Kuangqi Zhou

Publication Activity (10 Years)

Years Active: 2020-2023
Publications (10 Years): 18

Top Topics

Reinforcement Learning

Semantic Segmentation

Top Venues

Publications

Kaixin Wang, Kuangqi Zhou, Jiashi Feng, Bryan Hooi, Xinchao Wang
Reachability-Aware Laplacian Representation in Reinforcement Learning. ICML (2023)
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)
Kaixin Wang, Kuangqi Zhou, Bingyi Kang, Jiashi Feng, Shuicheng Yan
Revisiting Intrinsic Reward for Exploration in Procedurally Generated Environments. ICLR (2023)
Kaixin Wang, Navdeep Kumar, Kuangqi Zhou, Bryan Hooi, Jiashi Feng, Shie Mannor
The Geometry of Robust Value Functions. CoRR (2022)
Kaixin Wang, Navdeep Kumar, Kuangqi Zhou, Bryan Hooi, Jiashi Feng, Shie Mannor
The Geometry of Robust Value Functions. ICML (2022)
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)
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 (2022)
Kuangqi Zhou, Kaixin Wang, Jiashi Feng, Jian Tang, Tingyang Xu, Xinchao Wang
Tyger: Task-Type-Generic Active Learning for Molecular Property Prediction. CoRR (2022)
Kaixin Wang, Kuangqi Zhou, Jiashi Feng, Bryan Hooi, Xinchao Wang
Reachability-Aware Laplacian Representation in Reinforcement Learning. CoRR (2022)
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)
Chengqian Gao, Ke Xu, Kuangqi Zhou, Lanqing Li, Xueqian Wang, Bo Yuan, Peilin Zhao
Value Penalized Q-Learning for Recommender Systems. SIGIR (2022)
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 (2021)
Kaixin Wang, Kuangqi Zhou, Qixin Zhang, Jie Shao, Bryan Hooi, Jiashi Feng
Towards Better Laplacian Representation in Reinforcement Learning with Generalized Graph Drawing. CoRR (2021)
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)
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)
Zihang Jiang, Bingyi Kang, Kuangqi Zhou, Jiashi Feng
Few-shot Classification via Adaptive Attention. CoRR (2020)
Kuangqi Zhou, Yanfei Dong, Wee Sun Lee, Bryan Hooi, Huan Xu, Jiashi Feng
Effective Training Strategies for Deep Graph Neural Networks. CoRR (2020)
Kuangqi Zhou, Qibin Hou, Zun Li, Jiashi Feng
Multi-Miner: Object-Adaptive Region Mining for Weakly-Supervised Semantic Segmentation. CoRR (2020)