% Key papers for Minimax estimation rates (scime atlas, https://atlas.scime.ai). Authors are not included: add them from the paper pages.

@inproceedings{aaai2020_stable_102162,
  title = {{Stable Learning via Sample Reweighting}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2020},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/6024},
}

@inproceedings{icml2016_stochastically_35994,
  title = {{Stochastically Transitive Models for Pairwise Comparisons: Statistical and Computational Issues}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2016},
  url = {https://proceedings.mlr.press/v48/shahb16.html},
}

@inproceedings{neurips2016_interaction_55794,
  title = {{Interaction Screening: Efficient and Sample-Optimal Learning of Ising Models}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2016},
  url = {https://nips.cc/virtual/2016/poster/6989},
}

@inproceedings{aaai2018_learning_102460,
  title = {{Learning Mixtures of Random Utility Models}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2018},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/11727},
}

@inproceedings{neurips2019_characterizing_57992,
  title = {{Characterizing the Exact Behaviors of Temporal Difference Learning Algorithms Using Markov Jump Linear System Theory}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2019},
  url = {https://nips.cc/virtual/2019/poster/13909},
}

@inproceedings{neurips2019_pac_58781,
  title = {{PAC-Bayes under potentially heavy tails}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2019},
  url = {https://nips.cc/virtual/2019/poster/13460},
}

@inproceedings{neurips2016_multivariate_55886,
  title = {{Multivariate tests of association based on univariate tests}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2016},
  url = {https://nips.cc/virtual/2016/poster/7095},
}

@inproceedings{icml2019_maximum_37535,
  title = {{Maximum Likelihood Estimation for Learning Populations of Parameters}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2019},
  url = {https://icml.cc/virtual/2019/poster/4310},
}

@inproceedings{icml2019_on_37613,
  title = {{On the statistical rate of nonlinear recovery in generative models with heavy-tailed data}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2019},
  url = {https://icml.cc/virtual/2019/poster/3990},
}

@inproceedings{icml2016_square_35985,
  title = {{Square Root Graphical Models: Multivariate Generalizations of Univariate Exponential Families that Permit Positive Dependencies}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2016},
  url = {https://proceedings.mlr.press/v48/inouye16.html},
}

@inproceedings{cvpr2018_multi_13406,
  title = {{Multi-Task Learning by Maximizing Statistical Dependence}},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2018},
  url = {https://openaccess.thecvf.com/content_cvpr_2018/html/Mejjati_Multi-Task_Learning_by_CVPR_2018_paper.html},
}

@inproceedings{icml2016_learning_35882,
  title = {{Learning Mixtures of Plackett-Luce Models}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2016},
  url = {https://proceedings.mlr.press/v48/zhaob16.html},
}

@inproceedings{icml2016_parameter_35944,
  title = {{Parameter Estimation for Generalized Thurstone Choice Models}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2016},
  url = {https://proceedings.mlr.press/v48/vojnovic16.html},
}

@inproceedings{neurips2019_globally_58341,
  title = {{Globally optimal score-based learning of directed acyclic graphs in high-dimensions}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2019},
  url = {https://nips.cc/virtual/2019/poster/13599},
}

@inproceedings{icml2019_fast_37360,
  title = {{Fast Rates for a kNN Classifier Robust to Unknown Asymmetric Label Noise}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2019},
  url = {https://icml.cc/virtual/2019/poster/3851},
}

@inproceedings{iclr2020_geometric_29623,
  title = {{Geometric Insights into the Convergence of Nonlinear TD Learning}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2020},
  url = {https://openreview.net/forum?id=SJezGp4YPr},
}

@inproceedings{neurips2019_rethinking_58909,
  title = {{Rethinking Generative Mode Coverage: A Pointwise Guaranteed Approach}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2019},
  url = {https://nips.cc/virtual/2019/poster/13411},
}

@inproceedings{neurips2022_convergence_65509,
  title = {{Convergence for score-based generative modeling with polynomial complexity}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2022},
  url = {https://nips.cc/virtual/2022/poster/53863},
}

@inproceedings{aaai2020_sequential_102156,
  title = {{Sequential Mode Estimation with Oracle Queries}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2020},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/6018},
}

@inproceedings{aaai2018_relational_103366,
  title = {{Relational Marginal Problems: Theory and Estimation}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2018},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/12113},
}

