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   Yao-Xiang Ding   丁尧相


     Assistant Professor


       State Key Lab of CAD & CG
       College of Computer Science & Technology
       Zhejiang University

      Email: dingyx.gm at gmail.com (preferred), yxding at zju.edu.cn
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Wir müssen wissen. Wir werden wissen. 憧憬光明，就不会惧怕黑暗。

Biography

I am a tenure-track assistant professor in the State Key Lab of CAD & CG and College of Computer Science & Technology, Zhejiang University, as a member of the GAPS group led by Prof. Kun Zhou. I received Ph.D. degree in computer science from LAMDA, Nanjing University in 2020, advised by Prof. Zhi-Hua Zhou. Before that, I received M.Sc. degree in computer science from Peking University in 2014 and B.Sc. degree in mechanical engineering from University of Science and Technology Beijing in 2011.

Research Interests

I work on building and understanding intelligent heuristics in the field of machine learning, with special interests on the following topics:

• neurosymbolic visual understanding, generation, and decision making: bridging symbolic (logical, causal) inference and neural network learning to jointly solve system-1 and system-2 visual learning problems.
• active learning, machine teaching, and pretrained model reuse: understanding how prior information can help reduce the sample complexity.

Our small research subgroup is named "future-item". [GitHub]
• For prospective students who are interested to work with me, please read this note first.

Research Papers

Preprints:

• Jingming Liu, Yumeng Li, Wei Shi, Yao-Xiang Ding, Hui Su, Kun Zhou, "Harnessing the Power of Reinforcement Learning for Language-Model-Based Information Retriever via Query-Document Co-Augmentation", arxiv preprint 2506.18670.

• Yumeng Li, Yao-Xiang Ding, Zhong Ren, Kun Zhou, "QPoser: Quantized Explicit Pose Prior Modeling for Controllable Pose Generation", arxiv preprint 2312.01104.

Conference Papers:

• Yifei Peng, Zijie Zha, Yu Jin, Zhexu Luo, Wang-Zhou Dai, Zhong Ren, Yao-Xiang Ding, Kun Zhou, "Generating by Understanding: Neural Visual Generation with Logical Symbol Groundings", KDD (2025 Feburary round research track paper), 2025. [arXiv]

• Bohong Chen, Yumeng Li, Youyi Zheng, Yao-Xiang Ding, Kun Zhou, "Motion-example-controlled Co-speech Gesture Generation Leveraging Large Language Models", SIGGRAPH, 2025.

• Qing Chang, Yao-Xiang Ding, Kun Zhou, "Enhancing Identity-Deformation Disentanglement in StyleGAN for One-Shot Face Video Re-Enactment", AAAI, 2025.

• Lanjihong Ma, Yao-Xiang Ding, Zhen-Yu Zhang, Zhi-Hua Zhou, "Achieving Nearly-Optimal Regret and Sample Complexity in Dueling Bandits with Applications in Online Recommendations", KDD (2024 August round research track paper), 2025.

• Yu Jin, Jingming Liu, Zhexu Luo, Yifei Peng, Ziang Qin, Wang-Zhou Dai, Yao-Xiang Ding, Kun Zhou, "Pre-Training Meta-Rule Selection Policy for Visual Generative Abductive Learning", (conference track long paper), International Joint Conference on Learning and Reasoning (IJCLR), 2024. [arXiv]

• Bohong Chen, Yumeng Li, Yao-Xiang Ding, Tianjia Shao, Kun Zhou, "Enabling Synergistic Full-Body Control in Prompt-Based Co-Speech Motion Generation", ACM Multimedia, 2024.

• Lanjihong Ma, Zhen-Yu Zhang, Yao-Xiang Ding, Zhi-Hua Zhou, "Handling Varied Objectives by Online Decision Making", KDD (2024 Feburary round research track paper), 2024.

• Yu-Cheng He, Yao-Xiang Ding, Han-Jia Ye, Zhi-Hua Zhou, "Learning Only When It Matters: Cost-Aware Long-Tailed Classification", AAAI, 2024.

• Yi-Kai Zhang, Ting-Ji Huang, Yao-Xiang Ding, De-Chuan Zhan, Han-Jia Ye, "Model Spider: Learning to Rank Pre-Trained Models Efficiently", NeurIPS (spotlight paper), 2023.

• Xin-Qiang Cai, Yao-Xiang Ding, Zi-Xuan Chen, Yuan Jiang, Masashi Sugiyama, Zhi-Hua Zhou, "Seeing Differently, Acting Similarly: Heterogeneously Observable Imitation Learning", ICLR (spotlight paper), 2023.

• Yao-Xiang Ding, Xi-Zhu Wu, Kun Zhou, Zhi-Hua Zhou, "Pre-Trained Model Reusability Evaluation for Small-Data Transfer Learning", NeurIPS, 2022.

