3 citations · 9 across the 15 of their papers we have counts for
4 papers · 1 filter
Domain Invariant Learning for Gaussian Processes and Bayesian Exploration
Xilong Zhao, Siyuan Bian, Yaoyun Zhang +5
Out-of-distribution (OOD) generalization has long been a challenging problem that remains largely unsolved. Gaussian processes (GP), as popular probabilistic model classes, especia…
Graph Out-of-Distribution Generalization with Controllable Data Augmentation
Bin Lu, Xiaoying Gan, Ze Zhao +4
Graph Neural Network (GNN) has demonstrated extraordinary performance in classifying graph properties. However, due to the selection bias of training and testing data (e.g., traini…
Prediction with Incomplete Data under Agnostic Mask Distribution Shift
Yichen Zhu, Jian Yuan, Bo Jiang +4
Data with missing values is ubiquitous in many applications. Recent years have witnessed increasing attention on prediction with only incomplete data consisting of observed feature…
Asymmetric Polynomial Loss For Multi-Label Classification
Yusheng Huang, Jiexing Qi, Xinbing Wang +1
Various tasks are reformulated as multi-label classification problems, in which the binary cross-entropy (BCE) loss is frequently utilized for optimizing well-designed models. Howe…