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