arXiv research

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…

LLM-Lasso uses LLMs to improve feature selection in Lasso regression.

Bayesian approach improves network lasso for multi-task learning.

Proposes MM-DUST for efficient generalized lasso solution paths.

Robust Lasso-Zero handles missing covariates and sparse corruptions.

The performance of the Lasso is well understood under the assumptions of the standard linear model with homoscedastic noise. However, in several applications, the standard model does not describe the important features of the data. This paper examines how the Lasso performs on a non-standard model that is motivated by …

A new method speeds up overlapping group lasso computations.

A sparse modeling is a major topic in machine learning and statistics. LASSO (Least Absolute Shrinkage and Selection Operator) is a popular sparse modeling method while it has been known to yield unexpected large bias especially at a sparse representation. There have been several studies for improving this problem such…

While considerable advances have been made in estimating high-dimensional structured models from independent data using Lasso-type models, limited progress has been made for settings when the samples are dependent. We consider estimating structured VAR (vector auto-regressive models), where the structure can be capture…

We propose an improved LASSO estimation technique based on Stein-rule. We shrink classical LASSO estimator using preliminary test, shrinkage, and positive-rule shrinkage principle. Simulation results have been carried out for various configurations of correlation coefficients ($r$), size of the parameter vector ($β$), …

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…

We consider the least-square linear regression problem with regularization by the l1-norm, a problem usually referred to as the Lasso. In this paper, we present a detailed asymptotic analysis of model consistency of the Lasso. For various decays of the regularization parameter, we compute asymptotic equivalents of the …

Paper improves Lasso for S&P500 index tracking with post-selection inference.

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…

Study improves statistical inference for CATEs using Lasso and DML.

We compare alternative computing strategies for solving the constrained lasso problem. As its name suggests, the constrained lasso extends the widely-used lasso to handle linear constraints, which allow the user to incorporate prior information into the model. In addition to quadratic programming, we employ the alterna…

Nested model averaging improves high-dimensional linear regression performance.

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

Study evaluates scikit-learn regularization frameworks for machine learning models.

Improves Group Lasso for categorical data by reducing dimensionality and selecting models.

We apply network Lasso to semi-supervised regression problems involving network structured data. This approach lends quite naturally to highly scalable learning algorithms in the form of message passing over an empirical graph which represents the network structure of the data. By using a simple non-parametric regressi…

PLS-Lasso integrates dimension reduction into regression for financial index tracking.

The paper examines Adaptive Lasso and Transfer Lasso, highlighting their differences and proposing a new method.

The Lasso is a very well known penalized regression model, which adds an $L_{1}$ penalty with parameter $λ_{1}$ on the coefficients to the squared error loss function. The Fused Lasso extends this model by also putting an $L_{1}$ penalty with parameter $λ_{2}$ on the difference of neighboring coefficients, assuming the…

The paper improves Lasso inference methods for survey data.

The least absolute shrinkage and selection operator (lasso) and ridge regression produce usually different estimates although input, loss function and parameterization of the penalty are identical. In this paper we look for ridge and lasso models with identical solution set. It turns out, that the lasso model with shri…

Exponential Lasso improves Lasso's robustness to outliers and heavy-tailed noise.

Study examines Lasso performance in high-dimensional MoE models.

Proposes a new Lasso method with performance constraints.

Paper analyzes adaptive Lasso for high-dimensional diffusion processes, improving support recovery and bias.

The lasso model has been widely used for model selection in data mining, machine learning, and high-dimensional statistical analysis. However, with the ultrahigh-dimensional, large-scale data sets now collected in many real-world applications, it is important to develop algorithms to solve the lasso that efficiently sc…

Efficient algorithms solve joint graphical lasso problems.

We consider the least-square linear regression problem with regularization by the $\ell^1$-norm, a problem usually referred to as the Lasso. In this paper, we first present a detailed asymptotic analysis of model consistency of the Lasso in low-dimensional settings. For various decays of the regularization parameter, w…

A new estimator corrects bias in high-dimensional predictive regressions.

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

A new screening rule improves lasso solving speed.

New method proves neural networks can select features consistently.

The paper examines how to protect LASSO-based feature selection from adversarial attacks.

LASSO-PCA combines LASSO and PCA for automated forecast averaging.

Deep neural nets on 1-D data are convex Lasso models with reflection features.

In this paper we present a regression based model for day-ahead electricity spot prices. We estimate the considered linear regression model by the lasso estimation method. The lasso approach allows for many possible parameters in the model, but also shrinks and sparsifies the parameters automatically to avoid overfitti…

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

Paper proposes a new method combining random forests and Lasso selection.

Selective inference for group lasso estimators across various distributions and covariates.

Exclusive Lasso improves survival prediction in cancer datasets.

New screening rules improve lasso model fitting efficiency.

A fast method for Lasso and Logistic Lasso problems.

We leverage recent advances in high-dimensional statistics to derive new L2 estimation upper bounds for Lasso and Group Lasso in high-dimensions. For Lasso, our bounds scale as $(k^*/n) \log(p/k^*)$---$n\times p$ is the size of the design matrix and $k^*$ the dimension of the ground truth $\boldsymbolβ^*$---and match t…