Publications

You can also find my articles on my Google Scholar profile.

∗ for equal contribution

2025

• Self-Consistent Model-based Adaptation for Visual Reinforcement Learning
  Xinning Zhou^∗, Chengyang Ying^∗, Yao Feng, Hang Su, Jun Zhu
  International Joint Conference on Artificial Intelligence (IJCAI), Montreal, Canada, 2025
  [arXiv]

2024

• PEAC: Unsupervised Pre-training for Cross-Embodiment Reinforcement Learning
  Chengyang Ying, Zhongkai Hao, Xinning Zhou, Xuezhou Xu, Hang Su, Xingxing Zhang, Jun Zhu
  Neural Information Processing Systems (NeurIPS), Vancouver, Canada, 2024
  [project page] [arXiv] [code] [video]

• Fourier Controller Networks for Real-Time Decision-Making in Embodied Learning
  Hengkai Tan, Songming Liu, Kai Ma, Chengyang Ying, Xingxing Zhang, Hang Su, Jun Zhu
  International Conference on Machine Learning (ICML), Vienna, Austria, 2024
  [project page] [arXiv] [code]

• DPOT: Auto-Regressive Denoising Operator Transformer for Large-Scale PDE Pre-Training
  Zhongkai Hao, Chang Su, Songming Liu, Julius Berner, Chengyang Ying, Hang Su, Anima Anandkumar, Jian Song, Jun Zhu
  International Conference on Machine Learning (ICML), Vienna, Austria, 2024
  [arXiv] [code]

• Understanding Adversarial Attacks on Observations in Deep Reinforcement Learning
  You Qiaoben, Chengyang Ying, Xinning Zhou, Hang Su, Jun Zhu, Bo Zhang
  SCIENCE CHINA Information Sciences (SCIS), 2024
  [arXiv] [code]

2023

• On the Reuse Bias in Off-Policy Reinforcement Learning
  Chengyang Ying, Zhongkai Hao, Xinning Zhou, Hang Su, Dong Yan, Jun Zhu
  International Joint Conference on Artificial Intelligence (IJCAI), Macao, China, 2023
  [arXiv] [code] [slide]

• GNOT: A General Neural Operator Transformer for Operator Learning
  Zhongkai Hao, Zhengyi Wang, Hang Su, Chengyang Ying, Yinpeng Dong, Songming Liu, Ze Cheng, Jun Zhu, Jian Song
  International Conference on Machine Learning (ICML), Hawaii, USA, 2023
  [arXiv] [code]

• NUNO: A General Framework for Learning Parametric PDEs with Non-Uniform Data
  Songming Liu, Zhongkai Hao, Chengyang Ying, Hang Su, Ze Cheng, Jun Zhu
  International Conference on Machine Learning (ICML), Hawaii, USA, 2023
  [arXiv] [code]

• Consistent Attack: Universal Adversarial Perturbation on Embodied Vision Navigation
  Chengyang Ying^∗, You Qiaoben^∗, Xinning Zhou^∗, Hang Su, Wenbo Ding, Jianyong Ai
  Pattern Recognition Letters (PRL), 2023
  [arXiv] [code]

• Offline Reinforcement Learning via High-Fidelity Generative Behavior Modeling
  Huayu Chen, Cheng Lu, Chengyang Ying, Hang Su, Jun Zhu
  International Conference on Learning Representations (ICLR), Kigali, Rwanda, 2023
  [arXiv] [code]

• Bi-level Physics-Informed Neural Networks for PDE Constrained Optimization using Broyden’s Hypergradients
  Zhongkai Hao, Chengyang Ying, Hang Su, Jun Zhu, Jian Song, Ze Cheng
  International Conference on Learning Representations (ICLR), Kigali, Rwanda, 2023
  [arXiv] [code]

2022

• A Unified Hard-Constraint Framework for Solving Geometrically Complex PDEs
  Songming Liu, Zhongkai Hao, Chengyang Ying, Hang Su, Jun Zhu, Ze Cheng
  Neural Information Processing Systems (NeurIPS), New Orleans, USA, 2022
  [arXiv] [code]

• GSmooth: Certified Robustness against Semantic Transformations via Generalized Randomized Smoothing
  Zhongkai Hao, Chengyang Ying, Yinpeng Dong, Hang Su, Jian Song, Jun Zhu
  International Conference on Machine Learning (ICML), Baltimore, USA, 2022
  [arXiv]

• Towards Safe Reinforcement Learning via Constraining Conditional Value at Risk
  Chengyang Ying, Xinning Zhou, Hang Su, Dong Yan, Ning Chen, Jun Zhu
  International Joint Conference on Artificial Intelligence (IJCAI), Vienna, Austria, 2022
  [arXiv] [code] [slide] [ICML 2021 AML Workshop]

Worshop Papers & Preprints

• Exploratory Diffusion Policy for Unsupervised Reinforcement Learning
  Chengyang Ying, Huayu Chen, Xinning Zhou, Zhongkai Hao, Hang Su, Jun Zhu
  [arXiv]

• Task Aware Dreamer for Task Generalization in Reinforcement Learning
  Chengyang Ying^∗, Xinning Zhou^∗, Zhongkai Hao, Hang Su, Songming Liu, Dong Yan, Jun Zhu
  [arXiv]

• Physics-Informed Machine Learning: A Survey on Problems, Methods and Applications
  Zhongkai Hao, Songming Liu, Yichi Zhang, Chengyang Ying, Yao Feng, Hang Su, Jun Zhu
  [arXiv]

• Strategically-timed State-Observation Attacks on Deep Reinforcement Learning Agents
  You Qiaoben, Xinning Zhou, Chengyang Ying, Jun Zhu
  ICML 2021 Workshop on Adversarial Machine Learning, 2021