mlsanityData to 7 Oct 2026

Trimmed Density Ratio Estimation

arXiv 1703.03216 ↗First posted 9 Mar 2017stat.MLcited by 15

Not on the map. Why?

Abstract (arXiv)

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.

Key facts. Cited by 15 papers in the map (data through 7 Oct 2026). First posted on arXiv 9 Mar 2017.

Builds on

No reference list yet.

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 4; 2022: 1; 2023: 2; 2024: 2; 2025: 2; 2026: 4.

15 papers.

Sort:

By area: