Renet: Principled and Efficient Relaxation for the Elastic Net via Dynamic Objective Selection
Renet：通过动态目标选择为弹性网提供有原则且高效的松弛方法AI 解读 ↓
← 返回论文列表与筛选
阅读说明
本页整理公开书目信息，不生成学术质量评分。AI 解读根据题名及所注明的摘要或原文片段生成，未完成全文核验，可能有误；具体假设、证明与结论请核对原文。摘要保留来源文本，未经模型改写。
DOI 首次登记不等于接收或发表日期。部分更正、评论等条目没有独立摘要；缺少可靠材料时保留待补状态。
本页为预印本版本；arXiv 收录、作者填写的期刊信息以及作者既有发表经历，都不单独证明本篇已接收。作者发表记录按 OpenAlex 身份与完整姓名、本站覆盖期刊 ISSN 及正式卷期核对，只展示已核实例，档案仍可能误合并。
引用量采用 OpenAlex 单条关联记录，不合计多个记录；关联记录可能包含正式发表版本，不等于此版本单独引用。缺失不视为零，引用指标不代表论文质量。
关注作者按书目中的完整姓名匹配，同名和姓名变体需人工辨别。相关论文先按共享细分方向和具体方法排列，再参考通用方法与发表日期。
分类依据与纠错
AI 根据所提供的论文材料归类：摘要讨论弹性网松弛、KKT条件与高维变量选择等统计方法学内容，未涉及所给因果推断、面板、时间序列、资产定价等具体领域细分，故不选择。
反馈分类问题
正在核对关联版本…
代码、数据与补充材料
正在核对对应版本的资源…
关注这篇论文的作者
Renet：通过动态目标选择为弹性网提供有原则且高效的松弛方法
- 研究问题
- 摘要称，标准弹性网因收缩偏差常导致预测精度次优，而现有松弛实现依赖惩罚解与非惩罚解的朴素线性插值，忽略正则化路径的非线性几何并可能违反KKT条件；如何为弹性网族构建更有原则的松弛方法。
- 方法与思路
- 作者提出Renet，通过自适应松弛过程强制符号一致性，在凸混合与高效子路径重拟合之间动态切换；并给出含超高维情形自动稳定性保障的理论框架，配合自适应求解器后端。
- 材料陈述的贡献
- 摘要称Renet是Relaxed Lasso到弹性网族的推广，形式化了松弛与“一倍标准误”规则的协同，使1-SE规则的稀疏性不牺牲预测保真度；在20个合成与真实数据集基准上一致优于标准弹性网，在高维、低信噪比与高共线性情形下比自适应弹性网更稳健。
- 阅读关注点
- 应核对符号一致性的具体定义与保证条件、凸混合与子路径重拟合的切换准则、稳定性保障所依赖的假设、基准数据集的构造与调参协议，以及计算效率比较是否在同等条件下进行。
- 阅读边界
- 仅依据arXiv预印本摘要，未读全文；不能据此判断理论假设是否成立、基准结果是否可复现，也不能声称已通过同行评审或期刊接收。
原文摘要
We introduce Renet, a principled generalization of the Relaxed Lasso to the Elastic Net family of estimators. While, on the one hand, $\ell_1$-regularization is a standard tool for variable selection in high-dimensional regimes and, on the other hand, the $\ell_2$ penalty provides stability and solution uniqueness through strict convexity, the standard Elastic Net nevertheless suffers from shrinkage bias that frequently yields suboptimal prediction accuracy. We propose to address this limitation through a framework called \textit{relaxation}. Existing relaxation implementations rely on naive linear interpolations of penalized and unpenalized solutions, which ignore the non-linear geometry that characterizes the entire regularization path and risk violating the Karush-Kuhn-Tucker conditions. Renet addresses these limitations by enforcing sign consistency through an adaptive relaxation procedure that dynamically dispatches between convex blending and efficient sub-path refitting. Furthermore, we identify and formalize a unique synergy between relaxation and the ``One-Standard-Error'' rule: relaxation serves as a robust debiasing mechanism, allowing practitioners to leverage the parsimony of the 1-SE rule without the traditional loss in predictive fidelity. Our theoretical framework incorporates automated stability safeguards for ultra-high dimensional regimes and is supported by a comprehensive benchmarking suite across 20 synthetic and real-world datasets, demonstrating that Renet consistently outperforms the standard Elastic Net and provides a more robust alternative to the Adaptive Elastic Net in high-dimensional, low signal-to-noise ratio and high-multicollinearity regimes. By leveraging an adaptive solver backend, Renet delivers these statistical gains while offering a computational profile that remains competitive with state-of-the-art coordinate descent implementations.