A User-Friendly Computational Framework for Robust Structured Regression Using the L_2 Criterion
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We introduce a user-friendly computational framework for implementing robust versions of a wide variety of structured regression methods using the L_2 criterion. In addition to introducing a scalable algorithm for performing L_2E regression, our framework also enables robust regression using the L_2 criterion for additional structural constraints, works without requiring complex tuning procedures, can be used to automatically identify heterogeneous subpopulations, and can incorporate readily available non-robust structured regression solvers. We provide convergence guarantees for the framework and demonstrate its flexibility with some examples.
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