1 code implementation • 5 Mar 2024 • Meixia Lin, Yangjing Zhang
We consider the problem of jointly learning row-wise and column-wise dependencies of matrix-variate observations, which are modelled separately by two precision matrices.
no code implementations • 17 Aug 2023 • Chengjing Wang, Peipei Tang, Wenling He, Meixia Lin
To efficiently estimate the hub graphical models, we introduce a two-phase algorithm.
no code implementations • NeurIPS 2021 • Remi Bardenet, Subhro Ghosh, Meixia Lin
In particular, we show how specific DPPs and a string of controlled approximations can lead to gradient estimators with a variance that decays faster with the batchsize than under uniform sampling.
no code implementations • 6 May 2021 • Subhroshekhar Ghosh, Meixia Lin, Dongfang Sun
In this work, we investigate spectrogram analysis via an examination of the stochastic geometric properties of their level sets.
no code implementations • 17 Apr 2020 • Meixia Lin, Defeng Sun, Kim-Chuan Toh, Chengjing Wang
The sparsity and clustering structure of the concentration matrix is enforced to reduce model complexity and describe inherent regularities.
no code implementations • 26 Feb 2020 • Meixia Lin, Defeng Sun, Kim-Chuan Toh
We prove that the least squares estimator is computable via solving a constrained convex quadratic programming (QP) problem with $(n+1)d$ variables and at least $n(n-1)$ linear inequality constraints, where $n$ is the number of data points.
no code implementations • 1 Feb 2019 • Meixia Lin, Defeng Sun, Kim-Chuan Toh, Yancheng Yuan
In addition, we derive the corresponding HS-Jacobian to the proximal mapping and analyze its structure --- which plays an essential role in the efficient computation of the PPA subproblem via applying a semismooth Newton method on its dual.
no code implementations • 22 Aug 2018 • Meixia Lin, Yong-Jin Liu, Defeng Sun, Kim-Chuan Toh
Based on the new formulation, we derive an efficient procedure for its computation.