Portrait of Archer Yang

Archer Yang

Associate Academic Member
Associate professor, McGill University, Department of Mathematics and Statistics


Archer Yang is an associate professor in the Department of Mathematics and Statistics at McGill University, as well as an associate academic member of McGill’s School of Computer Science and the Quantitative Life Sciences program. His main research areas are statistical machine learning, statistical computing, high-dimensional statistics, causal inference and optimization, along with applications in biomedical data science.

Current Students

PhD - McGill University
PhD - McGill University
Co-supervisor :


Structured Learning in Time-dependent Cox Models
Guanbo Wang
Yi Lian
Robert W. Platt
Rui Wang
Sylvie Perreault
Marc Dorais
Mireille E. Schnitzer
Machine Learning Informed Diagnosis for Congenital Heart Disease in Large Claims Data Source
Ariane Marelli
Chao Li
Aihua Liu
Hanh Nguyen
Harry Moroz
James M. Brophy
Liming Guo
Privacy-preserving analysis of time-to-event data under nested case-control sampling
Lamin Juwara
Ana M Velly
Paramita Saha-Chaudhuri
Accelerating Generalized Random Forests with Fixed-Point Trees
David L. Fleischer
David A. Stephens
A Tweedie Compound Poisson Model in Reproducing Kernel Hilbert Space
Yi Lian
Boxiang Wang
Peng Shi
Robert William Platt
Abstract Tweedie models can be used to analyze nonnegative continuous data with a probability mass at zero. There have been wide application… (see more)s in natural science, healthcare research, actuarial science, and other fields. The performance of existing Tweedie models can be limited on today’s complex data problems with challenging characteristics such as nonlinear effects, high-order interactions, high-dimensionality and sparsity. In this article, we propose a kernel Tweedie model, Ktweedie, and its sparse variant, SKtweedie, that can simultaneously address the above challenges. Specifically, nonlinear effects and high-order interactions can be flexibly represented through a wide range of kernel functions, which is fully learned from the data; In addition, while the Ktweedie can handle high-dimensional data, the SKtweedie with integrated variable selection can further improve the interpretability. We perform extensive simulation studies to justify the prediction and variable selection accuracy of our method, and demonstrate the applications in ratemaking and loss-reserving in general insurance. Overall, the Ktweedie and SKtweedie outperform existing Tweedie models when there exist nonlinear effects and high-order interactions, particularly when the dimensionality is high relative to the sample size. The model is implemented in an efficient and user-friendly R package ktweedie (https://cran.r-project.org/package=ktweedie).