Kernel Ridge Regression

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4 papers in the last 28 days · 0.1% of indexed attention

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Period ending 2026-09-21

2 new papers

A weekly snapshot of new work published in Kernel Ridge Regression.

Period ending 2026-09-07

1 new paper

A weekly snapshot of new work published in Kernel Ridge Regression.

47 papers

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Recovering Governing Dynamics from Distributed Observations via Exact Spline Merging
Sep 15, 2026Naveen MysoreB-Spline Sea-Surface Temperature Data
Structured Features Overfit Where Random Features Grok
Sep 14, 2026Chon-Fai Kam, Miloud Bessafi, Frederic CadetOver-Parameterized Neural Networks Overfitting
Structured Extrema Errors in Classical Surrogates for Viscous Burgers: A Physics-Consistent Interpretation
Sep 7, 2026Youssef OubariSurrogate Models High-Fidelity Surrogates
Benchmarking External Generalization of SPD Matrix Learning for Resting-State fMRI Connectome Prediction
Aug 31, 2026Ce Ju, Antoine Collas, Florent Bouchard +1Brain Connectome Modality Spurious Correlations
High-dimensional ridgeless least squares interpolation under spiked covariance structures
Aug 7, 2026Zhijun Liu, Dandan JiangKernel Ridge Regression Covariance Estimation
Align-RAG: Alignment Is All You Need for TSFM In-Context Learning
Aug 6, 2026Mohammad Asadi, Soheil Hor, Bardiya Akhbari +6Time Series Foundation Models Hievi-Rag
Nonparametric Goodness-of-fit Testing under Covariate Shift
Aug 5, 2026Zhen Hou, Dong XiaCovariate Shift Statistical Models
A Simple Approximation to the Distribution of the Ridge Regression Estimator
Aug 3, 2026José Luis Montiel Olea, Ryan Strong, Amilcar Velez +2Kernel Ridge Regression Heteroskedasticity
The Noise Premium in Adversarial Training for Kernel Regression
Jul 30, 2026Yiling Xie, Xiaoming HuoKernel Ridge Regression Adversarial Training
Distributed Convolutional Rank Regression over Decentralized Networks
Jul 26, 2026Chunjing Li, Tiange Zhao, Xiaohui YuanKernel Ridge Regression Low-Rank Factorization
De-floored Principal Component Regression: When Rank Selection Alone Is Insufficient for Prediction
Jul 18, 2026Peng ZhaoPrincipal Component Analysis Kernel Ridge Regression
Learning dynamical systems from noisy data with Weak-form Kernel Ridge Regression
Jun 30, 2026Max Kreider, John Harlim, Daning HuangKernel Ridge Regression Dynamical Systems
Solve for the Hyperparameter, Skip the Search: Kolmogorov-Optimal Scaling Laws for Spline Regression
Jun 22, 2026Yong Yi Bay, Kathleen A. YearickKernel Ridge Regression Hyperparameter Optimization
CLARITree: Cholesky and Lookahead Accelerations for Regression with Interpretable Piecewise Linear Trees
Jun 11, 2026Yixiao Wang, Hayden McTavish, Varun Babbar +2Decision Trees Kernel Ridge Regression
When New Generators Arrive: Lifelong Machine-Generated Text Attribution via Ridge Feature Transfer
Jun 4, 2026Zhen Sun, Yifan Liao, Zhicong Huang +4Text Generation Generators
FFR: Forward-Forward Learning for Regression
Jun 2, 2026Xinyang Liu, Xuanyu Liang, Shiqi Ding +4Backpropagation Single Forward Pass
What Makes a Strong Model? A Unified Spectral Analysis of Knowledge Transfer over High-dimensional Linear Regression
May 31, 2026Wendao Wu, Fangqing Zhang, Haihan Zhang +1Knowledge Distillation Kernel Ridge Regression
Perturbative methods for non-parametric instrumental variable
May 29, 2026Wei Bu, Arthur GrettonKernel Ridge Regression Perturbation Propagation
Ridge Regression from Poisson Resetting: A Renewal Perspective on Spectral Regularization
May 28, 2026Petar JolakoskiKernel Ridge Regression Covariance Estimation
Optimal ridge regularization revisited
May 27, 2026Jack Timmermans, Sergio A. AlvarezKernel Ridge Regression Regularization Techniques
Conditional KRR: Injecting Unpenalized Features into Kernel Methods with Applications to Kernel Thresholding
May 25, 2026Rustem Takhanov, Zhenisbek AssylbekovKernel Ridge Regression Kernel Method
Efficient Benchmarking Is Just Feature Selection and Multiple Regression
May 25, 2026Sam Bowyer, Acyr Locatelli, Kris CaoBenchmarking Large Language Models Relevance
Data-Specific Hyper-Parameter Design: A Paradigm Shift in Reservoir Computing
May 24, 2026G Manjunath, Juan-Pablo Ortega, Alma van der MerweReservoir Computing Reservoir
Diversified Residual Symbolic Regression
May 15, 2026Koki Ikeda, Masahiro Nomura, Ryoki HamanoSymbolic Regression Kernel Ridge Regression
Average Gradient Outer Product in kernel regression provably recovers the central subspace for multi-index models
May 14, 2026Libin Zhu, Damek Davis, Dmitriy Drusvyatskiy +1Kernel Ridge Regression Kernel Method
Large Dimensional Kernel Ridge Regression: Extending to Product Kernels
May 14, 2026Yang Zhou, Yicheng Li, Yuqian Cheng +1Kernel Ridge Regression Kernel Method
On Kernel Eigen-alignments of KRR: Reconstruction and Generalization
May 14, 2026Yang Liu, Ernest Fokoue, Richard Lange +1Kernel Ridge Regression Largest Eigenvalue
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 +2Spectra Kernel Ridge Regression
FePySR: A Neural Feature Extraction Framework for Efficient and Scalable Symbolic Regression
May 12, 2026Zhiming Yu, Wangtao Lu, Xin LaiSymbolic Regression Kernel Ridge Regression
Exact Stiefel Optimization for Probabilistic PLS: Closed-Form Updates, Error Bounds, and Calibrated Uncertainty
May 12, 2026Haoran Hu, Xingce WangLeast Squares Calibrated Uncertainty
Non-Parametric Rehearsal Learning via Conditional Mean Embeddings
May 9, 2026Wen-Bo Du, Tian-Zuo Wang, Han-Jia Ye +1Learning-Augmented Algorithms Uncertainty-Aware Inference
Transformers Can Implement Preconditioned Richardson Iteration for In-Context Gaussian Kernel Regression
May 8, 2026Mingsong Yan, Dongyang Li, Charles Kulick +1Kernel Ridge Regression Transformer Attention
Cubit: Token Mixer with Kernel Ridge Regression
May 7, 2026Chuanyang Zheng, Jiankai Sun, Yihang Gao +6Transformer Attention Sequence Modeling
Differentiable Kernel Ridge Regression for Deep Learning Pipelines
May 4, 2026Jean-Marc Mercier, Gabriele SantinKernel Method Kernel Ridge Regression
A dimensional R2 regression metric
May 1, 2026Jaesung Yoo, Stefan Lemke, Jian Zhong Guo +2Intrinsic Dimensionality Kernel Ridge Regression
Transfer Learning for Tonal Noise Prediction in VRF Units Using Thermodynamic and Vibration Signals
Apr 28, 2026ZhiWei Su, Ding Wang, Yuan Guo +2Vibration Transfer Learning
Learning Curves and Benign Overfitting of Spectral Algorithms in Large Dimensions
Apr 25, 2026Weihao Lu, Qian Lin, Yingcun Xia +1Kernel Ridge Regression Overfitting
Complex Stochastic Gradient Descent and Directional Bias in Reproducing Kernel Hilbert Spaces
Apr 24, 2026Natanael Alpay, Emeric BattagliaKernel Hilbert Spaces Stochastic Gradient Descent
Pack only the essentials: Adaptive dictionary learning for kernel ridge regression
Apr 24, 2026Daniele Calandriello, Alessandro Lazaric, Michal ValkoKernel Ridge Regression Low-Rank Factorization
Analysis of Nystrom method with sequential ridge leverage scores
Apr 22, 2026Daniele Calandriello, Alessandro Lazaric, Michal ValkoKernel Ridge Regression Estimators
Replicable Bandits with UCB based Exploration
Apr 21, 2026Rohan Deb, Udaya Ghai, Karan Singh +1Bandits Replication
To Grok Grokking: Provable Grokking in Ridge Regression
Jan 27, 2026Mingyue Xu, Gal Vardi, Itay SafranOver-Parameterized Neural Networks Kernel Ridge Regression
Learning Multi-Index Models with Hyper-Kernel Ridge Regression
Oct 2, 2025Shuo Huang, Hippolyte Labarrière, Ernesto De Vito +2Kernel Ridge Regression Deep Network
A Compositional Kernel Model for Feature Learning
Sep 17, 2025Feng Ruan, Keli Liu, Michael JordanKernel Ridge Regression Feature Learning
Fixed-Gaussian Spectral Algorithms: Minimax Optimal Rates for Misspecified Learning and Transfer
Jan 18, 2025Haotian Lin, Matthew ReimherrKernel Ridge Regression Transfer Learning
On Regularization via Early Stopping for Least Squares Regression
Jun 6, 2024Rishi Sonthalia, Jackie Lok, Elizaveta RebrovaKernel Ridge Regression Early Stopping
Efficient Cross-Validation for Sparse Linear Regression
Jun 26, 2023Ryan Cory-Wright, Andrés GómezKernel Ridge Regression Lasso

