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 procedure 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.
|Title of host publication||Advances in Neural Information Processing Systems 30 (NIPS 2017)|
|Number of pages||11|
|Publication status||Published - 4 Dec 2017|
Liu, S., Takeda, A., Suzuki, T., & Fukumizu, K. (2017). Trimmed Density Ratio Estimation. In Advances in Neural Information Processing Systems 30 (NIPS 2017) https://papers.nips.cc/paper/7038-trimmed-density-ratio-estimation.pdf