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2012 NeurIPS NeurIPS 2012

A Polynomial-time Form of Robust Regression

Despite the variety of robust regression methods that have been developed, current regression formulations are either NP-hard, or allow unbounded response to even a single leverage point. We present a general formulation for robust regression --Variational M-estimation--that unifies a number of robust regression methods while allowing a tractable approximation strategy. We develop an estimator that requires only polynomial-time, while achieving certain robustness and consistency guarantees. An experimental evaluation demonstrates the effectiveness of the new estimation approach compared to standard methods.

🌉 Interdisciplinary Bridge - Artificial Intelligence and Mathematics & Optimization
📈 Trend Setter - Robust Learning
🧭 Keyword Pioneer - outlier handling
🐣 Hot Topic Early Bird - statistical learning

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