no code implementations • 1 Mar 2024 • LiWei Wang, Xinru Liu, Aaron Smith, Yves Atchade
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.
no code implementations • 10 Nov 2023 • Yves Atchade, Xinru Liu, Qiuyun Zhu
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.
1 code implementation • 16 Oct 2020 • Qiuyun Zhu, Yves Atchade
The method builds on Tan et al. (2018) and uses a re-scaled Rayleigh quotient function as the quasi-log-likelihood.
no code implementations • 20 Jun 2018 • Hossein Keshavarz, George Michailidis, Yves Atchade
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.