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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 lab 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 theories and algorithms of machine learning, with special interests on the following topics:

• active learning and machine teaching: understanding how prior information can help reduce the sample complexity.
• neurosymbolic generative learning: bridging symbolic (logical, causal) inference and neural network learning, with their applications in visual generation tasks.
• decision-making learning: imitation learning and model-based reinforcement learning.
• model reuse: reusing pre-trained models under decision-making and visual generation tasks.

Research Papers

• Yuting Tang, Xin-Qiang Cai, Yao-Xiang Ding, Qiyu Wu, Guoqing Liu, Masashi Sugiyama, "Reinforcement Learning from Bagged Reward: A Transformer-based Approach for Instance-Level Reward Redistribution", arxiv preprint 2402.03771.

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

• Yifei Peng, Yu Jin, Zhexu Luo, Yao-Xiang Ding, Wang-Zhou Dai, Zhong Ren, Kun Zhou, "Generating by Understanding: Neural Visual Generation with Logical Symbol Groundings", arxiv preprint 2310.17451.

• Yu Jin, Jingming Liu, Zhexu Luo, Yifei Peng, Ziang Qin, Yao-Xiang Ding, Wang-Zhou Dai, 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.

• 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", SIGGRAPH Asia (journal track paper, to appear in ACM Transactions on Graphics), 2024.

• 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 (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.

• 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'21 best paper award), 2021.

• 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.

• 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.

• 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.

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, Fall and Winter 2024, Zhejiang University. Previous versions: Spring and Summer 2024

• 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

• Yifei Peng, PhD student of Zhejiang University, 2022-.

• Zijie Zha, PhD student of Zhejiang University, 2024-.

• Boyuan Xiao, PhD student of Zhejiang University, 2024-.

• Yu Jin, Master student of Zhejiang University, 2022-.

• Ziang Qin, Master student of Zhejiang University, 2023-.

• Jingming Liu, Master student of Zhejiang University, 2023-.

• Junhua Shen, Master student of Zhejiang University, 2024-.

• Enbo Xia, Master student of Zhejiang University, 2024-.

• Yichang Jian, Undergraduate student of Zhejiang University, 2022-.

• Libin Sun, Undergraduate student of Zhejiang University, 2023-.

• Zhexu Luo, Undergraduate student of the Chinese University of Hong Kong, Shenzhen, 2022-2024 (Now: master student of University of Pennsylvania).

Academic Services

I am a regular program committee member of NeurIPS, ICML, ICLR, AAAI and IJCAI. I also serve as the reviewer/meta-reviewer for conferences like UAI, AISTATS, CVPR, ECML-PKDD, ECAI, CIKM, SDM, ICDM, ACML and PAKDD, as well as journals like IEEE TPAMI, IEEE TKDE, IEEE TKDD, MLJ and KAIS. I served as the publicity co-chair of SDM'23.
Latest update: 2024.9.9.