Uncertainty in Artificial Intelligence
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A Consistent Estimator of the Expected Gradient Outerproduct
Shubhendu Trivedi, Jialei Wang, Samory Kpotufe, Gregory Shakhnarovich
In high-dimensional classification or regression problems, the expected gradient outerproduct (EGOP) of the unknown regression function f , namely EX f (X) · f (X) , is known to recover those directions v ?? Rd most relevant to predicting the output Y . However, just as in gradient estimation, opti- mal estimators of the EGOP can be expensive in practice. We show that a simple rough estima- tor, much cheaper in practice, suffices to obtain significant improvements on real-world nonpara- metric classification and regression tasks. Fur- thermore, we prove that, despite its simplicity, this rough estimator remains statistically consis- tent under mild conditions.
Pages: 819-828
PS Link:
PDF Link: /papers/14/p819-trivedi.pdf
AUTHOR = "Shubhendu Trivedi and Jialei Wang and Samory Kpotufe and Gregory Shakhnarovich",
TITLE = "A Consistent Estimator of the Expected Gradient Outerproduct",
BOOKTITLE = "Proceedings of the Thirtieth Conference Annual Conference on Uncertainty in Artificial Intelligence (UAI-14)",
ADDRESS = "Corvallis, Oregon",
YEAR = "2014",
PAGES = "819--828"

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