### Abstract

In high-dimensional classification or regression problems, the expected gradient outerproduct (EGOP) of the unknown regression function f, namely E_{X} (Δf(X) Δf(X)^{T}, is known to recover those directions v ε R^{d} most relevant to predicting the output Y. However, just as in gradient estimation, optimal estimators of the EGOP can be expensive in practice. We show that a simple rough estimator, much cheaper in practice, suffices to obtain significant improvements on real-world nonparametric classification and regression tasks. Furthermore, we prove that, despite its simplicity, this rough estimator remains statistically consistent under mild conditions.

Original language | English (US) |
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Title of host publication | Uncertainty in Artificial Intelligence - Proceedings of the 30th Conference, UAI 2014 |

Editors | Nevin L. Zhang, Jin Tian |

Publisher | AUAI Press |

Pages | 819-828 |

Number of pages | 10 |

ISBN (Electronic) | 9780974903910 |

State | Published - Jan 1 2014 |

Event | 30th Conference on Uncertainty in Artificial Intelligence, UAI 2014 - Quebec City, Canada Duration: Jul 23 2014 → Jul 27 2014 |

### Publication series

Name | Uncertainty in Artificial Intelligence - Proceedings of the 30th Conference, UAI 2014 |
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### Other

Other | 30th Conference on Uncertainty in Artificial Intelligence, UAI 2014 |
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Country | Canada |

City | Quebec City |

Period | 7/23/14 → 7/27/14 |

### All Science Journal Classification (ASJC) codes

- Artificial Intelligence

## Cite this

*Uncertainty in Artificial Intelligence - Proceedings of the 30th Conference, UAI 2014*(pp. 819-828). (Uncertainty in Artificial Intelligence - Proceedings of the 30th Conference, UAI 2014). AUAI Press.