arXiv research

Bayesian method discovers PDEs with variable coefficients robustly.

This paper considers the problem of estimating multiple related Gaussian graphical models from a $p$-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…

In this paper, we investigate a multivariate multi-response (MVMR) linear regression problem, which contains multiple linear regression models with differently distributed design matrices, and different regression and output vectors. The goal is to recover the support union of all regression vectors using $l_1/l_2$-reg…

New method corrects selection bias in post-selective inference for Group LASSO.

MicroRNAs (miRNAs) are small RNA molecules composed of 19-22 nt, which play important regulatory roles in post-transcriptional gene regulation by inhibiting the translation of the mRNA into proteins or otherwise cleaving the target mRNA. Inferring miRNA targets provides useful information for understanding the roles of…

Graphical lasso may fail to fit models when data points are insufficient.

Ridge regression is revisited with debiasing and thresholding, offering advantages over Lasso.

We consider the sparse inverse covariance regularization problem or graphical lasso with regularization parameter $ρ$. Suppose the co- variance graph formed by thresholding the entries of the sample covariance matrix at $ρ$ is decomposed into connected components. We show that the vertex-partition induced by the thresh…

Thresholded Lasso bandit minimizes regret in sparse linear bandits.

In this paper, we introduce Adaptive Cluster Lasso(ACL) method for variable selection in high dimensional sparse regression models with strongly correlated variables. To handle correlated variables, the concept of clustering or grouping variables and then pursuing model fitting is widely accepted. When the dimension is…

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 review summarizes five Lasso optimization algorithms.

Bayesian approach improves network lasso for multi-task learning.

Heavy Lasso improves robustness in high-dimensional linear regression with heavy-tailed errors.

The Bayesian Lasso is constructed in the linear regression framework and applies the Gibbs sampling to estimate the regression parameters. This paper develops a new sparse learning model, named the Bayesian Lasso Sparse (BLS) model, that takes the hierarchical model formulation of the Bayesian Lasso. The main differenc…

Paper analyzes adaptive ISTA with MAD for LASSO problem.

Proposes a new Lasso method with performance constraints.

Optimal algorithm for high-dimensional stochastic linear bandits with sparse parameters.

The high-dimensional linear model $y = X β^0 + ε$ is considered and the focus is put on the problem of recovering the support $S^0$ of the sparse vector $β^0.$ We introduce Lasso-Zero, a new $\ell_1$-based estimator whose novelty resides in an "overfit, then threshold" paradigm and the use of noise dictionaries concate…

Lasso proves consistent model selection for high-dimensional Ising models.

To estimate a sparse linear model from data with Gaussian noise, consilience from lasso and compressed sensing literatures is that thresholding estimators like lasso and the Dantzig selector have the ability in some situations to identify with high probability part of the significant covariates asymptotically, and are …

A new method learns DAG structures from data without false edges.

A simple thresholding technique improves graph selection in neural connectivity studies.

Paper introduces MGLasso for multiscale graph inference in clustering and network analysis.

We investigate the choice of tuning parameters for a Bayesian multi-level group lasso model developed for the joint analysis of neuroimaging and genetic data. The regression model we consider relates multivariate phenotypes consisting of brain summary measures (volumetric and cortical thickness values) to single nucleo…

We consider the problem of learning a high-dimensional multi-task regression model, under sparsity constraints induced by presence of grouping structures on the input covariates and on the output predictors. This problem is primarily motivated by expression quantitative trait locus (eQTL) mapping, of which the goal is …

We consider a joint processing of $n$ independent sparse regression problems. Each is based on a sample $(y_{i1},x_{i1})...,(y_{im},x_{im})$ of $m$ \iid observations from $y_{i1}=x_{i1}\tβ_i+\eps_{i1}$, $y_{i1}\in \R$, $x_{i 1}\in\R^p$, $i=1,...,n$, and $\eps_{i1}\dist N(0,\sig^2)$, say. $p$ is large enough so that the…

In multivariate regression, a $K$-dimensional response vector is regressed upon a common set of $p$ covariates, with a matrix $B^*\in\mathbb{R}^{p\times K}$ of regression coefficients. We study the behavior of the multivariate group Lasso, in which block regularization based on the $\ell_1/\ell_2$ norm is used for supp…

Scalable Bayesian LASSO using variational inference for large p and n.

Paper proposes new Bayesian neural network models for efficient learning.

New bounds for Lasso and Group Lasso in high dimensions derived.

Improves graph recovery in Gaussian graphical modeling.

Unified framework for pattern recovery in penalized and thresholded estimation.

Confidence intervals based on penalized maximum likelihood estimators such as the LASSO, adaptive LASSO, and hard-thresholding are analyzed. In the known-variance case, the finite-sample coverage properties of such intervals are determined and it is shown that symmetric intervals are the shortest. The length of the sho…

Bayesian GAMs improve predictive performance for high-dimensional data.

The fused lasso penalizes a loss function by the $L_1$ norm for both the regression coefficients and their successive differences to encourage sparsity of both. In this paper, we propose a Bayesian generalized fused lasso modeling based on a normal-exponential-gamma (NEG) prior distribution. The NEG prior is assumed in…

DFR reduces the computational cost of sparse-group lasso and adaptive sparse-group lasso.

High-dimensional feature selection arises in many areas of modern science. For example, in genomic research we want to find the genes that can be used to separate tissues of different classes (e.g. cancer and normal) from tens of thousands of genes that are active (expressed) in certain tissue cells. To this end, we wi…

A privacy-preserving algorithm for high-dimensional bandits.

Iterative thresholding algorithms are well-suited for high-dimensional problems in sparse recovery and compressive sensing. The performance of this class of algorithms depends heavily on the tuning of certain threshold parameters. In particular, both the final reconstruction error and the convergence rate of the algori…

We introduce an application of the group lasso to design of experiments. Note that we are NOT trying to explain experimental design for the group lasso. Conversely, we explain how we can use the idea of the group lasso in experimental design, showing that the problem of constructing an optimal design matrix can be tran…

CV inference can be invalid for relatively unstable model comparisons.

Bayesian method for estimating functional graphical models from neuroimaging data.

Robust Lasso-Zero handles missing covariates and sparse corruptions.

A new method speeds up overlapping group lasso computations.

This paper develops a theory for group Lasso using a concept called strong group sparsity. Our result shows that group Lasso is superior to standard Lasso for strongly group-sparse signals. This provides a convincing theoretical justification for using group sparse regularization when the underlying group structure is …

Bayesian framework proves thresholds for multi-graph alignment feasibility.

Unified framework for shrinkage, thresholding, and regularization in normal mean estimation and linear regression.