19 citations · 27 across the 13 of their papers we have counts for
4 papers · 1 filter
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…
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…
Decorr: Environment Partitioning for Invariant Learning and OOD Generalization
Yufan Liao, Qi Wu, Zhaodi Wu +1
Invariant learning methods, aimed at identifying a consistent predictor across multiple environments, are gaining prominence in out-of-distribution (OOD) generalization. Yet, when…
Robust Causal Learning for the Estimation of Average Treatment Effects
Yiyan Huang, Cheuk Hang Leung, Xing Yan +5
Many practical decision-making problems in economics and healthcare seek to estimate the average treatment effect (ATE) from observational data. The Double/Debiased Machine Learnin…