On the public citation graph

Just interpolate: Kernel “Ridgeless” regression can generalize

https://doi.org/10.1214/19-aos1849
0 signed · 121 inferred · view as graph

Inferred edges come from two machine sources: sweeps of open repository metadata (mostly SSRN and Zenodo), and a bulk OpenAlex citation snapshot. Every row below is labelled with the one it came from. Neither is a complete citation index — absence here is not evidence of absence.

Is this your work? Sign in with ORCID and put your name on the claims that are yours.
Claim with ORCID
Reference health. See whether every reference in this work resolves to a known work and none has been retracted — and get a dated CiteStamped badge you can embed.
See reference health

What this work says

Outgoing claims: the papers this work cites, builds on, or argues with.

inferredcites →https://doi.org/10.1007/11503415_20openalex-snapshot
Exponential Convergence Rates in Classification
inferredcites →https://doi.org/10.1007/3-540-44581-1_15openalex-snapshot
Rademacher and Gaussian Complexities: Risk Bounds and Structural Results
inferredcites →https://doi.org/10.1007/978-3-642-41136-6_11openalex-snapshot
Kernel Ridge Regression
inferredcites →https://doi.org/10.1007/b97848openalex-snapshot
A Distribution-Free Theory of Nonparametric Regression
inferredcites →https://doi.org/10.1007/bf00353874openalex-snapshot
On the limit of the largest eigenvalue of the large dimensional sample covariance matrix
inferredcites →https://doi.org/10.1007/bf00889887openalex-snapshot
The origins of kriging
inferredcites →https://doi.org/10.1007/s00365-006-0663-2openalex-snapshot
On Early Stopping in Gradient Descent Learning
inferredcites →https://doi.org/10.1007/s10208-004-0134-1openalex-snapshot
Model Selection for Regularized Least-Squares Algorithm in Learning Theory
inferredcites →https://doi.org/10.1007/s10208-006-0196-8openalex-snapshot
Optimal Rates for the Regularized Least-Squares Algorithm
inferredcites →https://doi.org/10.1007/s102080010030openalex-snapshot
Best Choices for Regularization Parameters in Learning Theory: On the Bias—Variance Problem
inferredcites →https://doi.org/10.1017/cbo9780511809682openalex-snapshot
Kernel Methods for Pattern Analysis
inferredcites →https://doi.org/10.1023/a:1018946025316openalex-snapshot
Regularization Networks and Support Vector Machines
inferredcites →https://doi.org/10.1080/00401706.1979.10489751openalex-snapshot
Generalized Cross-Validation as a Method for Choosing a Good Ridge Parameter
inferredcites →https://doi.org/10.1080/01621459.2020.1745812openalex-snapshot
Training Neural Networks as Learning Data-adaptive Kernels: Provable Representation and Approximation Benefits
inferredcites →https://doi.org/10.1109/ita.2018.8503198openalex-snapshot
Implicit Regularization in Matrix Factorization
inferredcites →https://doi.org/10.1137/1.9781611970128openalex-snapshot
Spline Models for Observational Data
inferredcites →https://doi.org/10.1214/08-aos648openalex-snapshot
The spectrum of kernel random matrices
inferredcites →https://doi.org/10.48550/arxiv.1201.0490openalex-snapshot
Scikit-learn: Machine Learning in Python
inferredcites →https://doi.org/10.48550/arxiv.1412.6614openalex-snapshot
In Search of the Real Inductive Bias: On the Role of Implicit Regularization in Deep Learning
inferredcites →https://doi.org/10.48550/arxiv.1611.03530openalex-snapshot
Understanding deep learning requires rethinking generalization
inferredcites →https://doi.org/10.48550/arxiv.1801.03437openalex-snapshot
Approximation beats concentration? An approximation view on inference with smooth radial kernels
inferredcites →https://doi.org/10.48550/arxiv.1802.01396openalex-snapshot
To understand deep learning we need to understand kernel learning
inferredcites →https://doi.org/10.48550/arxiv.1806.05161openalex-snapshot
Overfitting or perfect fitting? Risk bounds for classification and regression rules that interpolate

What points at this work

Incoming claims: where this work appears in other papers’ reference lists and arguments.

