internet

https://hdl.handle.net/2144/38236

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