ABOUT ME
I am a Ph.D Candidate in Statistics at University of Toronto, advised by Daniel M. Roy and Jeffrey S. Rosenthal. I also work closely with Zhou Zhou.
Before coming to Toronto, I received an M.Sc. in Applied Mathematics from Queen's University
and B.Eng. in Electronic Engineering from Tsinghua University.
I am affiliated with Vector Institute. I spent Spring 2017 at UC Berkeley, as a visiting student researcher at Simons Institute.
RESEARCH
My current research interests include statistical learning theory, computational statistics, Bayesian inference, high-dimensional statistics, and applied probability.
Recent work:
- Fast PAC-Bayes via Shifted Rademacher Complexity
Jun Yang*, Shengyang Sun*, and Daniel M. Roy
- A Bayesian Decision-Theoretic Analysis of Bayesian Inference under Model Misspecification
Jun Yang and Daniel M. Roy
| earlier slides
- Optimal Scaling of Metropolis Algorithms on General Target Distributions
Jun Yang, Gareth O. Roberts, and Jeffrey S. Rosenthal
| arXiv:1904.12157
- State-domain Change Point Detection for Nonlinear Time Series Regression
(with Yan Cui and Zhou Zhou)
| arXiv:1904.11075
- Spectral Inference under Complex Temporal Dynamics
Jun Yang and Zhou Zhou
| arXiv:1812.07706
- Complexity Results for MCMC derived from Quantitative Bounds
Jun Yang and Jeffrey S. Rosenthal
| arXiv:1708.00829
- On Bounding the Union Probability using Partial Weighted Information
Jun Yang, Fady Alajaji, and Glen Takahara
Statistics and Probability Letters, 116, 38–44, 2016.
| arXiv:1506.08331
- Lower Bounds on the Probability of a Finite Union of Events
Jun Yang, Fady Alajaji, and Glen Takahara
SIAM Journal on Discrete Mathematics, 30(3), 1437–1452, 2016.
| arXiv:1401.5543
Link to the full list of preprints and publications.
OTHERS
Activities, teaching, non-work page (under construction).
