Multi-Stage Self-Supervised Learning for Graph Convolutional Networks on Graphs with Few Labeled Nodes K Sun, Z Zhu, Z Lin AAAI 2020, 2019 | 260 | 2019 |
AdaGCN: Adaboosting Graph Convolutional Networks into Deep Models K Sun, Z Lin, Z Zhu ICLR 2021, 2019 | 98 | 2019 |
Virtual Adversarial Training on Graph Convolutional Networks in Node Classification K Sun, H Guo, Z Zhu, Z Lin PRCV2019, 2019 | 29 | 2019 |
Towards Understanding Adversarial Examples Systematically: Exploring Data Size, Task and Model Factors K Sun, Z Zhu, Z Lin arXiv preprint arXiv:1902.11019, https://arxiv.org/abs/1902.11019, 2019 | 29 | 2019 |
Enhancing the Robustness of Deep Neural Networks by Boundary Conditional GAN K Sun, Z Zhu, Z Lin arXiv preprint arXiv:1902.11029, https://arxiv.org/abs/1902.11029, 2019 | 24 | 2019 |
Damped Anderson Mixing for Deep Reinforcement Learning: Acceleration, Convergence, and Stabilization K Sun, Y Wang, Y Liu, Y Zhao, B Pan, S Jui, B Jiang, L Kong NeurIPS 2021, 2021 | 16 | 2021 |
Exploring the Training Robustness of Distributional Reinforcement Learning against Noisy State Observations K Sun, Y Zhao, S Jui, L Kong ECML-PKDD 2023, 2021 | 16* | 2021 |
Identification, Amplification and Measurement: A bridge to Gaussian Differential Privacy Y Liu, K Sun, L Kong, B Jiang NeurIPS 2022, 2022 | 8 | 2022 |
How Does Return Distribution in Distributional Reinforcement Learning Help Optimization? K Sun, B Jiang, L Kong ICML 2024 Workshop: Aligning Reinforcement Learning Experimentalists and …, 2024 | 5* | 2024 |
Distributional Reinforcement Learning by Sinkhorn Divergence K Sun, Y Zhao, W Liu, B Jiang, L Kong arXiv preprint arXiv:2202.00769, 2022 | 5* | 2022 |
Interpreting Distributional Reinforcement Learning: A Regularization Perspective K Sun, Y Zhao, Y Liu, E Shi, Y Wang, X Yan, B Jiang, L Kong arXiv preprint arXiv:2110.03155, 2021 | 4* | 2021 |
An adaptive model checking test for functional linear model E Shi, Y Liu, K Sun, L Li, L Kong arXiv preprint arXiv:2204.01831, 2022 | 2 | 2022 |
Pareto Adversarial Robustness: Balancing Spatial Robustness and Sensitivity-based Robustness K Sun, M Li, Z Lin SCIENCE CHINA Information Sciences, 2021 | 2 | 2021 |
Patch-level neighborhood interpolation: A general and effective graph-based regularization strategy K Sun, B Yu, Z Lin, Z Zhu Asian Conference on Machine Learning (ACML) 2023, 2019 | 2* | 2019 |
A Simple Unified Framework for Anomaly Detection in Deep Reinforcement Learning H Zhang, K Sun, B Xu, L Kong, M Müller arXiv preprint arXiv:2109.09889, 2021 | 1 | 2021 |
Optimal Treatment Allocation Strategies for A/B Testing in Partially Observable Time Series Experiments K Sun, L Kong, H Zhu, C Shi arXiv preprint arXiv:2408.05342, 2024 | | 2024 |
Tracking full posterior in online Bayesian classification learning: a particle filter approach E Shi, J Xie, S Hu, K Sun, H Dai, B Jiang, L Kong, L Li Journal of Nonparametric Statistics, 1-19, 2024 | | 2024 |
Reweighted Bellman Targets for Continual Reinforcement Learning K Sun, J Jin, X Chen, W Liu, L Kong ICML 2024 Workshop: Aligning Reinforcement Learning Experimentalists and …, 2024 | | 2024 |
Classify and Generate Reciprocally: Simultaneous Positive-Unlabelled Learning and Conditional Generation with Extra Data B Yu, K Sun, H Wang, Z Lin, Z Zhu arXiv preprint arXiv:2006.07841, 2020 | | 2020 |