arXiv:2309. 15769v3 Announce Type: replace-cross Abstract: Recent advances in deep learning have highlighted the phenomenon of benign overfitting in overparameterized statistical models, sparking significant interest in understanding its foundations.
By Dennis Shen, Dogyoon Song, Peng Ding, Jasjeet S. Sekhon
arXiv:2608. 02539v1 Announce Type: cross Abstract: We present a simple Gaussian approximation to the finite-sample distribution of the classical ridge regression estimator.
By Jos\'e Luis Montiel Olea, Ryan Strong, Amilcar Velez, Zhuoheng Xu, Haomin Yu
arXiv:2609.38011v1 Announce Type: new
Abstract: Modern machine learning systems are trained on mixtures of data from different domains, and choosing the right mixture can substantially improve downst...
By Diyuan Wu, Lehan Chen, Theodor Misiakiewicz, Marco Mondelli
arXiv:2608. 28564v1 Announce Type: cross Abstract: We study kernel ridge regression under anisotropic Gaussian data, where the input covariance decays as a power law with exponent $\alpha\geq 0$ for polynomial inner-product kernels.
By Lorenzo Rizzi, Arie Wortsman Zurich, Bruno Loureiro
The paper examines whether benign overfitting—where highly overparameterized models still predict well—occurs in equity return prediction. It finds a double‑descent risk curve for ridgeless models and shows that while ridge regularization slightly improves performance, the advantage vanishes at high parameter‑to‑observation ratios. Ultimately, both models fail to beat a simple historical average, indicating that standard equity predictors lack genuine forecasting power even with flexible machine learning methods.
By Hui Guo, Jiawei Huang, Runze Li, Yan Yu
arXiv:2509. 17251v2 Announce Type: replace-cross Abstract: Existing theory suggests that for linear regression problems categorized by capacity and source conditions, gradient descent (GD) is always minimax optimal, while both ridge regression and online stochastic gradient descent (SGD) are polynomially suboptimal for certain categories of such problems.
By Jingfeng Wu, Peter L. Bartlett, Sham M. Kakade, Jason D. Lee, Bin Yu