inferred← citeshttps://doi.org/10.2139/ssrn.4609871openalex-extracted
Double and Single Descent in Causal Inference with an Application to High-Dimensional Synthetic Control
inferred← citeshttps://doi.org/10.1002/cpa.22008openalex-snapshot
The Generalization Error of Random Features Regression: Precise Asymptotics and the Double Descent Curve
inferred← citeshttps://doi.org/10.1007/978-3-030-86523-8_6openalex-snapshot
Which Minimizer Does My Neural Network Converge To?
inferred← citeshttps://doi.org/10.1007/978-3-030-95860-2_6openalex-snapshot
Regularization in Reproducing Kernel Hilbert Spaces
inferred← citeshttps://doi.org/10.1007/978-3-030-95860-2_8openalex-snapshot
Regularization for Nonlinear System Identification
inferred← citeshttps://doi.org/10.1007/978-981-16-8233-9_9openalex-snapshot
Reflecting on the Role of Overparameterization: Is it Solely Harmful?
inferred← citeshttps://doi.org/10.1007/978-981-95-8110-8_4openalex-snapshot
Scaling Laws of Deep-Learning Neural Networks: Taxonomy and Survey
inferred← citeshttps://doi.org/10.1007/s00440-023-01248-yopenalex-snapshot
Tractability from overparametrization: the example of the negative perceptron
inferred← citeshttps://doi.org/10.1007/s00440-024-01278-0openalex-snapshot
Benign overfitting and adaptive nonparametric regression
inferred← citeshttps://doi.org/10.1007/s00440-024-01336-7openalex-snapshot
A geometrical viewpoint on the benign overfitting property of the minimum $$\ell _2$$-norm interpolant estimator and its universality
inferred← citeshttps://doi.org/10.1007/s10589-026-00776-2openalex-snapshot
Effectively leveraging momentum terms in stochastic line search frameworks for fast optimization of finite-sum problems
inferred← citeshttps://doi.org/10.1007/s10994-022-06265-xopenalex-snapshot
SVRG meets AdaGrad: painless variance reduction
inferred← citeshttps://doi.org/10.1007/s10994-024-06692-yopenalex-snapshot
Knoop: practical enhancement of knockoff with over-parameterization for variable selection
inferred← citeshttps://doi.org/10.1007/s42952-025-00352-0openalex-snapshot
Overparametrized linear regression with noisy and missing data
inferred← citeshttps://doi.org/10.1007/s43670-022-00022-wopenalex-snapshot
Learning from non-random data in Hilbert spaces: an optimal recovery perspective
inferred← citeshttps://doi.org/10.1007/s43670-023-00063-9openalex-snapshot
HARFE: hard-ridge random feature expansion
inferred← citeshttps://doi.org/10.1007/s43670-023-00075-5openalex-snapshot
Rates of convergence for regression with the graph poly-Laplacian
inferred← citeshttps://doi.org/10.1016/j.acha.2021.12.003openalex-snapshot
Generalization error of random feature and kernel methods: Hypercontractivity and kernel matrix concentration
inferred← citeshttps://doi.org/10.1016/j.acha.2026.101920openalex-snapshot
Generalization error curves for analytic spectral algorithms under power-law decay
inferred← citeshttps://doi.org/10.1016/j.automatica.2024.111907openalex-snapshot
Deep networks for system identification: A survey
inferred← citeshttps://doi.org/10.1016/j.cmpb.2022.106905openalex-snapshot
Kernel machine learning methods to handle missing responses with complex predictors. Application in modelling five-year glucose changes using distributional representations
inferred← citeshttps://doi.org/10.1016/j.cnsns.2025.109343openalex-snapshot
Solving partial differential equations with random feature models
inferred← citeshttps://doi.org/10.1016/j.csda.2020.107154openalex-snapshot
Communication-efficient distributed estimator for generalized linear models with a diverging number of covariates
inferred← citeshttps://doi.org/10.1016/j.jece.2026.123930openalex-snapshot
Prediction toxicity threshold of herbicides single and combine to submerged plants: A QSAR model with ridge regression
inferred← citeshttps://doi.org/10.1016/j.jfranklin.2024.107311openalex-snapshot
