19 citations · 27 across the 13 of their papers we have counts for
5 papers · 1 filter
Invariant Random Forest: Tree-Based Model Solution for OOD Generalization
Yufan Liao, Qi Wu, Xing Yan
Out-Of-Distribution (OOD) generalization is an essential topic in machine learning. However, recent research is only focusing on the corresponding methods for neural networks. This…
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…
Generative Learning of Heterogeneous Tail Dependence
Xiangqian Sun, Xing Yan, Qi Wu
We propose a multivariate generative model to capture the complex dependence structure often encountered in business and financial data. Our model features heterogeneous and asymme…