NeurIPS 2017poster26 citations
Trimmed Density Ratio Estimation
Song Liu, Akiko Takeda, Taiji Suzuki, Kenji Fukumizu
Abstract
Density ratio estimation is a vital tool in both machine learning and statistical community. However, due to the unbounded nature of density ratio, the estimation proceudre can be vulnerable to corrupted data points, which often pushes the estimated ratio toward infinity. In this paper, we present a robust estimator which automatically identifies and trims outliers. The proposed estimator has a convex formulation, and the global optimum can be obtained via subgradient descent. We analyze the parameter estimation error of this estimator under high-dimensional settings. Experiments are conducted to verify the effectiveness of the estimator.
BibTeX
@inproceedings{NIPS2017_ea204361,
author = {Liu, Song and Takeda, Akiko and Suzuki, Taiji and Fukumizu, Kenji},
booktitle = {Advances in Neural Information Processing Systems},
editor = {I. Guyon and U. Von Luxburg and S. Bengio and H. Wallach and R. Fergus and S. Vishwanathan and R. Garnett},
pages = {},
publisher = {Curran Associates, Inc.},
title = {Trimmed Density Ratio Estimation},
url = {https://proceedings.neurips.cc/paper_files/paper/2017/file/ea204361fe7f024b130143eb3e189a18-Paper.pdf},
volume = {30},
year = {2017}
}