Polynomial-based kernel reproduced gradient descent for stochastic optimization
inferred← citeshttps://doi.org/10.1016/j.neucom.2023.126227openalex-snapshot
Do we really need a new theory to understand over-parameterization?
inferred← citeshttps://doi.org/10.1016/j.neunet.2024.107110openalex-snapshot
Convergence analysis of deep Ritz method with over-parameterization
inferred← citeshttps://doi.org/10.1016/j.physletb.2026.140722openalex-snapshot
Using neural networks to accelerate TALYS-2.0 nuclear reaction simulations
inferred← citeshttps://doi.org/10.1016/j.sigpro.2025.109904openalex-snapshot
Softmax-kernel reproduced gradient descent for stochastic optimization on streaming data
inferred← citeshttps://doi.org/10.1016/j.slads.2026.100026openalex-snapshot
Towards a statistical understanding of neural networks: Beyond the neural tangent kernel theories
inferred← citeshttps://doi.org/10.1016/j.xinn.2026.101427openalex-snapshot
Research Fronts 2025: Mapping the evolving landscape of global science and technology
inferred← citeshttps://doi.org/10.1017/s0962492921000027openalex-snapshot
Deep learning: a statistical viewpoint
inferred← citeshttps://doi.org/10.1017/s0962492921000039openalex-snapshot
Fit without fear: remarkable mathematical phenomena of deep learning through the prism of interpolation
inferred← citeshttps://doi.org/10.1038/s41467-020-14663-9openalex-snapshot
Complexity control by gradient descent in deep networks
inferred← citeshttps://doi.org/10.1038/s41467-021-23103-1openalex-snapshot
Spectral bias and task-model alignment explain generalization in kernel regression and infinitely wide neural networks
inferred← citeshttps://doi.org/10.1038/s42256-021-00345-8openalex-snapshot
Fitting elephants in modern machine learning by statistically consistent interpolation
inferred← citeshttps://doi.org/10.1073/pnas.2103091118openalex-snapshot
Exploring deep neural networks via layer-peeled model: Minority collapse in imbalanced training
inferred← citeshttps://doi.org/10.1073/pnas.2310002121openalex-snapshot
The training process of many deep networks explores the same low-dimensional manifold
inferred← citeshttps://doi.org/10.1080/01621459.2020.1745812openalex-snapshot
Training Neural Networks as Learning Data-adaptive Kernels: Provable Representation and Approximation Benefits
inferred← citeshttps://doi.org/10.1080/01621459.2020.1844210openalex-snapshot
A Multi-resolution Theory for Approximating Infinite-p-Zero-n: Transitional Inference, Individualized Predictions, and a World Without Bias-Variance Tradeoff
inferred← citeshttps://doi.org/10.1080/01621459.2020.1853547openalex-snapshot
Mehler’s Formula, Branching Process, and Compositional Kernels of Deep Neural Networks
inferred← citeshttps://doi.org/10.1080/01621459.2022.2093206openalex-snapshot
Benign Overfitting and Noisy Features
inferred← citeshttps://doi.org/10.1080/01621459.2024.2368844openalex-snapshot
Enhanced Response Envelope via Envelope Regularization
inferred← citeshttps://doi.org/10.1080/02331934.2025.2551250openalex-snapshot
Convergence conditions for stochastic line search based optimization of over-parametrized models
inferred← citeshttps://doi.org/10.1088/1742-5468/ac3a77openalex-snapshot
A random matrix analysis of random Fourier features: beyond the Gaussian kernel, a precise phase transition, and the corresponding double descent*
inferred← citeshttps://doi.org/10.1088/1742-5468/ac98a9openalex-snapshot
On the proliferation of support vectors in high dimensions*
inferred← citeshttps://doi.org/10.1088/1742-5468/ad292aopenalex-snapshot
Six lectures on linearized neural networks
inferred← citeshttps://doi.org/10.1088/1742-5468/ad642aopenalex-snapshot
Learning curves for deep structured Gaussian feature models*
inferred← citeshttps://doi.org/10.1093/biomet/asac049openalex-snapshot
