Jun Yang

Pronunciation: Jùn [tɕ]‑[u̯ən] Yáng [j]‑[ɑŋ]

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.

Drift, Minorization, and Hitting Times
(with Robert M. Anderson, Haosui Duanmu, and Aaron Smith)
| arXiv:1910.05904
Fast-Rate PAC-Bayes Generalization Bounds via Shifted Rademacher Processes
Jun Yang*, Shengyang Sun*, and Daniel M. Roy
in Advances in Neural Information Processing Systems (NeurIPS), 2019.
| arXiv:1908.07585
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
A Bayesian Decision-Theoretic Analysis of Bayesian Inference under Model Misspecification
Jun Yang and Daniel M. Roy
| earlier slides
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