Cover Image

Accept-Reject Lasso

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Liu, Yanxin, Zhang, Yunqi
Format:	Preprint
Published:	2025
Subjects:	Methodology Machine Learning
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Relative Overfitting and Accept-Reject Framework
by: Liu, Yanxin, et al.
Published: (2025)

A Stable Lasso
by: Nouraie, Mahdi, et al.
Published: (2025)

A cost-sensitive constrained Lasso
by: Blanquero, Rafael, et al.
Published: (2024)

Adaptive Lasso, Transfer Lasso, and Beyond: An Asymptotic Perspective
by: Takada, Masaaki, et al.
Published: (2023)

SMART Fine-tuning Factor Augmented Neural Lasso
by: Chai, Jinhang, et al.
Published: (2026)

Autotune: fast, accurate, and automatic tuning parameter selection for Lasso
by: Sadhukhan, Tathagata, et al.
Published: (2025)

The Contextual Lasso: Sparse Linear Models via Deep Neural Networks
by: Thompson, Ryan, et al.
Published: (2023)

Time-to-event prediction for grouped variables using Exclusive Lasso
by: Ravi, Dayasri, et al.
Published: (2025)

A Hierarchical Variational Graph Fused Lasso for Recovering Relative Rates in Spatial Compositional Data
by: Teixeira, Joaquim Valerio, et al.
Published: (2025)

Triple/Debiased Lasso for Statistical Inference of Conditional Average Treatment Effects
by: Kato, Masahiro
Published: (2024)

Exponential Lasso: robust sparse penalization under heavy-tailed noise and outliers with exponential-type loss
by: Mai, The Tien
Published: (2025)

Heavy Lasso: sparse penalized regression under heavy-tailed noise via data-augmented soft-thresholding
by: Mai, The Tien
Published: (2025)

Bayesian Pliable Lasso with Horseshoe Prior for Interaction Effects in GLMs with Missing Responses
by: Mai, The Tien
Published: (2025)

Lasso Penalization for High-Dimensional Beta Regression Models: Computation, Analysis, and Inference
by: Ramezani, Niloofar, et al.
Published: (2025)

Stability of a Generalized Debiased Lasso with Applications to Resampling-Based Variable Selection
by: Liu, Jingbo
Published: (2024)

BPE: Behavioral Profiling Ensemble
by: Liu, Yanxin, et al.
Published: (2026)

Online Conformal Selection with Accept-to-Reject Changes
by: Liu, Kangdao, et al.
Published: (2025)

RCT Rejection Sampling for Causal Estimation Evaluation
by: Keith, Katherine A., et al.
Published: (2023)

Few-shot Multi-Task Learning of Linear Invariant Features with Meta Subspace Pursuit
by: Zhang, Chaozhi, et al.
Published: (2024)

HACSurv: A Hierarchical Copula-Based Approach for Survival Analysis with Dependent Competing Risks
by: Liu, Xin, et al.
Published: (2024)

Minimum Wasserstein distance estimator under covariate shift: closed-form, super-efficiency and irregularity
by: Lang, Junjun, et al.
Published: (2026)

Constrained Multi-objective Bayesian Optimization through Optimistic Constraints Estimation
by: Li, Diantong, et al.
Published: (2024)

Optimal Kernel Choice for Score Function-based Causal Discovery
by: Wang, Wenjie, et al.
Published: (2024)

Advancing Counterfactual Inference through Nonlinear Quantile Regression
by: Xie, Shaoan, et al.
Published: (2023)

SubgroupTE: Advancing Treatment Effect Estimation with Subgroup Identification
by: Lee, Seungyeon, et al.
Published: (2024)

Positive and Unlabeled Data: Model, Estimation, Inference, and Classification
by: Liu, Siyan, et al.
Published: (2024)

Multi-Fidelity Quantile Regression
by: Liu, Yixiang, et al.
Published: (2026)

Causal Effect Estimation using identifiable Variational AutoEncoder with Latent Confounders and Post-Treatment Variables
by: Xie, Yang, et al.
Published: (2024)

Towards Identifiable Latent Additive Noise Models
by: Liu, Yuhang, et al.
Published: (2024)

Robust Variational Bayes by Min-Max Median Aggregation
by: Yan, Jiawei, et al.
Published: (2025)

Transfer Learning of CATE with Kernel Ridge Regression
by: Kim, Seok-Jin, et al.
Published: (2025)

SPARKLE: A Nonparametric Approach for Online Decision-Making with High-Dimensional Covariates
by: Wang, Wenjia, et al.
Published: (2025)

Towards Accurate and Interpretable Time-series Forecasting: A Polynomial Learning Approach
by: Liu, Bo, et al.
Published: (2026)

Semiparametric Learning from Open-Set Label Shift Data
by: Liu, Siyan, et al.
Published: (2025)

A Black-Box Debiasing Framework for Conditional Sampling
by: Cui, Han, et al.
Published: (2025)

Flexible Selective Inference with Flow-based Transport Maps
by: Liu, Sifan, et al.
Published: (2025)

We Have It Covered: A Resampling-based Method for Uplift Model Comparison
by: Liu, Yang, et al.
Published: (2025)

Convex estimation of Gaussian graphical regression models with covariates
by: Liu, Ruobin, et al.
Published: (2024)

Kernel Tests of Equivalence
by: Liu, Xing, et al.
Published: (2026)

Symbolic Density Estimation for Discrete Distributions
by: Liu, Ziwen, et al.
Published: (2026)