arXiv research

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…

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

A new method speeds up overlapping group lasso computations.

Paper proposes a method to efficiently estimate structural breaks in cointegrating regressions.

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

Introduces Temporal Group LASSO for healthcare time series prediction.

New method proves neural networks can select features consistently.

The group lasso is a penalized regression method, used in regression problems where the covariates are partitioned into groups to promote sparsity at the group level. Existing methods for finding the group lasso estimator either use gradient projection methods to update the entire coefficient vector simultaneously at e…

The sparse group lasso optimization problem is solved using a coordinate gradient descent algorithm. The algorithm is applicable to a broad class of convex loss functions. Convergence of the algorithm is established, and the algorithm is used to investigate the performance of the multinomial sparse group lasso classifi…

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

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

Sparse group Lasso optimizes sparse and grouped parameters in high-dimensional data.

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

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 …

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…

The Group-Lasso is a well-known tool for joint regularization in machine learning methods. While the l_{1,2} and the l_{1,\infty} version have been studied in detail and efficient algorithms exist, there are still open questions regarding other l_{1,p} variants. We characterize conditions for solutions of the l_{1,p} G…

In high dimensional settings, sparse structures are crucial for efficiency, either in term of memory, computation or performance. In some contexts, it is natural to handle more refined structures than pure sparsity, such as for instance group sparsity. Sparse-Group Lasso has recently been introduced in the context of l…

Recently, to solve large-scale lasso and group lasso problems, screening rules have been developed, the goal of which is to reduce the problem size by efficiently discarding zero coefficients using simple rules independently of the others. However, screening for overlapping group lasso remains an open challenge because…

New theorem for generalized group sparsity improves consistency and convergence rates.

Exclusive Group Lasso improves feature selection in correlated biological data.

We introduce a recursive adaptive group lasso algorithm for real-time penalized least squares prediction that produces a time sequence of optimal sparse predictor coefficient vectors. At each time index the proposed algorithm computes an exact update of the optimal $\ell_{1,\infty}$-penalized recursive least squares (R…

Standardization boosts sparse group lasso performance for categorical variables.

New guarantees for Group LASSO in sparse convex optimization.

Adaptive Group Lasso selects important features in neural networks.

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…

The paper improves support recovery guarantees for the group Lasso.

Improves robustness of high-dimensional regression with rank objective and group lasso regularization.

A distributed feature selection framework identifies genetic risk factors for Alzheimer's disease.

Study high-dimensional Granger causality tests for VIX and financial news.

The paper improves machine learning for heavy-tailed panel data.

DNN nodes selection improved using gLasso regularization.

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

Proposes an algorithm for infinite-dimensional sparse learning in system identification.

A parallel algorithm improves gLasso scalability for large datasets.

New group-sparse SVD models improve biclustering of gene expression data.

In a recent paper, it is shown that the LASSO algorithm exhibits "near-ideal behavior," in the following sense: Suppose $y = Az + η$ where $A$ satisfies the restricted isometry property (RIP) with a sufficiently small constant, and $\Vert η\Vert_2 \leq ε$. Then minimizing $\Vert z \Vert_1$ subject to $\Vert y - Az \Ver…

We present a data dependent generalization bound for a large class of regularized algorithms which implement structured sparsity constraints. The bound can be applied to standard squared-norm regularization, the Lasso, the group Lasso, some versions of the group Lasso with overlapping groups, multiple kernel learning a…

A new model identifies genetic risk factors using gene-level priors.

A wide class of regularization problems in machine learning and statistics employ a regularization term which is obtained by composing a simple convex function ωwith a linear transformation. This setting includes Group Lasso methods, the Fused Lasso and other total variation methods, multi-task learning methods and man…

Channel pruning and weight binarization improve keyword spotting accuracy.

We present two sets of theoretical results on the grouped lasso with overlap of Jacob, Obozinski and Vert (2009) in the linear regression setting. This method allows for joint selection of predictors in sparse regression, allowing for complex structured sparsity over the predictors encoded as a set of groups. This flex…

In compressed sensing, in order to recover a sparse or nearly sparse vector from possibly noisy measurements, the most popular approach is $\ell_1$-norm minimization. Upper bounds for the $\ell_2$- norm of the error between the true and estimated vectors are given in [1] and reviewed in [2], while bounds for the $\ell_…

Paper develops a new estimator for high-dimensional panel data with common shocks.

Sparse regularization reduces NLP model complexity without sacrificing accuracy.

In applications ranging from communications to genetics, signals can be modeled as lying in a union of subspaces. Under this model, signal coefficients that lie in certain subspaces are active or inactive together. The potential subspaces are known in advance, but the particular set of subspaces that are active (i.e., …

Study improves carbon price forecasting using quantile regression and feature selection.

Paper uses machine learning for nowcasting corporate earnings from mixed-frequency data.

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…