mlsanityData to 7 Oct 2026

Benign overfitting and adaptive nonparametric regression

arXiv 2206.13347 ↗First posted 27 Jun 2022math.ST cs.LG stat.MLcited by 3

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Abstract (arXiv)

In the nonparametric regression setting, we construct an estimator which is a continuous function interpolating the data points with high probability, while attaining minimax optimal rates under mean squared risk on the scale of H"older classes adaptively to the unknown smoothness.

Key facts. Cited by 3 papers in the map (data through 7 Oct 2026). Builds on 11 of its 34 references that are in the map. First posted on arXiv 27 Jun 2022.

Builds on

11 of 34 references.

References · 11

  1. 2018Reconciling modern machine learning practice and the bias-variance trade-offcited by 919
  2. 2019Benign Overfitting in Linear Regressioncited by 544
  3. 2018Just Interpolate: Kernel "Ridgeless" Regression Can Generalizecited by 190
  4. 2018To understand deep learning we need to understand kernel learningcited by 186
  5. 2020Benign overfitting in ridge regressioncited by 152
  6. 2021Fit without fear: remarkable mathematical phenomena of deep learning through the prism of interpolationcited by 130
  7. 2019Harmless interpolation of noisy data in regressioncited by 121
  8. 2018Overfitting or perfect fitting? Risk bounds for classification and regression rules that interpolatecited by 99
  9. 2018Does data interpolation contradict statistical optimality?cited by 95
  10. 2018Consistency of Interpolation with Laplace Kernels is a High-Dimensional Phenomenoncited by 44
  11. 2020Failures of model-dependent generalization bounds for least-norm interpolationcited by 26

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2022: 1; 2023: 0; 2024: 0; 2025: 0; 2026: 2.

3 papers.

  1. 2022A Universal Trade-off Between the Model Size, Test Loss, and Training Loss of Linear Predictorscited by 7
  2. 2026On damage of interpolation to adversarial robustness in regressioncited by 1
  3. 2026When Kernel Ridge Regression Meets the H"older-Zygmund Class: Minimax Optimality and Failure of Propernesscited by 0