2012 NIPS 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.

🧭 Keyword Pioneer β€” variational estimation
🐝 Cross-Pollinator β€” Artificial Intelligence, Computer Science, Computer Vision, Data Science & Analytics, Deep Learning, Healthcare & Medicine, Interdisciplinary, Machine Learning, Mathematics & Optimization, Natural Language Processing, Reinforcement Learning, Security & Privacy
🌱 Topic Pioneer β€” Robustness
πŸŒ‰ Interdisciplinary Bridge β€” Machine Learning and Mathematics & Optimization
πŸ“ˆ Trend Setter β€” Robustness
🐣 Hot Topic Early Bird β€” statistical learning