Zobrazeno 1 - 10
of 306
pro vyhledávání: '"RAJARATNAM, BALA"'
Graphical models have found widespread applications in many areas of modern statistics and machine learning. Iterative Proportional Fitting (IPF) and its variants have become the default method for undirected graphical model estimation, and are thus
Externí odkaz:
http://arxiv.org/abs/2408.11718
Knowledge graphs (KGs) are known for their large scale and knowledge inference ability, but are also notorious for the incompleteness associated with them. Due to the long-tail distribution of the relations in KGs, few-shot KG completion has been pro
Externí odkaz:
http://arxiv.org/abs/2209.01205
Many applications benefit from theory relevant to the identification of variables having large correlations or partial correlations in high dimension. Recently there has been progress in the ultra-high dimensional setting when the sample size $n$ is
Externí odkaz:
http://arxiv.org/abs/2101.04715
Autor:
Khare, Apoorva, Rajaratnam, Bala
Publikováno v:
In Journal of Mathematical Analysis and Applications 15 December 2023 528(2)
Publikováno v:
Seminaire Lotharingien de Combinatoire 78B (2017), Article #62
A surprising result of FitzGerald and Horn (1977) shows that $A^{\circ \alpha} := (a_{ij}^\alpha)$ is positive semidefinite (p.s.d.) for every entrywise nonnegative $n \times n$ p.s.d. matrix $A = (a_{ij})$ if and only if $\alpha$ is a positive integ
Externí odkaz:
http://arxiv.org/abs/1802.06976
Autor:
Vaccaro, Adam, Emile-Geay, Julien, Guillot, Dominque, Verna, Resherle, Morice, Colin, Kennedy, John, Rajaratnam, Bala
Publikováno v:
Journal of Climate, 2021 May . 34(10), 4169-4188.
Externí odkaz:
https://www.jstor.org/stable/27076847
Simple random walks are a basic staple of the foundation of probability theory and form the building block of many useful and complex stochastic processes. In this paper we study a natural generalization of the random walk to a process in which the a
Externí odkaz:
http://arxiv.org/abs/1708.03116
Bayesian shrinkage methods have generated a lot of recent interest as tools for high-dimensional regression and model selection. These methods naturally facilitate tractable uncertainty quantification and incorporation of prior information. A common
Externí odkaz:
http://arxiv.org/abs/1703.09163
Autor:
Khare, Apoorva, Rajaratnam, Bala
Publikováno v:
Journal of Mathematical Analysis and Applications 528 (2023), no. 2, art. # 127545, 18 pp
The Khinchin-Kahane inequality is a fundamental result in the probability literature, with the most general version to date holding in Banach spaces. Motivated by modern settings and applications, we generalize this inequality to arbitrary metric gro
Externí odkaz:
http://arxiv.org/abs/1610.03037
Covariance estimation for high-dimensional datasets is a fundamental problem in modern day statistics with numerous applications. In these high dimensional datasets, the number of variables p is typically larger than the sample size n. A popular way
Externí odkaz:
http://arxiv.org/abs/1610.02436