Kernel two-sample tests in high dimensions: interplay between moment discrepancy and dimension-and-sample orders
inferred← citeshttps://doi.org/10.1093/biomet/asad048openalex-snapshot
Kernel interpolation generalizes poorly
inferred← citeshttps://doi.org/10.1093/biomet/asae057openalex-snapshot
The phase diagram of kernel interpolation in large dimensions
inferred← citeshttps://doi.org/10.1093/imaiai/iaad054openalex-snapshot
Conditioning of random Fourier feature matrices: double descent and generalization error
inferred← citeshttps://doi.org/10.1101/2023.12.09.570922openalex-snapshot
AI without networks
inferred← citeshttps://doi.org/10.1103/physreve.106.025304openalex-snapshot
Bias-variance decomposition of overparameterized regression with random linear features
inferred← citeshttps://doi.org/10.1103/physrevresearch.4.013201openalex-snapshot
Memorizing without overfitting: Bias, variance, and interpolation in overparameterized models
inferred← citeshttps://doi.org/10.1103/physrevresearch.7.023119openalex-snapshot
Thermodynamic limit in learning period three
inferred← citeshttps://doi.org/10.1109/ieeeconf53345.2021.9723382openalex-snapshot
Model selection and explainability in neural networks using a polytope interpolation framework
inferred← citeshttps://doi.org/10.1109/isit50566.2022.9834388openalex-snapshot
The Directional Bias Helps Stochastic Gradient Descent to Generalize in Kernel Regression Models
inferred← citeshttps://doi.org/10.1109/isit50566.2022.9834569openalex-snapshot
Regularization-wise double descent: Why it occurs and how to eliminate it
inferred← citeshttps://doi.org/10.1109/tit.2022.3215088openalex-snapshot
On Generalization Bounds for Deep Networks Based on Loss Surface Implicit Regularization
inferred← citeshttps://doi.org/10.1109/tit.2023.3344656openalex-snapshot
Optimal Rates for Agnostic Distributed Learning
inferred← citeshttps://doi.org/10.1109/tit.2025.3531048openalex-snapshot
Generalization Performance of Empirical Risk Minimization on Over-Parameterized Deep ReLU Nets
inferred← citeshttps://doi.org/10.1109/tkde.2023.3270750openalex-snapshot
COLTR: Semi-Supervised Learning to Rank With Co-Training and Over-Parameterization for Web Search
inferred← citeshttps://doi.org/10.1109/tpami.2021.3120183openalex-snapshot
Towards a Unified Quadrature Framework for Large-Scale Kernel Machines
inferred← citeshttps://doi.org/10.1109/tsp.2025.3549591openalex-snapshot
Online Federated Reproduced Gradient Descent With Time-Varying Global Optima
inferred← citeshttps://doi.org/10.1111/rssb.12542openalex-snapshot
Universal Prediction Band via Semi-Definite Programming
inferred← citeshttps://doi.org/10.1137/20m1320031openalex-snapshot
Diversity Sampling is an Implicit Regularization for Kernel Methods
inferred← citeshttps://doi.org/10.1137/22m1499819openalex-snapshot
On the Inconsistency of Kernel Ridgeless Regression in Fixed Dimensions
inferred← citeshttps://doi.org/10.1137/22m1519948openalex-snapshot
Kernel Interpolation of High Dimensional Scattered Data
inferred← citeshttps://doi.org/10.1137/22m1540302openalex-snapshot
A Universal Trade-off Between the Model Size, Test Loss, and Training Loss of Linear Predictors
inferred← citeshttps://doi.org/10.1137/23m1568764openalex-snapshot
New Equivalences between Interpolation and SVMs: Kernels and Structured Features
inferred← citeshttps://doi.org/10.1137/24m1642548openalex-snapshot
Ensemble Linear Interpolators: The Role of Ensembling
inferred← citeshttps://doi.org/10.1142/s0219530522400115openalex-snapshot
For interpolating kernel machines, minimizing the norm of the ERM solution maximizes stability
inferred← citeshttps://doi.org/10.1142/s0219530526500028openalex-snapshot
Sobolev norm inconsistency of kernel interpolation
inferred← citeshttps://doi.org/10.1214/20-aos1990openalex-snapshot
Linearized two-layers neural networks in high dimension
