% Key papers for High-dimensional linear regression theory (scime atlas, https://atlas.scime.ai). Authors are not included: add them from the paper pages.

@inproceedings{cvpr2016_a_11371,
  title = {{A Nonlinear Regression Technique for Manifold Valued Data With Applications to Medical Image Analysis}},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2016},
}

@inproceedings{neurips2016_joint_55800,
  title = {{Joint quantile regression in vector-valued RKHSs}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2016},
  url = {https://nips.cc/virtual/2016/poster/7126},
}

@inproceedings{neurips2019_on_58742,
  title = {{On the number of variables to use in principal component regression}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2019},
  url = {https://nips.cc/virtual/2019/poster/13652},
}

@inproceedings{icml2016_additive_35734,
  title = {{Additive Approximations in High Dimensional Nonparametric Regression via the SALSA}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2016},
  url = {https://proceedings.mlr.press/v48/kandasamy16.html},
}

@inproceedings{neurips2020_dual_59740,
  title = {{Dual Instrumental Variable Regression}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2020},
  url = {https://nips.cc/virtual/2020/poster/16806},
}

@inproceedings{neurips2018_hunting_57213,
  title = {{Hunting for Discriminatory Proxies in Linear Regression Models}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2018},
  url = {https://nips.cc/virtual/2018/poster/11450},
}

@inproceedings{neurips2019_fast_58270,
  title = {{Fast, Provably convergent IRLS Algorithm for p-norm Linear Regression}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2019},
  url = {https://nips.cc/virtual/2019/poster/14340},
}

@inproceedings{icml2018_approximate_36520,
  title = {{Approximate Leave-One-Out for Fast Parameter Tuning in High Dimensions}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2018},
  url = {https://icml.cc/virtual/2018/poster/2473},
}

@inproceedings{neurips2021_parameter_61172,
  title = {{Parameter-free HE-friendly Logistic Regression}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2021},
  url = {https://nips.cc/virtual/2021/poster/27635},
}

@inproceedings{icml2016_no_35917,
  title = {{No penalty no tears: Least squares in high-dimensional linear models}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2016},
  url = {https://proceedings.mlr.press/v48/wange16.html},
}

@inproceedings{neurips2019_minimizers_58604,
  title = {{Minimizers of the Empirical Risk and Risk Monotonicity}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2019},
  url = {https://nips.cc/virtual/2019/poster/13831},
}

@inproceedings{icml2022_a_40192,
  title = {{A new similarity measure for covariate shift with applications to nonparametric regression}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2022},
  url = {https://icml.cc/virtual/2022/poster/17403},
}

@inproceedings{icml2021_a_38975,
  title = {{A Precise Performance Analysis of Support Vector Regression}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2021},
  url = {https://icml.cc/virtual/2021/poster/9883},
}

@inproceedings{neurips2020_truncated_61017,
  title = {{Truncated Linear Regression in High Dimensions}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2020},
  url = {https://nips.cc/virtual/2020/poster/18060},
}

@inproceedings{neurips2021_support_61530,
  title = {{Support vector machines and linear regression coincide with very high-dimensional features}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2021},
  url = {https://nips.cc/virtual/2021/poster/27524},
}

@inproceedings{neurips2020_randomized_60666,
  title = {{Randomized tests for high-dimensional regression: A more efficient and powerful solution}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2020},
  url = {https://nips.cc/virtual/2020/poster/18241},
}

@inproceedings{neurips2023_estimating_67524,
  title = {{Estimating Koopman operators with sketching to provably learn large scale dynamical systems}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2023},
  url = {https://nips.cc/virtual/2023/poster/72213},
}

@inproceedings{icml2020_subspace_38820,
  title = {{Subspace Fitting Meets Regression: The Effects of Supervision and Orthonormality Constraints on Double Descent of Generalization Errors}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2020},
  url = {https://icml.cc/virtual/2020/poster/6741},
}

@inproceedings{neurips2019_first_58284,
  title = {{First order expansion of convex regularized estimators}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2019},
  url = {https://nips.cc/virtual/2019/poster/13520},
}

@inproceedings{neurips2022_the_63843,
  title = {{The Power and Limitation of Pretraining-Finetuning for Linear Regression under Covariate Shift}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2022},
  url = {https://nips.cc/virtual/2022/poster/52894},
}

@inproceedings{iclr2024_flat_35234,
  title = {{Flat Minima in Linear Estimation and an Extended Gauss Markov Theorem}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2024},
  url = {https://iclr.cc/virtual/2024/poster/17839},
}

