2012
NeurIPS
NeurIPS 2012
A Polynomial-time Form of Robust Regression
Abstract
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.
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Interdisciplinary Bridge
- Artificial Intelligence and Mathematics & Optimization
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Trend Setter
- Robust Learning
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Keyword Pioneer
- outlier handling
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Hot Topic Early Bird
- statistical learning
Authors
Topics
Machine Learning > Core Methods > Regression
Machine Learning > Optimization & Theory > Bayesian Inference
Machine Learning > Optimization & Theory > Optimization
Machine Learning > Optimization & Theory > Statistical Learning
Machine Learning > Learning Types > Supervised Learning
Mathematics & Optimization > Optimization > Convex Optimization
Machine Learning > Learning Types > Robust Learning
Machine Learning > Learning Types > Robustness