@inproceedings{neurips2024_stopping_72995,
  title = {{Stopping Bayesian Optimization with Probabilistic Regret Bounds}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2024},
  url = {https://neurips.cc/virtual/2024/poster/94425},
}

@inproceedings{aaai2024_efficient_5800,
  title = {{Efficient Algorithms for Non-gaussian Single Index Models with Generative Priors}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2024},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/29014},
}

@inproceedings{neurips2024_gaussian_72205,
  title = {{Gaussian Approximation and Multiplier Bootstrap for Polyak-Ruppert Averaged Linear Stochastic Approximation with Applications to TD Learning}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2024},
  url = {https://neurips.cc/virtual/2024/poster/95136},
}

@inproceedings{icml2024_statistically_43399,
  title = {{Statistically Optimal Generative Modeling with Maximum Deviation from the Empirical Distribution}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2024},
  url = {https://icml.cc/virtual/2024/poster/35053},
}

@inproceedings{icml2025_phase_49032,
  title = {{Phase transitions for the existence of unregularized M-estimators in single index models}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2025},
  url = {https://icml.cc/virtual/2025/poster/43592},
}

@inproceedings{icml2024_a_43805,
  title = {{A Fine-grained Analysis of Fitted Q-evaluation: Beyond Parametric Models}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2024},
  url = {https://icml.cc/virtual/2024/poster/34647},
}

@inproceedings{icml2024_sharp_43817,
  title = {{Sharp Rates in Dependent Learning Theory: Avoiding Sample Size Deflation for the Square Loss}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2024},
  url = {https://icml.cc/virtual/2024/poster/34635},
}

@inproceedings{icml2024_gibbs_45087,
  title = {{Gibbs Sampling of Continuous Potentials on a Quantum Computer}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2024},
  url = {https://icml.cc/virtual/2024/poster/33365},
}

@inproceedings{icml2025_the_45994,
  title = {{The Empirical Mean is Minimax Optimal for Local Glivenko-Cantelli}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2025},
  url = {https://icml.cc/virtual/2025/poster/46552},
}

@inproceedings{icml2025_learning_46751,
  title = {{Learning Parametric Distributions from Samples and Preferences}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2025},
  url = {https://icml.cc/virtual/2025/poster/45822},
}

@inproceedings{icml2025_probabilistic_47304,
  title = {{Probabilistic Factorial Experimental Design for Combinatorial Interventions}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2025},
  url = {https://icml.cc/virtual/2025/poster/45285},
}

@inproceedings{icml2025_leveraging_48530,
  title = {{Leveraging Sparsity for Sample-Efficient Preference Learning: A Theoretical Perspective}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2025},
  url = {https://icml.cc/virtual/2025/poster/44091},
}

@inproceedings{icml2026_efficient_49564,
  title = {{Efficient and Minimax Optimal In-context Nonparametric Regression with Transformers}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2026},
  url = {https://openreview.net/forum?id=3hD1gzThtY},
}

@inproceedings{icml2026_near_52216,
  title = {{Near-Optimal and Efficient First-Order Algorithm for Multi-Task Learning with Shared Linear Representation}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2026},
  url = {https://openreview.net/forum?id=TnquAvyTtL},
}

@inproceedings{icml2026_characterization_52279,
  title = {{Characterization of Gaussian Universality Breakdown in High-Dimensional Empirical Risk Minimization}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2026},
  url = {https://openreview.net/forum?id=UHQDfvZBFi},
}

@inproceedings{icml2026_towards_54520,
  title = {{Towards Parameter-Free Temporal Difference Learning}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2026},
  url = {https://openreview.net/forum?id=ppIhZgFCTI},
}

@inproceedings{icml2026_optimal_55021,
  title = {{Optimal structure learning and conditional independence testing}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2026},
  url = {https://openreview.net/forum?id=uurjpxrLc5},
}

@inproceedings{icml2026_gaavi_55122,
  title = {{GAAVI: Global Asymptotic Anytime Valid Inference for the Conditional Mean Function}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2026},
  url = {https://openreview.net/forum?id=vmMAa2HGP9},
}

@inproceedings{neurips2024_non_70166,
  title = {{Non-parametric classification via expand-and-sparsify representation}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2024},
  url = {https://neurips.cc/virtual/2024/poster/96917},
}

@inproceedings{neurips2024_near_72057,
  title = {{Near-Optimality of Contrastive Divergence Algorithms}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2024},
  url = {https://neurips.cc/virtual/2024/poster/95264},
}