9 citations · 25 across the 6 of their papers we have counts for
6 papers
Nearly Optimal Bayesian Inference for Structural Missingness
Chen Liang, Donghua Yang, Yutong Zhao +9
Structural missingness breaks 'just impute and train': values can be undefined by causal or logical constraints, and the mask may depend on observed variables, unobserved variables…
Towards Poisoning Fair Representations
Tianci Liu, Haoyu Wang, Feijie Wu +4
Fair machine learning seeks to mitigate model prediction bias against certain demographic subgroups such as elder and female. Recently, fair representation learning (FRL) trained b…
MDM: Molecular Diffusion Model for 3D Molecule Generation
Lei Huang, Hengtong Zhang, Tingyang Xu +1
Molecule generation, especially generating 3D molecular geometries from scratch (i.e., 3D \textit{de novo} generation), has become a fundamental task in drug designs. Existing diff…
A Survey of Trustworthy Graph Learning: Reliability, Explainability, and Privacy Protection
Bingzhe Wu, Jintang Li, Junchi Yu +17
Deep graph learning has achieved remarkable progresses in both business and scientific areas ranging from finance and e-commerce, to drug and advanced material discovery. Despite t…
Practical Data Poisoning Attack against Next-Item Recommendation
Hengtong Zhang, Yaliang Li, Bolin Ding +1
Online recommendation systems make use of a variety of information sources to provide users the items that users are potentially interested in. However, due to the openness of the…
Data Poisoning Attack against Knowledge Graph Embedding
Hengtong Zhang, Tianhang Zheng, Jing Gao +4
Knowledge graph embedding (KGE) is a technique for learning continuous embeddings for entities and relations in the knowledge graph.Due to its benefit to a variety of downstream ta…