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20192023
most citedA Comprehensive Survey on Trustworthy Graph Neural Networks: Privacy, Robustness, Fairness, and Explainability

87 citations · 343 across the 20 of their papers we have counts for

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Showing 2019Show all

5 papers · 1 filter

cs.CL2019★ 1 cited

Learning Multi-level Dependencies for Robust Word Recognition

Zhiwei Wang, Hui Liu, Jiliang Tang +3

Robust language processing systems are becoming increasingly important given the recent awareness of dangerous situations where brittle machine learning models can be easily broken…

cs.LG2019

Adversarial Attacks and Defenses in Images, Graphs and Text: A Review

Han Xu, Yao Ma, Haochen Liu +4

Deep neural networks (DNN) have achieved unprecedented success in numerous machine learning tasks in various domains. However, the existence of adversarial examples has raised conc…

cs.CL2019★ 11 cited

Does Gender Matter? Towards Fairness in Dialogue Systems

Haochen Liu, Jamell Dacon, Wenqi Fan +3

Recently there are increasing concerns about the fairness of Artificial Intelligence (AI) in real-world applications such as computer vision and recommendations. For example, recog…

cs.IR2019★ 9 cited

DEAR: Deep Reinforcement Learning for Online Advertising Impression in Recommender Systems

Xiangyu Zhao, Changsheng Gu, Haoshenglun Zhang +4

With the recent prevalence of Reinforcement Learning (RL), there have been tremendous interests in utilizing RL for online advertising in recommendation platforms (e.g., e-commerce…

cs.IR2019★ 49 cited

Whole-Chain Recommendations

Xiangyu Zhao, Long Xia, Linxin Zou +3

With the recent prevalence of Reinforcement Learning (RL), there have been tremendous interests in developing RL-based recommender systems. In practical recommendation sessions, us…