24 citations · 68 across the 9 of their papers we have counts for
9 papers
Towards Off-Policy Reinforcement Learning for Ranking Policies with Human Feedback
Teng Xiao, Suhang Wang
Probabilistic learning to rank (LTR) has been the dominating approach for optimizing the ranking metric, but cannot maximize long-term rewards. Reinforcement learning models have b…
Learning How to Propagate Messages in Graph Neural Networks
Teng Xiao, Zhengyu Chen, Donglin Wang +1
This paper studies the problem of learning message propagation strategies for graph neural networks (GNNs). One of the challenges for graph neural networks is that of defining the…
A General Offline Reinforcement Learning Framework for Interactive Recommendation
Teng Xiao, Donglin Wang
This paper studies the problem of learning interactive recommender systems from logged feedbacks without any exploration in online environments. We address the problem by proposing…
Towards Fair Graph Neural Networks via Graph Counterfactual
Zhimeng Guo, Jialiang Li, Teng Xiao +2
Graph neural networks have shown great ability in representation (GNNs) learning on graphs, facilitating various tasks. Despite their great performance in modeling graphs, recent w…
Fairness-aware Message Passing for Graph Neural Networks
Huaisheng Zhu, Guoji Fu, Zhimeng Guo +3
Graph Neural Networks (GNNs) have shown great power in various domains. However, their predictions may inherit societal biases on sensitive attributes, limiting their adoption in r…
Counterfactual Learning on Graphs: A Survey
Zhimeng Guo, Teng Xiao, Zongyu Wu +3
Graph-structured data are pervasive in the real-world such as social networks, molecular graphs and transaction networks. Graph neural networks (GNNs) have achieved great success i…
Decoupled Self-supervised Learning for Non-Homophilous Graphs
Teng Xiao, Zhengyu Chen, Zhimeng Guo +2
This paper studies the problem of conducting self-supervised learning for node representation learning on graphs. Most existing self-supervised learning methods assume the graph is…
Semi-supervisedly Co-embedding Attributed Networks
Zaiqiao Meng, Shangsong Liang, Jinyuan Fang +1
Deep generative models (DGMs) have achieved remarkable advances. Semi-supervised variational auto-encoders (SVAE) as a classical DGM offer a principled framework to effectively gen…
Neural Variational Hybrid Collaborative Filtering
Teng Xiao, Shangsong Liang, Hong Shen +1
Collaborative Filtering (CF) is one of the most used methods for Recommender System. Because of the Bayesian nature and nonlinearity, deep generative models, e.g. Variational Autoe…