Sep 15, 2026 · cs.LGJ/K move · Enter open · S save

Recovering Governing Dynamics from Distributed Observations via Exact Spline Merging

Naveen Mysore

Scientific observations are frequently distributed across locations, time periods, and institutions. Combining such observations into a continuous, differentiable field enables recovering governing physical parameters from its derivatives. This paper makes two contributions in this setting. First, the established additive structure of fixed-basis ridge-regression statistics is applied to tensor-product spline fields: each data holder computes a local Gram matrix and moment vector, and the merged solution is mathematically identical to centralized fitting, with no raw data shared and no iterative synchronization. This property is specific to the fixed-feature squared-error setting; the present derivation does not establish an analogous guarantee for general jointly trained multilayer networks. Second, a complete pipeline connects distributed observations to physical parameter inference through field reconstruction, derivative extraction, and linear regression. The pipeline is validated on four PDEs: diffusion, wave, heat-with-source, and the nonlinear viscous Burgers equation, recovering governing parameters to sub-percent accuracy in the linear cases and 5% for Burgers. In all cases, distributed merging introduces zero degradation relative to centralized fitting. Application to 41 years of NOAA sea-surface temperature data confirms the result on real spatiotemporal observations. Source code to reproduce all experiments is available at https://github.com/NAVEENMN/gramfield.

B-Spline Sea-Surface Temperature Data Federated Averaging Automatic Differentiation Kernel Ridge Regression Model Merging

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