arXiv research

Proposes a new method for estimating treatment effects using outcome highly adaptive lasso.

PCHAL and PCHAR use principal components to speed up HAL and HAR methods.

New nonparametric estimators improve causal effect estimation.

Proposes a new method for estimating non-pathwise differentiable functional parameters.

Paper proposes ASCCA for sparse CCA with trace Lasso regularization.

We study the distribution of the adaptive LASSO estimator (Zou (2006)) in finite samples as well as in the large-sample limit. The large-sample distributions are derived both for the case where the adaptive LASSO estimator is tuned to perform conservative model selection as well as for the case where the tuning results…

MT-HAL learns features and task associations for multiple tasks with a shared sparse structure.

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

Proposes a new method for hyperspectral image dimensionality reduction.

We consider the problem of predicting an outcome variable using $p$ covariates that are measured on $n$ independent observations, in the setting in which flexible and interpretable fits are desirable. We propose the fused lasso additive model (FLAM), in which each additive function is estimated to be piecewise constant…

TMLE improves causal effect estimation in missing data scenarios with various positivity violations.

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

gOMP algorithm selects features for various types of data.

HALO learns to prune neural networks by adaptively shrinking weights.

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

Using the $\ell_1$-norm to regularize the estimation of the parameter vector of a linear model leads to an unstable estimator when covariates are highly correlated. In this paper, we introduce a new penalty function which takes into account the correlation of the design matrix to stabilize the estimation. This norm, ca…

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

Study improves statistical inference for CATEs using Lasso and DML.

LUQ-Learning adapts Q-learning for healthcare decisions considering patient preferences.

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

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

New algorithms for batch decision-making with high-dimensional user data.

We derive new theoretical results on the properties of the adaptive least absolute shrinkage and selection operator (adaptive lasso) for time series regression models. In particular, we investigate the question of how to conduct finite sample inference on the parameters given an adaptive lasso model for some fixed valu…

New robust estimator for high-dimensional data with outliers and leverage points.

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

Network Lasso improves semi-supervised regression on network data.

Paper analyzes adaptive ISTA with MAD for LASSO problem.

Proposes a novel framework for unsupervised domain adaptation using causal representations.

LARGE adapts regularization for better graph estimation in high-dimensional data.

Efficient algorithms for clustered Lasso and OSCAR reduce computational costs.

New method proves neural networks can select features consistently.

Efficient method solves constrained Lasso problems.

Random forests and LASSO methods improve small area estimation using auxiliary data.

Network Lasso classifies partially labeled data with high-dimensional features.

Proposes online debiasing to correct bias in adaptive data collection for high-dimensional linear regression.

Proposes a new Lasso method for high missing rate data.

Adaptive Group Lasso selects important features in neural networks.

Exclusive Group Lasso improves feature selection in correlated biological data.

The $\ell_1$-penalized method, or the Lasso, has emerged as an important tool for the analysis of large data sets. Many important results have been obtained for the Lasso in linear regression which have led to a deeper understanding of high-dimensional statistical problems. In this article, we consider a class of weigh…

Although the Lasso has been extensively studied, the relationship between its prediction performance and the correlations of the covariates is not fully understood. In this paper, we give new insights into this relationship in the context of multiple linear regression. We show, in particular, that the incorporation of …

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.…

A reinforcement learning method predicts medical outcomes from masked feature vectors.

Estimates CATEs using high-dimensional linear regression models.

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

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

Network Lasso improves machine learning from network data with new sampling conditions.

Cluster stability selection improves feature selection in correlated data.

A key requirement for the current generation of artificial decision-makers is that they should adapt well to changes in unexpected situations. This paper addresses the situation in which an AI for aerial dog fighting, with tunable parameters that govern its behavior, must optimize behavior with respect to an objective …