A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.
A simple thresholding technique improves graph selection in neural connectivity studies.
Graphical Lasso detects anomalies in noisy data by splitting covariance matrix into clean and outlier parts.
New method for tuning Graphical Lasso hyperparameters.
Graphical lasso may fail to fit models when data points are insufficient.
Efficient algorithms solve joint graphical lasso problems.
We consider the task of estimating a Gaussian graphical model in the high-dimensional setting. The graphical lasso, which involves maximizing the Gaussian log likelihood subject to an l1 penalty, is a well-studied approach for this task. We begin by introducing a surprising connection between the graphical lasso and hi…
Method infers multi-layer networks from gene expression data.
A graphical model is a statistical model that is associated to a graph whose nodes correspond to variables of interest. The edges of the graph reflect allowed conditional dependencies among the variables. Graphical models admit computationally convenient factorization properties and have long been a valuable tool for t…
We develop a new method called Discriminated Hub Graphical Lasso (DHGL) based on Hub Graphical Lasso (HGL) by providing prior information of hubs. We apply this new method in two situations: with known hubs and without known hubs. Then we compare DHGL with HGL using several measures of performance. When some hubs are k…
We consider the sparse inverse covariance regularization problem or graphical lasso with regularization parameter . Suppose the co- variance graph formed by thresholding the entries of the sample covariance matrix at is decomposed into connected components. We show that the vertex-partition induced by the thresh…
Lasso performs poorly with correlated covariates, but a rescaled approach fixes this.
Paper introduces MGLasso for multiscale graph inference in clustering and network analysis.
In this article, we propose a new class of priors for Bayesian inference with multiple Gaussian graphical models. We introduce fully Bayesian treatments of two popular procedures, the group graphical lasso and the fused graphical lasso, and extend them to a continuous spike-and-slab framework to allow self-adaptive shr…
This paper considers the problem of estimating multiple related Gaussian graphical models from a -dimensional dataset consisting of different classes. Our work is based upon the formulation of this problem as group graphical lasso. This paper proposes a novel hybrid covariance thresholding algorithm that can effecti…
Covariance graphical lasso applies a lasso penalty on the elements of the covariance matrix. This method is useful because it not only produces sparse estimation of covariance matrix but also discovers marginal independence structures by generating zeros in the covariance matrix. We propose and explore two new algorith…
For large scale on-line inference problems the update strategy is critical for performance. We derive an adaptive scan Gibbs sampler that optimizes the update frequency by selecting an optimum mini-batch size. We demonstrate performance of our adaptive batch-size Gibbs sampler by comparing it against the collapsed Gibb…
New method aggregates nodes in sparse graphical models.
New framework models complex spatial data with basis functions and graphical vectors.
In many applications of finance, biology and sociology, complex systems involve entities interacting with each other. These processes have the peculiarity of evolving over time and of comprising latent factors, which influence the system without being explicitly measured. In this work we present latent variable time-va…
Graphical Lasso algorithm segments occupants' energy usage behaviors in HC-CP systems.
We show that the two-stage adaptive Lasso procedure (Zou, 2006) is consistent for high-dimensional model selection in linear and Gaussian graphical models. Our conditions for consistency cover more general situations than those accomplished in previous work: we prove that restricted eigenvalue conditions (Bickel et al.…
Novel approach to robustly estimate inverse covariance matrix for multivariate data.
We consider the problem of Graphical lasso with an additional element-wise norm constraint on the precision matrix. This problem has applications in high-dimensional covariance decomposition such as in \citep{Janzamin-12}. We propose an ADMM algorithm to solve this problem. We also use a continuation st…
A new algorithm improves GLasso for sparse precision matrix estimation.
Improves graph recovery in Gaussian graphical modeling.
Bayesian method for estimating functional graphical models from neuroimaging data.
Gaussian Graphical Models (GGMs) are popular tools for studying network structures. However, many modern applications such as gene network discovery and social interactions analysis often involve high-dimensional noisy data with outliers or heavier tails than the Gaussian distribution. In this paper, we propose the Tri…
This thesis studies two problems in modern statistics. First, we study selective inference, or inference for hypothesis that are chosen after looking at the data. The motiving application is inference for regression coefficients selected by the lasso. We present the Condition-on-Selection method that allows for valid s…
SyGlasso models tensor data dependencies using Sylvester equations.
We propose a scalable, efficient and statistically motivated computational framework for Graphical Lasso (Friedman et al., 2007b) - a covariance regularization framework that has received significant attention in the statistics community over the past few years. Existing algorithms have trouble in scaling to dimensions…
Paper develops differentially private methods for estimating precision matrices.
Graphical lasso models ASR utterance dependencies for consistent WER estimation.
Bayesian method improves portfolio management with limited data.
A new method speeds up overlapping group lasso computations.
This paper examines the implementation of a statistical arbitrage trading strategy based on co-integration relationships where we discover candidate portfolios using multiple factors rather than just price data. The portfolio selection methodologies include K-means clustering, graphical lasso and a combination of the t…
We study a norm for structured sparsity which leads to sparse linear predictors whose supports are unions of prede ned overlapping groups of variables. We call the obtained formulation latent group Lasso, since it is based on applying the usual group Lasso penalty on a set of latent variables. A detailed analysis of th…
Novel segmentation method for energy game-theoretic frameworks using graphical lasso.
Develops FGL for better portfolio allocation under common factor influence.
R package for multi-objective model selection in statistics.
The graphical lasso \citep{FHT2007a} is an algorithm for learning the structure in an undirected Gaussian graphical model, using regularization to control the number of zeros in the precision matrix ${\BΘ}={\BΣ}^{-1}$ \citep{BGA2008,yuan_lin_07}. The {\texttt R} package \GL\ \citep{FHT2007a} is popular, fast, …
A new method for joint eQTL mapping and gene network estimation.
Fair GLASSO estimates fair GGMs by balancing statistical dependencies across groups.
The paper shows cross-validation fails in learning Gaussian graphical model structures.
GTMs model complex multivariate data with varying conditional independencies.
We learn sparse precision matrices from compressed data sketches.
Paper relaxes identifiability conditions for causal models with latent variables.
New method learns graphical models with latent variables for extreme events.
Paper compares two methods for inferring network structures in presence of latent confounders.