Ke Sun

According to our database, Ke Sun authored at least 17 papers between 2019 and 2023.

Collaborative distances:

• Dijkstra number of five.
• Erdős number of four.

2023

Mathematical Challenges in Deep Learning.

Vahid Partovi Nia, Guojun Zhang, Ivan Kobyzev, Michael R. Metel, Xinlin Li, Ke Sun, Sobhan Hemati, Masoud Asgharian, Linglong Kong, Wulong Liu, Boxing Chen

CoRR, 2023

Exploring the Training Robustness of Distributional Reinforcement Learning Against Noisy State Observations.

Ke Sun, Yingnan Zhao, Shangling Jui, Linglong Kong

Proceedings of the Machine Learning and Knowledge Discovery in Databases: Research Track, 2023

2022

How Does Value Distribution in Distributional Reinforcement Learning Help Optimization?

Ke Sun, Bei Jiang, Linglong Kong

CoRR, 2022

Distributional Reinforcement Learning via Sinkhorn Iterations.

Ke Sun, Yingnan Zhao, Yi Liu, Bei Jiang, Linglong Kong

CoRR, 2022

Identification, Amplification and Measurement: A bridge to Gaussian Differential Privacy.

Yi Liu, Ke Sun, Bei Jiang, Linglong Kong

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

2021

Towards Understanding Distributional Reinforcement Learning: Regularization, Optimization, Acceleration and Sinkhorn Algorithm.

Ke Sun, Yingnan Zhao, Yi Liu, Enze Shi, Yafei Wang, Aref Sadeghi, Xiaodong Yan, Bei Jiang, Linglong Kong

CoRR, 2021

A Simple Unified Framework for Anomaly Detection in Deep Reinforcement Learning.

Hongming Zhang, Ke Sun, Bo Xu, Linglong Kong, Martin Müller

CoRR, 2021

Exploring the Robustness of Distributional Reinforcement Learning against Noisy State Observations.

Ke Sun, Yi Liu, Yingnan Zhao, Hengshuai Yao, Shangling Jui, Linglong Kong

CoRR, 2021

Damped Anderson Mixing for Deep Reinforcement Learning: Acceleration, Convergence, and Stabilization.

Ke Sun, Yafei Wang, Yi Liu, Yingnan Zhao, Bo Pan, Shangling Jui, Bei Jiang, Linglong Kong

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

AdaGCN: Adaboosting Graph Convolutional Networks into Deep Models.

Ke Sun, Zhanxing Zhu, Zhouchen Lin

Proceedings of the 9th International Conference on Learning Representations, 2021

2020

Classify and Generate Reciprocally: Simultaneous Positive-Unlabelled Learning and Conditional Generation with Extra Data.

Bing Yu, Ke Sun, He Wang, Zhouchen Lin, Zhanxing Zhu

CoRR, 2020

Multi-Stage Self-Supervised Learning for Graph Convolutional Networks on Graphs with Few Labeled Nodes.

Ke Sun, Zhouchen Lin, Zhanxing Zhu

Proceedings of the Thirty-Fourth AAAI Conference on Artificial Intelligence, 2020

2019

Patch-level Neighborhood Interpolation: A General and Effective Graph-based Regularization Strategy.

Ke Sun, Bing Yu, Zhouchen Lin, Zhanxing Zhu

CoRR, 2019

Multi-Stage Self-Supervised Learning for Graph Convolutional Networks.

Ke Sun, Zhanxing Zhu, Zhouchen Lin

CoRR, 2019

Enhancing the Robustness of Deep Neural Networks by Boundary Conditional GAN.

Ke Sun, Zhanxing Zhu, Zhouchen Lin

CoRR, 2019

Towards Understanding Adversarial Examples Systematically: Exploring Data Size, Task and Model Factors.

Ke Sun, Zhanxing Zhu, Zhouchen Lin

CoRR, 2019

Virtual Adversarial Training on Graph Convolutional Networks in Node Classification.

Ke Sun, Zhouchen Lin, Hantao Guo, Zhanxing Zhu

Proceedings of the Pattern Recognition and Computer Vision - Second Chinese Conference, 2019