Ridge Regression

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2 papers in the last four weeks, with none the four weeks before. 0.0% of all new papers.

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Latest papers 22

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Gauss-Newton Accuracy and Indefinite Hessians: Uniform Coexistence in Low-Cost Sets
Oct 7, 2026Kihun Rhee, Hanjoon ByunGauss-Newton Optimization Second-Order Optimization
Exact Calibration and Sharp Risk Geometry for Volume-Sampled Ridge Regression
Oct 6, 2026Kihun RheeRidge Regression
The Advantages of Fresh Sketching for Ridge Regression
Sep 29, 2026Linkai Ma, Qilin Li, Petros DrineasImportance Sampling Randomized Sketching
Purdue University · University of Wisconsin-Madison
Structured Features Overfit Where Random Features Grok
Sep 14, 2026Chon-Fai Kam, Miloud Bessafi, Frederic CadetNeural Network Generalization Ridge Regression
University Paris City & University of Reunion, Paris, France; Dipartimento di Fisica e Chimica · Emilio Segr`e, Universit`a degli Studi di Palermo, Italy · EnergyLab, University of Reunion, Saint-Denis, France +2
A Simple Approximation to the Distribution of the Ridge Regression Estimator
Aug 3, 2026José Luis Montiel Olea, Ryan Strong, Amilcar Velez +2Ridge Regression Hyperparameter Optimization
Department of Economics at Cornell University
Beyond Negative-Ridge Endpoints: Mixed-Sign Spectral Regularization via Negative-Shifted Gradient Descent
Jul 24, 2026Peng ZhaoSpectral Regularization Early Stopping
Department of Applied Economics and Statistics · University of Delaware
Regularized Machine Learning for System Identification of Ship Free-Running Manoeuvres from CFD-Based Synthetic Data: A Comparative Study
Jun 15, 2026R. F. Suárez, J. C. Berndt, M. Abdel-MaksoudNonlinear System Identification System Identification
Hamburg University of Technology (TUHH), Institute for Fluid Dynamics and Ship Theory (M8), Am Schwarzenberg-Campus 4 (C), 21073, Hamburg, Germany
IR3DE: A Linear Router for Large Language Models
Jun 4, 2026Eros Fanì, Oğuzhan ErsoyLLM Inference LLM Routing
Gensyn
When New Generators Arrive: Lifelong Machine-Generated Text Attribution via Ridge Feature Transfer
Jun 4, 2026Zhen Sun, Yifan Liao, Zhicong Huang +4Class-Incremental Learning AI-Generated Text Detection
Wuhan University · Ant Group · The Hong Kong University of Science and Technology (Guangzhou) +1
GRKV: Global Regression for Training-Free KV Cache Compression in Long-Context LLMs
May 29, 2026Junjie Peng, You Wu, Haoyi Wu +4KV Caching Long-Context Language Modeling
1Sun Yat-sen University · 2ShanghaiTech University · 3Guangdong Province Key Laboratory of Information Security Technology
Ridge Regression from Poisson Resetting: A Renewal Perspective on Spectral Regularization
May 28, 2026Petar JolakoskiSpectral Regularization Linear Regression
Research Center for Computer Science and Information Technologies, Macedonian Academy of Sciences and Arts, Bul. Krste Misirkov 2, 1000 Skopje, Macedonia
Optimal ridge regularization revisited
May 27, 2026Jack Timmermans, Sergio A. AlvarezLinear Regression Statistical Learning Theory
Department of Computer Science, Boston College, Chestnut Hill, MA 02467 USA
Conditional KRR: Injecting Unpenalized Features into Kernel Methods with Applications to Kernel Thresholding
May 25, 2026Rustem Takhanov, Zhenisbek AssylbekovKernel Ridge Regression Kernel Regression
Department of Mathematics, Nazarbayev University, Astana, Kazakhstan · Nazarbayev University Research Administration, Astana, Kazakhstan · Purdue University Fort Wayne, Indiana, USA.
Canonical Regularisation of Wide Feature-Learning Neural Networks
May 18, 2026George Whittle, Pranav Vaidhyanathan, Juliusz Ziomek +2Kernel Ridge Regression Representation Learning
Department of Engineering Science University of Oxford
Large Dimensional Kernel Ridge Regression: Extending to Product Kernels
May 14, 2026Yang Zhou, Yicheng Li, Yuqian Cheng +1Double Descent Kernel Ridge Regression
Department of Statistics and Data Science Tsinghua University Beijing, 100084, China · Department of Mathematical Science Tsinghua University Beijing, 100084, China
Reframing preprocessing selection as model-internal calibration in near-infrared spectroscopy: A large-scale benchmark of operator-adaptive PLS and Ridge models
May 13, 2026Gregory Beurier, Robin Reiter, Camille Noûs +2Linear Regression Spectral Methods
CIRAD, UMR AGAP Institut, Montpellier, France · UMR AGAP Institut, Univ Montpellier, CIRAD, INRAE, Institut Agro, Montpellier, France · CIRAD, UMR AGAP Institut, F-34398 Montpellier, France +1
Transformers Can Implement Preconditioned Richardson Iteration for In-Context Gaussian Kernel Regression
May 8, 2026Mingsong Yan, Dongyang Li, Charles Kulick +1Softmax Attention Transformer Interpretability
Department of Mathematics, University of California, Santa Barbara, CA
Differentiable Kernel Ridge Regression for Deep Learning Pipelines
May 4, 2026Jean-Marc Mercier, Gabriele SantinKernel Ridge Regression Kernel Regression
1MPG-Partners, Paris, France · Department of Environmental Sciences, Informatics and Statistics, Ca’ Foscari University of Venice, Italy
Pack only the essentials: Adaptive dictionary learning for kernel ridge regression
Apr 24, 2026Daniele Calandriello, Alessandro Lazaric, Michal ValkoKernel Ridge Regression Kernel Regression
SequeL team, INRIA Lille - Nord Europe, France
Analysis of Nystrom method with sequential ridge leverage scores
Apr 22, 2026Daniele Calandriello, Alessandro Lazaric, Michal ValkoKernel Ridge Regression Kernel Regression
SequeL team INRIA Lille - Nord Europe
To Grok Grokking: Provable Grokking in Ridge Regression
Jan 27, 2026Mingyue Xu, Gal Vardi, Itay SafranNeural Network Generalization Ridge Regression
Department of Computer Science, Purdue University, West Lafayette, IN, USA · Department of Computer Science and Applied Mathematics, Weizmann Institute of Science, Israel · Stein Faculty of Computer and Information Science, Ben-Gurion University of the Negev, Israel
On Regularization via Early Stopping for Least Squares Regression
Jun 6, 2024Rishi Sonthalia, Jackie Lok, Elizaveta RebrovaEarly Stopping Linear Regression
Department of Mathematics, Boston College · ORFE Department, Princeton University