@inproceedings{icml2024_random_43716,
  title = {{Random features models: a way to study the success of naive imputation}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2024},
  url = {https://icml.cc/virtual/2024/poster/34736},
}

@inproceedings{icml2024_unveiling_44690,
  title = {{Unveiling the Cycloid Trajectory of EM Iterations in Mixed Linear Regression}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2024},
  url = {https://icml.cc/virtual/2024/poster/33762},
}

@inproceedings{icml2024_minimum_44738,
  title = {{Minimum-Norm Interpolation Under Covariate Shift}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2024},
  url = {https://icml.cc/virtual/2024/poster/33714},
}

@inproceedings{icml2025_spurious_46526,
  title = {{Spurious Correlations in High Dimensional Regression: The Roles of Regularization, Simplicity Bias and Over-Parameterization}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2025},
  url = {https://icml.cc/virtual/2025/poster/46042},
}

@inproceedings{icml2026_scaling_51419,
  title = {{Scaling Laws for Precision in High-Dimensional Linear Regression}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2026},
  url = {https://openreview.net/forum?id=LyhBIrNBXv},
}

@inproceedings{neurips2024_distributional_73006,
  title = {{Distributional regression: CRPS-error bounds for model fitting, model selection and convex aggregation}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2024},
  url = {https://neurips.cc/virtual/2024/poster/94415},
}

@inproceedings{neurips2025_beyond_74903,
  title = {{Beyond Least Squares: Uniform Approximation and the Hidden Cost of Misspecification}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2025},
  url = {https://openreview.net/forum?id=2T2zMiqcY6},
}

@inproceedings{neurips2025_optimal_78743,
  title = {{Optimal Nuisance Function Tuning for Estimating a Doubly Robust Functional under Proportional Asymptotics}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2025},
  url = {https://openreview.net/forum?id=htN3xhUpjD},
}

@inproceedings{iclr2025_optimality_82149,
  title = {{Optimality and Adaptivity of Deep Neural Features for Instrumental Variable Regression}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2025},
  url = {https://iclr.cc/virtual/2025/poster/29643},
}

@inproceedings{iclr2026_random_84307,
  title = {{Random-projection ensemble dimension reduction}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2026},
  url = {https://openreview.net/forum?id=1Cl84lwpdv},
}

@inproceedings{neurips2026_characterizing_92645,
  title = {{Characterizing the Generalization Error of Random Feature Regression with Arbitrary Data-Augmentation}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2026},
  url = {https://neurips.cc/virtual/2026/poster/153550},
}

@inproceedings{icml2024_regression_43404,
  title = {{Regression Learning with Limited Observations of Multivariate Outcomes and Features}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2024},
  url = {https://icml.cc/virtual/2024/poster/35048},
}

@inproceedings{icml2024_no_44198,
  title = {{No Double Descent in Principal Component Regression: A High-Dimensional Analysis}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2024},
  url = {https://icml.cc/virtual/2024/poster/34254},
}

@inproceedings{icml2024_towards_44957,
  title = {{Towards Theoretical Understanding of Learning Large-scale Dependent Data via Random Features}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2024},
  url = {https://icml.cc/virtual/2024/poster/33495},
}

@inproceedings{icml2026_one_52608,
  title = {{One-Shot Weighted Ensemble Estimation for Federated Quantile Regression: Optimal Statistical Guarantees under Heterogeneous Structured Data}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2026},
  url = {https://openreview.net/forum?id=XZPqbzKVN0},
}

@inproceedings{icml2026_deep_52820,
  title = {{Deep Neural Network Regression with Functional Covariates}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2026},
  url = {https://openreview.net/forum?id=ZL4hmM496P},
}

@inproceedings{icml2026_signal_53340,
  title = {{Signal Strength Estimation in Logistic Regression Using Data Splitting}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2026},
  url = {https://openreview.net/forum?id=ebRDSATK4K},
}

@inproceedings{iclr2026_covariate_84340,
  title = {{Covariate-Guided Clusterwise Linear Regression for Generalization to Unseen Data}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2026},
  url = {https://openreview.net/forum?id=1XowCDuqSM},
}

@inproceedings{neurips2026_riemannian_90768,
  title = {{Riemannian Ordinary Least Squares}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2026},
  url = {https://neurips.cc/virtual/2026/poster/152577},
}