[4] Hongming Zhang*, Ke Sun*, Bo Xu, Linglong Kong, Martin Müller. A Distance-based Anomaly Detection Framework in Deep Reinforcement Learning. Transactions on Machine Learning Research (TMLR), 2024
[3] Enze Shi, Yi Liu, Ke Sun, Lingzhu Li, Linglong Kong. An Adaptive Model Checking Test for Functional Linear Model. Bernoulli. 2024.
[2] Enze Shi, Jinhan Xie, Shenggang Hu, Ke Sun, Hongsheng Dai, Bei Jiang, Linglong Kong, Lingzhu Li. Tracking full posterior in online Bayesian classification learning: a particle filter approach. Journal of Nonparametric Statistics. 2024
[1] Ke Sun, Mingjie Li, Zhouchen Lin. Pareto Adversarial Robustness: Balancing Spatial Robustness and Sensitivity-based Robustness. SCIENCE CHINA Information Sciences (SCIS, CCF-A), 2023
[12] Ke Sun, Yingnan Zhao, Enze Shi, Yafei Wang, Xiaodong Yan, Bei Jiang, Linglong Kong. Intrinsic Benefits of Being Categorical Distributional: Uncertainty-aware Regularized Exploration in Reinforcement Learning. Advances in Neural Information Processing Systems (NeurIPS), 2025; Preliminary Version Accepted in ICML Workshop: Exploration in AI Today (EXAIT), 2025
[11] Bing Yu*, Ke Sun*, He Wang, Zhouchen Lin, Zhanxing Zhu. On Leveraging Unlabeled Data for Concurrent Positive-Unlabeled Classification and Robust Generation. International Conference on Image and Graphics (ICIG), 2025
[10] Ke Sun, Yingnan Zhao, Wulong Liu, Bei Jiang, Linglong Kong. Distributional Reinforcement Learning with Regularized Wasserstein Loss [code]. Advances in Neural Information Processing Systems (NeurIPS), 2024
[9] Ke Sun, Jun Jin, Xi Chen, Wulong Liu, Linglong Kong. Reweighted Bellman Targets for Continual Reinforcement Learning. ICML Workshop: Aligning Reinforcement Learning Experimentalists and Theorists, 2024
[8] Ke Sun, Bei Jiang, Linglong Kong. How Does Return Distribution in Distributional Reinforcement Learning Help Optimization? ICML Workshop: Aligning Reinforcement Learning Experimentalists and Theorists, 2024
[7] Ke Sun*, Bing Yu*, Zhouchen Lin, Zhanxing Zhu. Patch-level Neighborhood Interpolation: A General and Effective Graph-based Regularization Strategy. Asian Conference on Machine Learning (ACML), 2023
[6] Ke Sun, Yingnan Zhao, Shangling Jui, Linglong Kong. Exploring the Training Robustness of Distributional Reinforcement Learning against Noisy State Observations [code]. European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML-PKDD), 2023.
[5] Yi Liu, Ke Sun, Bei Jiang, Linglong Kong. Identification, Amplification and Measurement: A bridge to Gaussian Differential Privacy. Advances in Neural Information Processing Systems (NeurIPS), 2022.
[4] Ke Sun*, Yafei Wang*, Yi Liu, Yingnan Zhao, Bo Pan, Shangling Jui, Bei Jiang, Linglong Kong. (*equal contribution in alphabetical order). Damped Anderson Mixing for Deep Reinforcement Learning: Acceleration, Convergence, and Stabilization [code]. Advances in Neural Information Processing Systems (NeurIPS), 2021.
[3] Ke Sun, Zhanxing Zhu, Zhouchen Lin. AdaGCN: AdaBoosting Graph Convolutional Networks into Deep Models [code]. International Conference on Learning Representations (ICLR), 2021.
[2] Ke Sun, Zhouchen Lin, Zhanxing Zhu. Multi-Stage Self-Supervised Learning for Graph Convolutional Networks on Graphs with Few Labels [code]. Association for the Advancement of Artificial Intelligence (AAAI), 2020.
[1] Ke Sun, Zhouchen Lin, Hantao Guo, Zhanxing Zhu. Virtual Adversarial Training on Graph Convolutional Networks in Node Classification. Chinese Conference on Pattern Recognition and Computer Vision (PRCV) (oral presentation), 2019.
(* denotes equal contribution)
[1] Ke Sun. Advances in Distributional Reinforcement Learning: Bridging Theory with Algorithmic Practice. University of Alberta. 2024.
[1] Ke Sun, Zhanxing Zhu, Zhouchen Lin. Towards Understanding Adversarial Examples Systematically: Exploring Data Size, Task and Model Factors. arxiv. 2019.
[2] Ke Sun, Zhanxing Zhu, Zhouchen Lin. Enhancing the Robustness of Deep Neural Networks by Boundary Conditional GAN. arXiv. 2019.