% Key papers for Kernel ridge regression analysis (scime atlas, https://atlas.scime.ai). Authors are not included: add them from the paper pages.

@inproceedings{neurips2017_generalization_56338,
  title = {{Generalization Properties of Learning with Random Features}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2017},
  url = {https://nips.cc/virtual/2017/poster/9106},
}

@inproceedings{icml2017_sketched_36395,
  title = {{Sketched Ridge Regression: Optimization Perspective, Statistical Perspective, and Model Averaging}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2017},
  url = {https://icml.cc/virtual/2017/poster/486},
}

@inproceedings{neurips2020_a_59299,
  title = {{A random matrix analysis of random Fourier features: beyond the Gaussian kernel, a precise phase transition, and the corresponding double descent}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2020},
  url = {https://nips.cc/virtual/2020/poster/17126},
}

@inproceedings{neurips2020_kernel_60124,
  title = {{Kernel Alignment Risk Estimator: Risk Prediction from Training Data}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2020},
  url = {https://nips.cc/virtual/2020/poster/18527},
}

@inproceedings{icml2018_an_36514,
  title = {{An Iterative, Sketching-based Framework for Ridge Regression}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2018},
  url = {https://icml.cc/virtual/2018/poster/1895},
}

@inproceedings{aaai2019_non_101597,
  title = {{Non-Asymptotic Uniform Rates of Consistency for k-NN Regression}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2019},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/4292},
}

@inproceedings{iclr2020_ridge_29513,
  title = {{Ridge Regression: Structure, Cross-Validation, and Sketching}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2020},
  url = {https://openreview.net/forum?id=HklRwaEKwB},
}

@inproceedings{neurips2021_generalization_61676,
  title = {{Generalization Error Rates in Kernel Regression: The Crossover from the Noiseless to Noisy Regime}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2021},
  url = {https://nips.cc/virtual/2021/poster/27554},
}

@inproceedings{neurips2020_on_60483,
  title = {{On the Optimal Weighted \$ell\_2\$ Regularization in Overparameterized Linear Regression}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2020},
  url = {https://nips.cc/virtual/2020/poster/17182},
}

@inproceedings{icml2018_optimal_36903,
  title = {{Optimal Tuning for Divide-and-conquer Kernel Ridge Regression with Massive Data}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2018},
  url = {https://icml.cc/virtual/2018/poster/2205},
}

@inproceedings{iclr2021_effective_30574,
  title = {{Effective Distributed Learning with Random Features: Improved Bounds and Algorithms}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2021},
  url = {https://iclr.cc/virtual/2021/poster/2922},
}

@inproceedings{aaai2019_a_101752,
  title = {{A Sharper Generalization Bound for Divide-and-Conquer Ridge Regression}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2019},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/4467},
}

@inproceedings{icml2020_implicit_38299,
  title = {{Implicit Regularization of Random Feature Models}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2020},
  url = {https://icml.cc/virtual/2020/poster/6454},
}

@inproceedings{iclr2021_dataset_30591,
  title = {{Dataset Meta-Learning from Kernel Ridge-Regression}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2021},
  url = {https://iclr.cc/virtual/2021/poster/3251},
}

@inproceedings{iclr2021_on_29946,
  title = {{On the Universality of the Double Descent Peak in Ridgeless Regression}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2021},
  url = {https://iclr.cc/virtual/2021/poster/2611},
}

@inproceedings{icml2020_one_38565,
  title = {{One-shot Distributed Ridge Regression in High Dimensions}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2020},
  url = {https://icml.cc/virtual/2020/poster/5844},
}

@inproceedings{neurips2021_can_61311,
  title = {{Can we globally optimize cross-validation loss? Quasiconvexity in ridge regression}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2021},
  url = {https://nips.cc/virtual/2021/poster/27987},
}

@inproceedings{neurips2024_provable_72526,
  title = {{Provable and Efficient Dataset Distillation for Kernel Ridge Regression}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2024},
  url = {https://neurips.cc/virtual/2024/poster/94841},
}

@inproceedings{iclr2023_on_33297,
  title = {{On the Saturation Effect of Kernel Ridge Regression}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2023},
  url = {https://iclr.cc/virtual/2023/poster/11902},
}

@inproceedings{icml2022_failure_40531,
  title = {{Failure and success of the spectral bias prediction for Laplace Kernel Ridge Regression: the case of low-dimensional data}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2022},
  url = {https://icml.cc/virtual/2022/poster/16237},
}

