Daokun Zhang — accepted AI-conference papers

2025

Label Distribution Learning using the Squared Neural Family on the Probability Simplex

Daokun Zhang, Russell Tsuchida, Dino Sejdinovic

UAI 2025

Label distribution learning (LDL) provides a framework wherein a distribution over categories rather than a single category is predicted, with the aim of addressing ambiguity in labeled data. Existing research on LDL mainly focuses on the task of point estimation, i.e., finding an optimal distributi

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2023

Beyond Smoothing: Unsupervised Graph Representation Learning with Edge Heterophily Discriminating

Yixin Liu, Yizhen Zheng, Daokun Zhang, Vincent CS Lee, Shirui Pan

AAAI 2023technical

Unsupervised graph representation learning (UGRL) has drawn increasing research attention and achieved promising results in several graph analytic tasks. Relying on the homophily assumption, existing UGRL methods tend to smooth the learned node representations along all edges, ignoring the existence…

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