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2020 – today
- 2023
[c9]Qixin Zhang, Zengde Deng, Zaiyi Chen, Kuangqi Zhou, Haoyuan Hu, Yu Yang:
Online Learning for Non-monotone DR-Submodular Maximization: From Full Information to Bandit Feedback. AISTATS 2023: 3515-3537- [c8]Kaixin Wang, Kuangqi Zhou, Bingyi Kang, Jiashi Feng, Shuicheng Yan:
Revisiting Intrinsic Reward for Exploration in Procedurally Generated Environments. ICLR 2023 - [c7]Kaixin Wang, Kuangqi Zhou, Jiashi Feng, Bryan Hooi, Xinchao Wang:
Reachability-Aware Laplacian Representation in Reinforcement Learning. ICML 2023: 36670-36693 - 2022
- [c6]Yujun Shi, Kuangqi Zhou, Jian Liang, Zihang Jiang, Jiashi Feng, Philip H. S. Torr, Song Bai, Vincent Y. F. Tan:
Mimicking the Oracle: An Initial Phase Decorrelation Approach for Class Incremental Learning. CVPR 2022: 16701-16710 - [c5]Kaixin Wang, Navdeep Kumar, Kuangqi Zhou, Bryan Hooi, Jiashi Feng, Shie Mannor:
The Geometry of Robust Value Functions. ICML 2022: 22727-22751 - [c4]Kuangqi Zhou, Kaixin Wang, Jian Tang, Jiashi Feng, Bryan Hooi, Peilin Zhao, Tingyang Xu, Xinchao Wang:
Jointly Modelling Uncertainty and Diversity for Active Molecular Property Prediction. LoG 2022: 29 - [c3]Chengqian Gao, Ke Xu, Kuangqi Zhou, Lanqing Li, Xueqian Wang, Bo Yuan, Peilin Zhao:
Value Penalized Q-Learning for Recommender Systems. SIGIR 2022: 2008-2012 - [i9]Kaixin Wang, Navdeep Kumar, Kuangqi Zhou, Bryan Hooi, Jiashi Feng, Shie Mannor:
The Geometry of Robust Value Functions. CoRR abs/2201.12929 (2022) - [i8]Kuangqi Zhou, Kaixin Wang, Jiashi Feng, Jian Tang, Tingyang Xu
, Xinchao Wang:
Tyger: Task-Type-Generic Active Learning for Molecular Property Prediction. CoRR abs/2205.11279 (2022) - [i7]Qixin Zhang, Zengde Deng, Zaiyi Chen, Kuangqi Zhou, Haoyuan Hu, Yu Yang:
Online Learning for Non-monotone Submodular Maximization: From Full Information to Bandit Feedback. CoRR abs/2208.07632 (2022) - [i6]Kaixin Wang, Kuangqi Zhou, Jiashi Feng, Bryan Hooi, Xinchao Wang:
Reachability-Aware Laplacian Representation in Reinforcement Learning. CoRR abs/2210.13153 (2022) - 2021
- [c2]Kuangqi Zhou, Yanfei Dong, Kaixin Wang, Wee Sun Lee, Bryan Hooi, Huan Xu, Jiashi Feng:
Understanding and Resolving Performance Degradation in Deep Graph Convolutional Networks. CIKM 2021: 2728-2737 - [c1]Kaixin Wang, Kuangqi Zhou, Qixin Zhang, Jie Shao, Bryan Hooi, Jiashi Feng:
Towards Better Laplacian Representation in Reinforcement Learning with Generalized Graph Drawing. ICML 2021: 11003-11012 - [i5]Kaixin Wang, Kuangqi Zhou, Qixin Zhang, Jie Shao, Bryan Hooi, Jiashi Feng:
Towards Better Laplacian Representation in Reinforcement Learning with Generalized Graph Drawing. CoRR abs/2107.05545 (2021) - [i4]Yujun Shi, Kuangqi Zhou, Jian Liang, Zihang Jiang, Jiashi Feng, Philip H. S. Torr, Song Bai, Vincent Y. F. Tan:
Mimicking the Oracle: An Initial Phase Decorrelation Approach for Class Incremental Learning. CoRR abs/2112.04731 (2021) - 2020
- [i3]Kuangqi Zhou, Yanfei Dong, Wee Sun Lee, Bryan Hooi, Huan Xu, Jiashi Feng:
Effective Training Strategies for Deep Graph Neural Networks. CoRR abs/2006.07107 (2020) - [i2]Kuangqi Zhou, Qibin Hou, Zun Li, Jiashi Feng:
Multi-Miner: Object-Adaptive Region Mining for Weakly-Supervised Semantic Segmentation. CoRR abs/2006.07834 (2020) - [i1]Zihang Jiang, Bingyi Kang, Kuangqi Zhou, Jiashi Feng:
Few-shot Classification via Adaptive Attention. CoRR abs/2008.02465 (2020)
Coauthor Index
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