inferred← citeshttps://doi.org/10.1214/21-aos2116openalex-snapshot
Canonical thresholding for nonsparse high-dimensional linear regression
inferred← citeshttps://doi.org/10.1214/21-aos2133openalex-snapshot
Surprises in high-dimensional ridgeless least squares interpolation
inferred← citeshttps://doi.org/10.1214/22-aos2170openalex-snapshot
A precise high-dimensional asymptotic theory for boosting and minimum-ℓ1-norm interpolated classifiers
inferred← citeshttps://doi.org/10.1214/22-aos2190openalex-snapshot
On the robustness of minimum norm interpolators and regularized empirical risk minimizers
inferred← citeshttps://doi.org/10.1214/22-aos2211openalex-snapshot
The interpolation phase transition in neural networks: Memorization and generalization under lazy training
inferred← citeshttps://doi.org/10.1214/22-aos2212openalex-snapshot
A sieve stochastic gradient descent estimator for online nonparametric regression in Sobolev ellipsoids
inferred← citeshttps://doi.org/10.1214/23-aap2010openalex-snapshot
Deformed semicircle law and concentration of nonlinear random matrices for ultra-wide neural networks
inferred← citeshttps://doi.org/10.1214/25-aos2489openalex-snapshot
The generalization error of max-margin linear classifiers: Benign overfitting and high dimensional asymptotics in the overparametrized regime
inferred← citeshttps://doi.org/10.1214/25-aos2532openalex-snapshot
The high-dimensional asymptotics of principal component regression
inferred← citeshttps://doi.org/10.12677/pm.2026.165133openalex-snapshot
Large Adaptive Implicit Regularization and GeneralizationAnalysis for Multi-Kernel Interpolation
inferred← citeshttps://doi.org/10.1561/2200000092openalex-snapshot
A Unifying Tutorial on Approximate Message Passing
inferred← citeshttps://doi.org/10.2139/ssrn.5032413openalex-snapshot
"Double Descent" in Portfolio Optimization: Dance between Theoretical Sharpe Ratio and Estimation Accuracy
inferred← citeshttps://doi.org/10.23919/acc63710.2025.11107619openalex-snapshot
Safe and High-Performance Learning of Model Predictive Control using Kernel-Based Interpolation
inferred← citeshttps://doi.org/10.23939/sisn2026.19.100openalex-snapshot
Study of Regression Model Optimization by Means of Regularization
inferred← citeshttps://doi.org/10.3150/22-bej1473openalex-snapshot
On the robustness of the minimim ℓ2 interpolator
inferred← citeshttps://doi.org/10.3150/25-bej1877openalex-snapshot
Benign overfitting in time-series linear models with over-parameterization
inferred← citeshttps://doi.org/10.3390/math10060993openalex-snapshot
Statistical Methods with Applications in Data Mining: A Review of the Most Recent Works
inferred← citeshttps://doi.org/10.4208/cicp.oa-2023-0101openalex-snapshot
Convergence Analysis of PINNs with Over-Parameterization
inferred← citeshttps://doi.org/10.4208/cicp.oa-2023-0264openalex-snapshot
Convergence Analysis for Over-Parameterized Deep Learning
inferred← citeshttps://doi.org/10.4208/cicp.oa-2025-0102openalex-snapshot
A Complete Error Analysis of PINNs for Elliptic Equations Using Projected Stochastic Gradient Descent
inferred← citeshttps://doi.org/10.70675/24f761cbz67f0z4760zbeb2z340aaddb0a57openalex-snapshot
Topics in high-dimensional and non-parametric inference
inferred← citeshttps://doi.org/10.70675/c6ef8709z9816z43aeza681zd89f9145e79eopenalex-snapshot
When Random Forests Meet Neural Networks : A Finite-Sample Analysis
inferred← citeshttps://doi.org/10.70675/ffef887ez4944z4cb3z82b9zcaa0294ed85aopenalex-snapshot
Contributions to structured high-dimensional inference
Editorial review for independent work. ICSAC — the Institute for Complexity Science and Advanced Computing — runs a structured editorial panel for researchers publishing outside journals, and publishes on CiteStamp rails. Submission is free and goes through ORCID: icsacinstitute.org/submit.