Effective Training Strategies for Deep Graph Neural Networks. - Kuangqi Zhou; Yanfei Dong; Wee Sun Lee; Bryan Hooi; Huan Xu 001; Jiashi Feng (CoRR, 2020)

Multi-Miner: Object-Adaptive Region Mining for Weakly-Supervised Semantic Segmentation. - Kuangqi Zhou; Qibin Hou; Zun Li 001; Jiashi Feng (CoRR, 2020)

Few-shot Classification via Adaptive Attention. - Zihang Jiang; Bingyi Kang; Kuangqi Zhou; Jiashi Feng (CoRR, 2020)

Understanding and Resolving Performance Degradation in Deep Graph Convolutional Networks. - Kuangqi Zhou; Yanfei Dong; Kaixin Wang; Wee Sun Lee; Bryan Hooi; Huan Xu 001; Jiashi Feng (CIKM, 2021)

Towards Better Laplacian Representation in Reinforcement Learning with Generalized Graph Drawing. - Kaixin Wang; Kuangqi Zhou; Qixin Zhang 001; Jie Shao 006; Bryan Hooi; Jiashi Feng (ICML, 2021)

Mimicking the Oracle: An Initial Phase Decorrelation Approach for Class Incremental Learning. - Yujun Shi; Kuangqi Zhou; Jian Liang 001; Zihang Jiang; Jiashi Feng; Philip H. S. Torr; Song Bai 001; Vincent Y. F. Tan (CVPR, 2022)

The Geometry of Robust Value Functions. - Kaixin Wang; Navdeep Kumar; Kuangqi Zhou; Bryan Hooi; Jiashi Feng; Shie Mannor (ICML, 2022)

Jointly Modelling Uncertainty and Diversity for Active Molecular Property Prediction. - Kuangqi Zhou; Kaixin Wang; Jian Tang 005; Jiashi Feng; Bryan Hooi; Peilin Zhao; Tingyang Xu; Xinchao Wang (LoG, 2022)

Value Penalized Q-Learning for Recommender Systems. - Chengqian Gao; Ke Xu; Kuangqi Zhou; Lanqing Li; Xueqian Wang 001; Bo Yuan 008; Peilin Zhao (SIGIR, 2022)

Tyger: Task-Type-Generic Active Learning for Molecular Property Prediction. - Kuangqi Zhou; Kaixin Wang; Jiashi Feng; Jian Tang 005; Tingyang Xu; Xinchao Wang (CoRR, 2022)

Online Learning for Non-monotone Submodular Maximization: From Full Information to Bandit Feedback. - Qixin Zhang 001; Zengde Deng; Zaiyi Chen; Kuangqi Zhou; Haoyuan Hu; Yu Yang 001 (CoRR, 2022)

Online Learning for Non-monotone DR-Submodular Maximization: From Full Information to Bandit Feedback. - Qixin Zhang 001; Zengde Deng; Zaiyi Chen; Kuangqi Zhou; Haoyuan Hu; Yu Yang 001 (AISTATS, 2023)

Revisiting Intrinsic Reward for Exploration in Procedurally Generated Environments. - Kaixin Wang; Kuangqi Zhou; Bingyi Kang; Jiashi Feng; Shuicheng Yan (ICLR, 2023)

Reachability-Aware Laplacian Representation in Reinforcement Learning. - Kaixin Wang; Kuangqi Zhou; Jiashi Feng; Bryan Hooi; Xinchao Wang (ICML, 2023)