@inproceedings{icml2024_optimal_44835,
  title = {{Optimal Ridge Regularization for Out-of-Distribution Prediction}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2024},
  url = {https://icml.cc/virtual/2024/poster/33617},
}

@inproceedings{neurips2024_overfitting_70671,
  title = {{Overfitting Behaviour of Gaussian Kernel Ridgeless Regression: Varying Bandwidth or Dimensionality}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2024},
  url = {https://neurips.cc/virtual/2024/poster/96451},
}

@inproceedings{neurips2024_dimension_71248,
  title = {{Dimension-free deterministic equivalents and scaling laws for random feature regression}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2024},
  url = {https://neurips.cc/virtual/2024/poster/95995},
}

@inproceedings{iclr2024_an_34661,
  title = {{An Agnostic View on the Cost of Overfitting in (Kernel) Ridge Regression}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2024},
  url = {https://iclr.cc/virtual/2024/poster/18413},
}

@inproceedings{neurips2024_a_71489,
  title = {{A Comprehensive Analysis on the Learning Curve in Kernel Ridge Regression}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2024},
  url = {https://neurips.cc/virtual/2024/poster/95771},
}

@inproceedings{neurips2024_optimal_72364,
  title = {{Optimal Rates for Vector-Valued Spectral Regularization Learning Algorithms}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2024},
  url = {https://neurips.cc/virtual/2024/poster/94987},
}

@inproceedings{iclr2024_generalization_33563,
  title = {{Generalization error of spectral algorithms}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2024},
  url = {https://iclr.cc/virtual/2024/poster/19512},
}

@inproceedings{iclr2024_asymptotically_35000,
  title = {{Asymptotically Free Sketched Ridge Ensembles: Risks, Cross-Validation, and Tuning}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2024},
  url = {https://iclr.cc/virtual/2024/poster/18074},
}

@inproceedings{icml2024_characterizing_43826,
  title = {{Characterizing Overfitting in Kernel Ridgeless Regression Through the Eigenspectrum}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2024},
  url = {https://icml.cc/virtual/2024/poster/34626},
}

@inproceedings{icml2025_distributionally_48843,
  title = {{Distributionally Robust Active Learning for Gaussian Process Regression}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2025},
  url = {https://icml.cc/virtual/2025/poster/43779},
}

@inproceedings{icml2025_no_49133,
  title = {{No Free Lunch from Random Feature Ensembles: Scaling Laws and Near-Optimality Conditions}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2025},
  url = {https://icml.cc/virtual/2025/poster/43492},
}

@inproceedings{icml2026_to_49797,
  title = {{To Grok Grokking: Provable Grokking in Ridge Regression}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2026},
  url = {https://openreview.net/forum?id=5nNNVY8NW4},
}

@inproceedings{icml2026_optimal_51469,
  title = {{Optimal Unconstrained Self-Distillation in Ridge Regression: Strict Improvements, Precise Asymptotics, and One-Shot Tuning}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2026},
  url = {https://openreview.net/forum?id=MdHcU4C4Rm},
}

@inproceedings{icml2026_on_54183,
  title = {{On Uniform Error Bounds for Kernel Regression under Non-Gaussian Noise}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2026},
  url = {https://openreview.net/forum?id=miVCnnDatA},
}

@inproceedings{neurips2024_the_72131,
  title = {{The Reliability of OKRidge Method in Solving Sparse Ridge Regression Problems}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2024},
  url = {https://neurips.cc/virtual/2024/poster/95202},
}

@inproceedings{neurips2024_on_73577,
  title = {{On the Saturation Effects of Spectral Algorithms in Large Dimensions}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2024},
  url = {https://neurips.cc/virtual/2024/poster/93907},
}

@inproceedings{neurips2025_ridge_79181,
  title = {{Ridge Boosting is Both Robust and Efficient}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2025},
  url = {https://openreview.net/forum?id=mf0p4PO7ko},
}

@inproceedings{neurips2025_kernel_79833,
  title = {{Kernel Regression in Structured Non-IID Settings: Theory and Implications for Denoising Score Learning}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2025},
  url = {https://openreview.net/forum?id=tOwP9z1Zde},
}

@inproceedings{iclr2025_prediction_81160,
  title = {{Prediction Risk and Estimation Risk of the Ridgeless Least Squares Estimator under General Assumptions on Regression Errors}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2025},
  url = {https://iclr.cc/virtual/2025/poster/30610},
}