Virtual Adversarial Training on Graph Convolutional Networks in Node Classification. - Ke Sun 013; Zhouchen Lin; Hantao Guo; Zhanxing Zhu (PRCV (1), 2019)

Towards Understanding Adversarial Examples Systematically: Exploring Data Size, Task and Model Factors. - Ke Sun 013; Zhanxing Zhu; Zhouchen Lin (CoRR, 2019)

Enhancing the Robustness of Deep Neural Networks by Boundary Conditional GAN. - Ke Sun 013; Zhanxing Zhu; Zhouchen Lin (CoRR, 2019)

Multi-Stage Self-Supervised Learning for Graph Convolutional Networks. - Ke Sun 013; Zhanxing Zhu; Zhouchen Lin (CoRR, 2019)

Multi-Stage Self-Supervised Learning for Graph Convolutional Networks on Graphs with Few Labeled Nodes. - Ke Sun 013; Zhouchen Lin; Zhanxing Zhu (AAAI, 2020)

Classify and Generate Reciprocally: Simultaneous Positive-Unlabelled Learning and Conditional Generation with Extra Data. - Bing Yu; Ke Sun 013; He Wang 002; Zhouchen Lin; Zhanxing Zhu (CoRR, 2020)

AdaGCN: Adaboosting Graph Convolutional Networks into Deep Models. - Ke Sun 013; Zhanxing Zhu; Zhouchen Lin (ICLR, 2021)

Damped Anderson Mixing for Deep Reinforcement Learning: Acceleration, Convergence, and Stabilization. - Ke Sun 013; Yafei Wang; Yi Liu 062; Yingnan Zhao; Bo Pan; Shangling Jui; Bei Jiang; Linglong Kong (NeurIPS, 2021)

Exploring the Robustness of Distributional Reinforcement Learning against Noisy State Observations. - Ke Sun 013; Yi Liu 062; Yingnan Zhao; Hengshuai Yao; Shangling Jui; Linglong Kong (CoRR, 2021)

A Simple Unified Framework for Anomaly Detection in Deep Reinforcement Learning. - Hongming Zhang 003; Ke Sun 013; Bo Xu 002; Linglong Kong; Martin Müller 003 (CoRR, 2021)

Towards Understanding Distributional Reinforcement Learning: Regularization, Optimization, Acceleration and Sinkhorn Algorithm. - Ke Sun 013; Yingnan Zhao; Yi Liu 062; Enze Shi; Yafei Wang; Aref Sadeghi; Xiaodong Yan; Bei Jiang; Linglong Kong (CoRR, 2021)

Pareto Adversarial Robustness: Balancing Spatial Robustness and Sensitivity-based Robustness. - Ke Sun 013; Mingjie Li 007; Zhouchen Lin (CoRR, 2021)

Identification, Amplification and Measurement: A bridge to Gaussian Differential Privacy. - Yi Liu 062; Ke Sun 013; Bei Jiang; Linglong Kong (NeurIPS, 2022)

Distributional Reinforcement Learning via Sinkhorn Iterations. - Ke Sun 013; Yingnan Zhao; Yi Liu 062; Bei Jiang; Linglong Kong (CoRR, 2022)

How Does Value Distribution in Distributional Reinforcement Learning Help Optimization? - Ke Sun 013; Bei Jiang; Linglong Kong (CoRR, 2022)

Patch-level Neighborhood Interpolation: A General and Effective Graph-based Regularization Strategy. - Ke Sun 013; Bing Yu; Zhouchen Lin; Zhanxing Zhu (ACML, 2023)

Exploring the Training Robustness of Distributional Reinforcement Learning Against Noisy State Observations. - Ke Sun 013; Yingnan Zhao; Shangling Jui; Linglong Kong (ECML/PKDD (5), 2023)

Mathematical Challenges in Deep Learning. - Vahid Partovi Nia; Guojun Zhang; Ivan Kobyzev; Michael R. Metel; Xinlin Li; Ke Sun 013; Sobhan Hemati; Masoud Asgharian; Linglong Kong; Wulong Liu; Boxing Chen (CoRR, 2023)

Distributional Reinforcement Learning with Regularized Wasserstein Loss. - Ke Sun 013; Yingnan Zhao; Wulong Liu; Bei Jiang; Linglong Kong (NeurIPS, 2024)

A Distance-based Anomaly Detection Framework for Deep Reinforcement Learning. - Hongming Zhang 003; Ke Sun 013; Bo Xu 002; Linglong Kong; Martin Müller 003 (Trans. Mach. Learn. Res., 2024)