• Xin-Qiang Cai, Yao-Xiang Ding, Yuan Jiang, Zhi-Hua Zhou, "Imitation Learning from Pixel-Level Demonstrations by HashReward", AAMAS (long paper), 2021.

• Yao-Xiang Ding, Zhi-Hua Zhou, "Boosting-Based Reliable Model Reuse", ACML, 2020.

• Yao-Xiang Ding, Zhi-Hua Zhou, "Preference Based Adaptation for Learning Objectives", NeurIPS, 2018.

• Dingsheng Luo, Xiaoqiang Han, Yaoxiang Ding, Yang Ma, Zhan Liu, Xihong Wu, "Learning Push Recovery for a Bipedal Humanoid Robot with Dynamical Movement Primitives", IEEE Humanoids, 2015.

• Dingsheng Luo, Yaoxiang Ding, Zidong Cao, Xihong Wu, "A Multi-stage Approach for Efficiently Learning Humanoid Robot Stand-up Behavior", IEEE ICMA, 2014.

Journal Articles:

• Jingming Liu, Yumeng Li, Boyuan Xiao, Yichang Jian, Ziang Qin, Tianjia Shao, Yao-Xiang Ding, Kun Zhou, "Autonomous Imagination: Closed-Loop Decomposition of Visual-to-Textual Conversion in Visual Reasoning for Multimodal Large Language Models", Transactions on Machine Learning Research (TMLR), 2025. [arXiv] [Project Page]

• Zipei Chen, Yumeng Li, Zhong Ren, Yao-Xiang Ding, Kun Zhou, "Appearance as Reliable Evidence: Reconciling Appearance and Generative Priors for Monocular Motion Estimation", Computers & Graphics (CAD/Graphics 2025 conference best paper award), 2025.

• Yuting Tang, Xin-Qiang Cai, Yao-Xiang Ding, Qiyu Wu, Guoqing Liu, Masashi Sugiyama, "Reinforcement Learning from Bagged Reward", Transactions on Machine Learning Research (TMLR), 2025. [arXiv]

• Lanjihong Ma, Yao-Xiang Ding, Peng Zhao, Zhi-Hua Zhou, "Learning Objective Adaptation by Correlation-based Model Reuse", IEEE Transactions on Neural Networks and Learning Systems (TNNLS), 2025.

• Yumeng Li, Bohong Chen, Zhong Ren, Yao-Xiang Ding, Libin Liu, Tianjia Shao, Kun Zhou, "CPoser: An Optimization-after-Parsing Approach for Text-to-Pose Generation Using Large Language Models", ACM Transactions on Graphics (TOG) (SIGGRAPH Asia journal track paper), 2024.

• Yu-Cheng He, Yao-Xiang Ding, Zhi-Hua Zhou, "Mechanism Design for Requester-Platform Strategies Under the Three-Party Crowdsourcing Market", Journal of Computer Research And Development (in Chinese, CCFAI 2021 conference best paper award), 2021.

• Yao-Xiang Ding, Zhi-Hua Zhou, "Crowdsourcing with Unsure Option", Machine Learning Journal, 2018.

• Dingsheng Luo, Yaoxiang Ding, Xiaoqiang Han, Yang Ma, Yian Deng, Zhan Liu, Xihong Wu, "Humanoid Environmental Perception with Gaussian Process Regression", International Journal of Advanced Robotic Systems, 2016.

Activities

• Model Reuse: Theories and Applications, Tutorial on AAAI'24, co-organized with Dr. Han-Jia Ye. [Slides of part1][Slides of part2]

Courses

• Advanced Data Structures and Algorithm Analysis, Undergraduate, Zhejiang University. Latest version: [Fall and Winter 2025]

• Introduction to Machine Learning (co-teach with Prof. Yingchun Yang, Dr. Qian Zheng and Dr. Chaochao Chen), Graduate, Spring 2023, Zhejiang University. [Slides of Lecture 7][Slides of Lecture 8]

• Introduction to Artificial Intelligence, Undergraduate, Fall and Winter 2022, Zhejiang University.

Students

Current Students:

• Yifei Peng, PhD, ZJU.
• Zijie Zha, PhD, ZJU.
• Boyuan Xiao, PhD, ZJU.
• Ziang Qin, Master, ZJU.
• Jingming Liu, Master, ZJU.
• Junhua Shen, Master, ZJU.
• Enbo Xia, Master, ZJU.
• Yaoli Liu, Master, ZJU.
• Yichang Jian, Undergraduate, ZJU.
• Pu Wang, Undergraduate, ZJU.

Alumni:

• Yu Jin, Master, ZJU. (Graduate destination: SPD Bank).
• Yi Jiang, Undergraduate, ZJU. (Graduate destination: PhD student, ZJU).
• Libin Sun, Undergraduate, ZJU. (Graduate destination: master student, NYU).
• Zhexu Luo, Undergraduate, CUHK (Shenzhen). (Graduate destination: master student, Penn).
Latest update: 2025.9.16.