1 citations · 1 across the 2 of their papers we have counts for
2 papers
cs.LG2022★ 1 cited
Improving Uncertainty Quantification of Variance Networks by Tree-Structured Learning
Wenxuan Ma, Xing Yan, Kun Zhang
To improve the uncertainty quantification of variance networks, we propose a novel tree-structured local neural network model that partitions the feature space into multiple region…
cs.LG2022
Ensemble Multi-Quantiles: Adaptively Flexible Distribution Prediction for Uncertainty Quantification
Xing Yan, Yonghua Su, Wenxuan Ma
We propose a novel, succinct, and effective approach for distribution prediction to quantify uncertainty in machine learning. It incorporates adaptively flexible distribution predi…