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

Preprints

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

Publication

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

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

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