11 citations · 15 across the 5 of their papers we have counts for
5 papers
Active Learning for Graphs with Noisy Structures
Hongliang Chi, Cong Qi, Suhang Wang +1
Graph Neural Networks (GNNs) have seen significant success in tasks such as node classification, largely contingent upon the availability of sufficient labeled nodes. Yet, the exce…
Enhancing Graph Contrastive Learning with Node Similarity
Hongliang Chi, Yao Ma
Graph Neural Networks (GNNs) have achieved great success in learning graph representations and thus facilitating various graph-related tasks. However, most GNN methods adopt a supe…
Multimodality in Meta-Learning: A Comprehensive Survey
Yao Ma, Shilin Zhao, Weixiao Wang +2
Meta-learning has gained wide popularity as a training framework that is more data-efficient than traditional machine learning methods. However, its generalization ability in compl…
Gradient Descent for Sparse Rank-One Matrix Completion for Crowd-Sourced Aggregation of Sparsely Interacting Workers
Yao Ma, Alex Olshevsky, Venkatesh Saligrama +1
We consider worker skill estimation for the single-coin Dawid-Skene crowdsourcing model. In practice, skill-estimation is challenging because worker assignments are sparse and irre…
Crowdsourcing with Sparsely Interacting Workers
Yao Ma, Alex Olshevsky, Venkatesh Saligrama +1
We consider estimation of worker skills from worker-task interaction data (with unknown labels) for the single-coin crowd-sourcing binary classification model in symmetric noise. W…