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Found 4 papers, 1 papers with code
On Cyclical MCMC Sampling
Cyclical MCMC is a novel MCMC framework recently proposed by Zhang et al. (2019) to address the challenge posed by high-dimensional multimodal posterior distributions like those arising in deep learning.
A statistical perspective on algorithm unrolling models for inverse problems
We show that the unrolling depth needed for the optimal statistical performance of GDNs is of order $\log(n)/\log(\varrho_n^{-1})$, where $n$ is the sample size, and $\varrho_n$ is the convergence rate of the corresponding gradient descent algorithm.
Minimax Quasi-Bayesian estimation in sparse canonical correlation analysis via a Rayleigh quotient function
The method builds on Tan et al. (2018) and uses a re-scaled Rayleigh quotient function as the quasi-log-likelihood.
Sequential change-point detection in high-dimensional Gaussian graphical models
High dimensional piecewise stationary graphical models represent a versatile class for modelling time varying networks arising in diverse application areas, including biology, economics, and social sciences.
