arXiv research

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

New method for tuning Graphical Lasso hyperparameters.

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

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

Novel AMP framework for multi-environment transfer learning.

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…

This paper is a survey of dictionary screening for the lasso problem. The lasso problem seeks a sparse linear combination of the columns of a dictionary to best match a given target vector. This sparse representation has proven useful in a variety of subsequent processing and decision tasks. For a given target vector, …

Complex network reconstruction is a hot topic in many fields. Currently, the most popular data-driven reconstruction framework is based on lasso. However, it is found that, in the presence of noise, lasso loses efficiency for weighted networks. This paper builds a new framework to cope with this problem. The key idea i…

Proposes MM-DUST for efficient generalized lasso solution paths.

This work aims at recovering signals that are sparse on graphs. Compressed sensing offers techniques for signal recovery from a few linear measurements and graph Fourier analysis provides a signal representation on graph. In this paper, we leverage these two frameworks to introduce a new Lasso recovery algorithm on gra…

Thresholded Lasso bandit minimizes regret in sparse linear bandits.

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

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

New mathematical framework proves the effectiveness of reducing neural network sizes.

Sparse modeling is a powerful framework for data analysis and processing. Traditionally, encoding in this framework is performed by solving an L1-regularized linear regression problem, commonly referred to as Lasso or Basis Pursuit. In this work we combine the sparsity-inducing property of the Lasso model at the indivi…

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…

We study a generalized framework for structured sparsity. It extends the well-known methods of Lasso and Group Lasso by incorporating additional constraints on the variables as part of a convex optimization problem. This framework provides a straightforward way of favouring prescribed sparsity patterns, such as orderin…

Paper introduces machine learning for time series data, improving nowcasting accuracy.

Javanmard and Montanari propose a debiased estimator for high-dimensional regression.

Ride sharing has important implications in terms of environmental, social and individual goals by reducing carbon footprints, fostering social interactions and economizing commuter costs. The ride sharing systems that are commonly available lack adaptive and scalable techniques that can simultaneously learn from the la…

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…

Bayesian pliable lasso with horseshoe prior models interactions in GLMs with missing data.

We develop a general approach to valid inference after model selection. At the core of our framework is a result that characterizes the distribution of a post-selection estimator conditioned on the selection event. We specialize the approach to model selection by the lasso to form valid confidence intervals for the sel…

In high dimension, it is customary to consider Lasso-type estimators to enforce sparsity. For standard Lasso theory to hold, the regularization parameter should be proportional to the noise level, yet the latter is generally unknown in practice. A possible remedy is to consider estimators, such as the Concomitant/Scale…

In many learning tasks, structural models usually lead to better interpretability and higher generalization performance. In recent years, however, the simple structural models such as lasso are frequently proved to be insufficient. Accordingly, there has been a lot of work on "superposition-structured" models where mul…

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…

Classification with a sparsity constraint on the solution plays a central role in many high dimensional machine learning applications. In some cases, the features can be grouped together so that entire subsets of features can be selected or not selected. In many applications, however, this can be too restrictive. In th…

Proposes HSIC-Lasso for selective inference in non-linear data.

PliableBVS extends Bayesian lasso for modeling interactions with modifying variables.

The analysis of scientific data of increasing size and complexity requires statistical machine learning methods that are both interpretable and predictive. Union of Intersections (UoI), a recently developed framework, is a two-step approach that separates model selection and model estimation. A linear regression algori…

SMART-FAN-Lasso fine-tunes neural networks for high-dimensional nonparametric regression.

Enhances selective inference for generalized lasso using parametric programming.

Unified framework for high-dimensional bandit problems with low-dimensional structures.

The paper analyzes LASSO penalization for high-dimensional Beta regression models.

A neural network approach unifies Lasso for variable selection.

Paper proposes a method to model health outcomes using varying-coefficients and KNN-based LASSO.

New functional ME models for predicting heterogeneous functional data.

Penalized regression is an attractive framework for variable selection problems. Often, variables possess a grouping structure, and the relevant selection problem is that of selecting groups, not individual variables. The group lasso has been proposed as a way of extending the ideas of the lasso to the problem of group…

New method stabilizes private LASSO for high-dimensional data with diverse covariate scales.

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.

New method recovers relative rates in spatial compositional data from IMS.

Novel approach integrates Multivariate Square-root Lasso into Synthetic Control for high-dimensional data.

Model selection is difficult to analyse yet theoretically and empirically important, especially for high-dimensional data analysis. Recently the least absolute shrinkage and selection operator (Lasso) has been applied in the statistical and econometric literature. Consis- tency of Lasso has been established under vario…

Study finds short-term trading signals can enhance alpha in U.S. S&P 500 portfolios.

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

New framework models complex spatial data with basis functions and graphical vectors.

Unified framework for pattern recovery in penalized and thresholded estimation.