21 citations · 40 across the 10 of their papers we have counts for
10 papers
Tuning Stochastic Gradient Algorithms for Statistical Inference via Large-Sample Asymptotics
Jeffrey Negrea, Jun Yang, Haoyue Feng +2
The tuning of stochastic gradient algorithms (SGAs) for optimization and sampling is often based on heuristics and trial-and-error rather than generalizable theory. We address this…
Stereographic Markov Chain Monte Carlo
Jun Yang, Krzysztof Łatuszyński, Gareth O. Roberts
High-dimensional distributions, especially those with heavy tails, are notoriously difficult for off-the-shelf MCMC samplers: the combination of unbounded state spaces, diminishing…
Dimension-free Mixing for High-dimensional Bayesian Variable Selection
Quan Zhou, Jun Yang, Dootika Vats +2
Yang et al. (2016) proved that the symmetric random walk Metropolis--Hastings algorithm for Bayesian variable selection is rapidly mixing under mild high-dimensional assumptions. W…
Fast-rate PAC-Bayes Generalization Bounds via Shifted Rademacher Processes
Jun Yang, Shengyang Sun, Daniel M. Roy
The developments of Rademacher complexity and PAC-Bayesian theory have been largely independent. One exception is the PAC-Bayes theorem of Kakade, Sridharan, and Tewari (2008), whi…
Optimal Scaling of Random-Walk Metropolis Algorithms on General Target Distributions
Jun Yang, Gareth O. Roberts, Jeffrey S. Rosenthal
One main limitation of the existing optimal scaling results for Metropolis--Hastings algorithms is that the assumptions on the target distribution are unrealistic. In this paper, w…
State-domain Change Point Detection for Nonlinear Time Series Regression
Yan Cui, Jun Yang, Zhou Zhou
Change point detection in time series has attracted substantial interest, but most of the existing results have been focused on detecting change points in the time domain. This pap…