About Me

I am a PhD student in Department of Statistics, The Pennsylvania State University. I am fortunate to be advised by Professor Matthew Reimherr. Prior to this, I received my Bachelor degree in Statistics from University of Science and Technology of China.

My research interests are mainly focusing on statistical transfer learning, functional/longitudinal data and differential privacy with kernel methods. Currently, I’m interested in leveraging kernel methods to address learning problems with different types of distribution shifts.

Curriculum Vitae

Google Scholar

Publications and Preprints

(* Equal Contribution)

  • Co-Regularization for Multi-Source Knowledge Transfer in High Dimensions.
    Shuo-Shuo Liu*, Haotian Lin*, Matthew Reimherr, and Runze Li
    (Submitted)

  • Model-Robust and Adaptive-Optimal Transfer Learning for Tackling Concept Shifts in Nonparametric Regression.
    Haotian Lin and Matthew Reimherr
    (Submitted)
    [arXiv:2501.10870]

  • Pure Differential Privacy for Functional Summaries with a Laplace-like Process.
    Haotian Lin and Matthew Reimherr
    Journal of Machine Learning Research (JMLR), 2024
    [Paper] [arXiv:2309.00125]

  • Smoothness Adaptive Hypothesis Transfer Learning.
    Haotian Lin and Matthew Reimherr
    International Conference on Machine Learning (ICML), 2024
    [Paper] [arXiv:2402.14966]

  • On Hypothesis Transfer Learning in Functional Linear Models.
    Haotian Lin and Matthew Reimherr
    International Conference on Machine Learning (ICML), 2024
    [Paper] [arXiv:2206.04277] [Code]