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 forcing on statistical transfer learning, functional/longitudinal data and differential privacy with kernel methods. Currently, I’m interested in leveraging kernel methods to theoretically address learning problems with different types of distribution shifts.

Curriculum Vitae

Google Scholar

Preprints

  • Spectral Algorithms with Gaussian Kernels: Robustness and Optimality.
    Haotian Lin and Matthew Reimherr
    (Under Review)

Publication

  • Pure Differential Privacy for Functional Summaries via a Laplce-like Process.
    Haotian Lin and Matthew Reimherr
    To appear in Journal of Machine Learning Research (JMLR), 2024
    [arXiv]

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

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