Oct 7, 2026, cs.LGJ/K move · Enter open · S save

Gauss-Newton Accuracy and Indefinite Hessians: Uniform Coexistence in Low-Cost Sets

Kihun Rhee, Hanjoon Byun

Gauss-Newton Optimization Second-Order Optimization Nonconvex Optimization Ridge Regression

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We study the accuracy of Gauss-Newton curvature in ridge-regularized nonlinear least squares. Under local regularity and persistence of level-set curvature magnitude along an exact-fit section, we prove uniform coexistence of two curvature regimes. Global minimizers exist, and every global minimizer has relative Hessian error below

(1+\sqrt2)/8

, while the same low-cost set contains a point with an indefinite Hessian and relative error at least

15/8

. One positive ridge cap works for all independent center and label perturbations in fixed neighborhoods and every positive ridge weight up to the cap. These neighborhoods do not shrink as the ridge weight tends to zero. A pointwise certificate based on the current prediction level set controls the normal, mixed, and tangent parts of the Hessian correction. We prove a sharp relative-error bound over the stated pointwise class when the prediction map and ridge vary. Analytic examples describe the roles of output alignment, curvature orientation, and persistence. A separate structural result gives full Jacobian row rank throughout low-cost sets and exact interpolation near a rank-deficient reference.

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