论文检索

张量图形Lasso：TeraLassoAI 解读

Summary The paper introduces a multiway tensor generalization of the bigraphical lasso which uses a two-way sparse Kronecker sum multivariate normal model for the precision matrix to model parsimoniously conditional dependence relationships of matrix variate data based on the Cartesian product of graphs. We call this tensor graphical lasso generalization TeraLasso. We demonstrate by using theory and examples that the TeraLasso model can be accurately and scalably estimated…

无重复矩阵型数据的均值与协方差联合估计AI 解读

Michael Hornsteina*, Roger Fana, Kerby Sheddena & Shuheng Zhouaba Department of Statistics, University of Michigan, Ann Arbor, MIb Department of Statistics, University of California, Riverside, CACONTACT Michael Hornstein mdhorn@umich.edu Department of Statistics, University of Michigan, Ann Arbor, MI 48109.Color versions of one or more of the figures in the article can be found online at www.tandfonline.com/r/JASA.Supplementary materials for this article are available…

Gemini：基于矩阵正态观测的图结构估计AI 解读

Undirected graphs can be used to describe matrix variate distributions. In this paper, we develop new methods for estimating the graphical structures and underlying parameters, namely, the row and column covariance and inverse covariance matrices from the matrix variate data. Under sparsity conditions, we show that one is able to recover the graphs and covariance matrices with a single random matrix from the matrix variate normal distribution. Our method extends, with…

差分隐私的统计框架AI 解读

One goal of statistical privacy research is to construct a data release mechanism that protects individual privacy while preserving information content. An example is a random mechanism that takes an input database X and outputs a random database Z according to a distribution Qn(⋅|X). Differential privacy is a particular privacy requirement developed by computer scientists in which Qn(⋅|X) is required to be insensitive to changes in one data point in X. This makes it…