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- W2912478319 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." @default.
- W2912478319 created "2019-02-21" @default.
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- W2912478319 date "2018-12-01" @default.
- W2912478319 modified "2023-10-01" @default.
- W2912478319 title "A Distributionally Robust Optimization Approach for Outlier Detection *" @default.
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- W2912478319 doi "https://doi.org/10.1109/cdc.2018.8619435" @default.
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