Tianyang Hu

Assistant Professor, School of Data Science, The Chinese University of Hong Kong, Shenzhen.

sufe.jpg

Photo taken at SUFE in 2023. Special thanks to Yixuan and Taiyun :)

I was previously a Postdoctoral Fellow at the National University of Singapore, working with Prof. Kenji Kawaguchi, and a Research Scientist in the AI Theory Group of Huawei Noah’s Ark Lab. I received my Ph.D. in Statistics from Purdue University under the supervision of Prof. Guang Cheng, after earning an M.S. in Statistics from the University of Chicago and a B.S. in Mathematics from Tsinghua University.

My research focuses on the intersection of Statistics and AI, aiming to deepen the theoretical foundations of AI and design theory-inspired algorithms. I am particularly interested in:

  • Statistical Machine Learning
  • Representation Learning
  • Deep Generative Modeling

I am actively seeking PhD/MPhil students and research assistants with a strong theoretical foundation and hands-on experience in AI to join my group. If you are interested, please send me an email with your CV.

selected publications

  1. JMLR
    Minimax Optimal Deep Neural Network Classifiers Under Smooth Decision Boundary
    Tianyang Hu, Ruiqi Liu, Zuofeng Shang, and 1 more author
    Journal of Machine Learning Research, To appear, 2025
    arXiv
  2. ICML
    Exact Conversion of In-Context Learning to Model Weights in Linearized-Attention Transformers
    Brian Chen, Tianyang Hu, Hui Jin, and 2 more authors
    International Conference on Machine Learning, 2024
    arXiv
  3. ICML
    Referee Can Play: An Alternative Approach to Conditional Generation via Model Inversion
    Xuantong Liu, Tianyang Hu, Wenjia Wang, and 2 more authors
    International Conference on Machine Learning, 2024
    arXiv
  4. NeurIPSSpotlight
    Complexity Matters: Rethinking the Latent Space for Generative Modeling
    Tianyang Hu, Fei Chen, Haonan Wang, and 4 more authors
    Advances in Neural Information Processing Systems, 2023
    arXiv
  5. NeurIPS
    Diff-Instruct: A Universal Approach for Transferring Knowledge From Pre-trained Diffusion Models
    Weijian Luo, Tianyang Hu, Shifeng Zhang, and 3 more authors
    Advances in Neural Information Processing Systems, 2023
    arXiv
  6. JMLR
    Random Smoothing Regularization in Kernel Gradient Descent Learning
    (**)Liang Ding, Tianyang Hu, Jiahang Jiang, and 3 more authors
    Journal of Machine Learning Research, 25(284), 2024
    arXiv
  7. ICLR
    Your Contrastive Learning Is Secretly Doing Stochastic Neighbor Embedding
    Tianyang Hu, Zhili Liu, Fengwei Zhou, and 2 more authors
    International Conference on Learning Representations, 2023
    arXiv
  8. NeurIPSSpotlight
    Understanding Square Loss in Training Overparametrized Neural Network Classifiers
    Tianyang Hu*, Jun Wang*, Wenjia Wang*, and 1 more author
    Advances in Neural Information Processing Systems, 2022
    arXiv
  9. AISTATS
    Regularization Matters: A Nonparametric Perspective on Overparametrized Neural Network
    Tianyang Hu*, Wenjia Wang*, Cong Lin, and 1 more author
    In International Conference on Artificial Intelligence and Statistics, 2021
    arXiv