A Distributionally Robust Optimization Approach for Outlier Detection

Ruidi Chen1, Ioannis Ch. Paschalidis1

  • 1Boston University

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Category

Regular Session

Sessions

10:00 - 12:00 | Mon 17 Dec | Glimmer 2 | MoA10

Machine Learning I

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Abstract

We consider the outlier detection problem in a linear regression setting. Outlying observations can be detected by large residuals but this approach is not robust to large outliers which tend to shift the residual function. Instead, we propose a new Distributionally Robust Optimization (DRO) method addressing this issue. The robust optimization problem reduces to solving a second-order cone programming problem. We prove several generalization guarantees for our solution under mild conditions. Extensive numerical experiments demonstrate that our approach outperforms Huber's robust regression approach.

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