A critical review of LASSO and its derivatives for variable selection under dependence among covariates

L Freijeiro‐González, M Febrero‐Bande… - International …, 2022 - Wiley Online Library
The limitations of the well‐known LASSO regression as a variable selector are tested when
there exists dependence structures among covariates. We analyse both the classic situation …

Generative models of brain dynamics

M Ramezanian-Panahi, G Abrevaya… - Frontiers in artificial …, 2022 - frontiersin.org
This review article gives a high-level overview of the approaches across different scales of
organization and levels of abstraction. The studies covered in this paper include …

Climate policies that achieved major emission reductions: Global evidence from two decades

A Stechemesser, N Koch, E Mark, E Dilger, P Klösel… - Science, 2024 - science.org
Meeting the Paris Agreement's climate targets necessitates better knowledge about which
climate policies work in reducing emissions at the necessary scale. We provide a global …

Sparse identification of nonlinear dynamics for model predictive control in the low-data limit

E Kaiser, JN Kutz, SL Brunton - Proceedings of the …, 2018 - royalsocietypublishing.org
Data-driven discovery of dynamics via machine learning is pushing the frontiers of modelling
and control efforts, providing a tremendous opportunity to extend the reach of model …

Lung adenocarcinoma and lung squamous cell carcinoma cancer classification, biomarker identification, and gene expression analysis using overlapping feature …

JW Chen, J Dhahbi - Scientific reports, 2021 - nature.com
Lung cancer is one of the deadliest cancers in the world. Two of the most common subtypes,
lung adenocarcinoma (LUAD) and lung squamous cell carcinoma (LUSC), have drastically …

A modern maximum-likelihood theory for high-dimensional logistic regression

P Sur, EJ Candès - Proceedings of the National Academy of …, 2019 - National Acad Sciences
Students in statistics or data science usually learn early on that when the sample size n is
large relative to the number of variables p, fitting a logistic model by the method of maximum …

A unifying tutorial on approximate message passing

OY Feng, R Venkataramanan, C Rush… - … and Trends® in …, 2022 - nowpublishers.com
Over the last decade or so, Approximate Message Passing (AMP) algorithms have become
extremely popular in various structured high-dimensional statistical problems. Although the …

[图书][B] Introduction to high-dimensional statistics

C Giraud - 2021 - taylorfrancis.com
Praise for the first edition:"[This book] succeeds singularly at providing a structured
introduction to this active field of research.… it is arguably the most accessible overview yet …

S-lime: Stabilized-lime for model explanation

Z Zhou, G Hooker, F Wang - Proceedings of the 27th ACM SIGKDD …, 2021 - dl.acm.org
An increasing number of machine learning models have been deployed in domains with
high stakes such as finance and healthcare. Despite their superior performances, many …

A unified framework for sparse relaxed regularized regression: SR3

P Zheng, T Askham, SL Brunton, JN Kutz… - IEEE …, 2018 - ieeexplore.ieee.org
Regularized regression problems are ubiquitous in statistical modeling, signal processing,
and machine learning. Sparse regression, in particular, has been instrumental in scientific …