3 citations · 5 across the 3 of their papers we have counts for
3 papers
cs.LG2024
Overcoming Pitfalls in Graph Contrastive Learning Evaluation: Toward Comprehensive Benchmarks
Qian Ma, Hongliang Chi, Hengrui Zhang +6
The rise of self-supervised learning, which operates without the need for labeled data, has garnered significant interest within the graph learning community. This enthusiasm has l…
cs.LG2024★ 2 cited
Multitask Active Learning for Graph Anomaly Detection
Wenjing Chang, Kay Liu, Kaize Ding +2
In the web era, graph machine learning has been widely used on ubiquitous graph-structured data. As a pivotal component for bolstering web security and enhancing the robustness of…
cs.LG2023★ 3 cited
Equal Opportunity of Coverage in Fair Regression
Fangxin Wang, Lu Cheng, Ruocheng Guo +2
We study fair machine learning (ML) under predictive uncertainty to enable reliable and trustworthy decision-making. The seminal work of ``equalized coverage'' proposed an uncertai…