4 citations · 6 across the 6 of their papers we have counts for
6 papers · 1 filter
Communication-Efficient and Privacy-Preserving Decentralized Meta-Learning
Hansi Yang, James T. Kwok
Distributed learning, which does not require gathering training data in a central location, has become increasingly important in the big-data era. In particular, random-walk-based…
Mixup Augmentation with Multiple Interpolations
Lifeng Shen, Jincheng Yu, Hansi Yang +1
Mixup and its variants form a popular class of data augmentation techniques.Using a random sample pair, it generates a new sample by linear interpolation of the inputs and labels.…
Loss-aware Curriculum Learning for Heterogeneous Graph Neural Networks
Zhen Hao Wong, Hansi Yang, Xiaoyi Fu +1
Heterogeneous Graph Neural Networks (HGNNs) are a class of deep learning models designed specifically for heterogeneous graphs, which are graphs that contain different types of nod…
Positive-Unlabeled Node Classification with Structure-aware Graph Learning
Hansi Yang, Yongqi Zhang, Quanming Yao +1
Node classification on graphs is an important research problem with many applications. Real-world graph data sets may not be balanced and accurate as assumed by most existing works…
An Adaptive Policy to Employ Sharpness-Aware Minimization
Weisen Jiang, Hansi Yang, Yu Zhang +1
Sharpness-aware minimization (SAM), which searches for flat minima by min-max optimization, has been shown to be useful in improving model generalization. However, since each SAM u…
Searching to Exploit Memorization Effect in Learning from Corrupted Labels
Quanming Yao, Hansi Yang, Bo Han +2
Sample selection approaches are popular in robust learning from noisy labels. However, how to properly control the selection process so that deep networks can benefit from the memo…