% Key papers for Sample complexity of ranking and selection (scime atlas, https://atlas.scime.ai). Authors are not included: add them from the paper pages.

@inproceedings{aaai2017_vote_100946,
  title = {{Vote Until Two of You Agree: Mechanisms with Small Distortion and Sample Complexity}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2017},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/10587},
}

@inproceedings{neurips2018_robust_57586,
  title = {{Robust Hypothesis Testing Using Wasserstein Uncertainty Sets}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2018},
  url = {https://nips.cc/virtual/2018/poster/11758},
}

@inproceedings{icml2018_accelerated_36488,
  title = {{Accelerated Spectral Ranking}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2018},
  url = {https://icml.cc/virtual/2018/poster/1999},
}

@inproceedings{neurips2020_statistical_60869,
  title = {{Statistical and Topological Properties of Sliced Probability Divergences}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2020},
  url = {https://nips.cc/virtual/2020/poster/16914},
}

@inproceedings{iclr2021_empirical_30573,
  title = {{Empirical or Invariant Risk Minimization? A Sample Complexity Perspective}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2021},
  url = {https://iclr.cc/virtual/2021/poster/2580},
}

@inproceedings{neurips2019_limiting_58548,
  title = {{Limiting Extrapolation in Linear Approximate Value Iteration}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2019},
  url = {https://nips.cc/virtual/2019/poster/13689},
}

@inproceedings{neurips2016_the_56043,
  title = {{The Power of Adaptivity in Identifying Statistical Alternatives}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2016},
  url = {https://nips.cc/virtual/2016/poster/7284},
}

@inproceedings{neurips2017_optimal_56555,
  title = {{Optimal Sample Complexity of M-wise Data for Top-K Ranking}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2017},
  url = {https://nips.cc/virtual/2017/poster/8959},
}

@inproceedings{neurips2020_sample_60773,
  title = {{Sample Complexity of Uniform Convergence for Multicalibration}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2020},
  url = {https://nips.cc/virtual/2020/poster/16864},
}

@inproceedings{neurips2020_private_60613,
  title = {{Private Identity Testing for High-Dimensional Distributions}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2020},
  url = {https://nips.cc/virtual/2020/poster/16813},
}

@inproceedings{icml2017_learning_36251,
  title = {{Learning Determinantal Point Processes with Moments and Cycles}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2017},
  url = {https://icml.cc/virtual/2017/poster/721},
}

@inproceedings{icml2016_actively_35731,
  title = {{Actively Learning Hemimetrics with Applications to Eliciting User Preferences}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2016},
  url = {https://proceedings.mlr.press/v48/singla16.html},
}

@inproceedings{icml2018_locally_36831,
  title = {{Locally Private Hypothesis Testing}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2018},
  url = {https://icml.cc/virtual/2018/poster/2071},
}

@inproceedings{aaai2018_the_101024,
  title = {{The Complexity of Bribery in Network-Based Rating Systems}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2018},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/11474},
}

@inproceedings{aaai2017_inductive_104435,
  title = {{Inductive Pairwise Ranking: Going Beyond the n log(n) Barrier}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2017},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/10812},
}

@inproceedings{neurips2018_sharp_57622,
  title = {{Sharp Bounds for Generalized Uniformity Testing}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2018},
  url = {https://nips.cc/virtual/2018/poster/11601},
}

@inproceedings{neurips2019_iterative_58437,
  title = {{Iterative Least Trimmed Squares for Mixed Linear Regression}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2019},
  url = {https://nips.cc/virtual/2019/poster/13725},
}

@inproceedings{aaai2022_the_2827,
  title = {{The Complexity of Subelection Isomorphism Problems}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2022},
  url = {https://aaai.org/papers/04991-the-complexity-of-subelection-isomorphism-problems/},
}

@inproceedings{icml2020_learning_38402,
  title = {{Learning the Valuations of a \$k\$-demand Agent}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2020},
  url = {https://icml.cc/virtual/2020/poster/5807},
}

@inproceedings{icml2020_quantum_38663,
  title = {{Quantum Boosting}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2020},
  url = {https://icml.cc/virtual/2020/poster/6610},
}

@inproceedings{icml2024_faster_43910,
  title = {{Faster Maximum Inner Product Search in High Dimensions}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2024},
  url = {https://icml.cc/virtual/2024/poster/34542},
}

@inproceedings{aaai2025_learnability_9719,
  title = {{Learnability of Parameter-Bounded Bayes Nets}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2025},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/33708},
}

@inproceedings{aaai2025_sample_10667,
  title = {{Sample Complexity of Linear Regression Models for Opinion Formation in Networks}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2025},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/33531},
}

@inproceedings{icml2025_the_47555,
  title = {{The Role of Randomness in Stability}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2025},
  url = {https://icml.cc/virtual/2025/poster/45041},
}

@inproceedings{icml2025_sample_48205,
  title = {{Sample-Optimal Agnostic Boosting with Unlabeled Data}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2025},
  url = {https://icml.cc/virtual/2025/poster/44406},
}

@inproceedings{icml2026_learning_50721,
  title = {{Learning to Rank from Incomplete Rankings}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2026},
  url = {https://openreview.net/forum?id=F8R6Z6ty2E},
}

@inproceedings{icml2026_multicalibration_50819,
  title = {{Multicalibration Yields Better Matchings}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2026},
  url = {https://openreview.net/forum?id=G1bzbJtcHD},
}

@inproceedings{icml2026_the_54591,
  title = {{The Interplay Between Interpolation and Aggregation in Regression: Optimal Sample Complexity}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2026},
  url = {https://openreview.net/forum?id=qXlovWytwg},
}

@inproceedings{neurips2024_optimal_72692,
  title = {{Optimal Top-Two Method for Best Arm Identification and Fluid Analysis}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2024},
  url = {https://neurips.cc/virtual/2024/poster/94693},
}

@inproceedings{neurips2024_entropy_72919,
  title = {{Entropy testing and its application to testing Bayesian networks}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2024},
  url = {https://neurips.cc/virtual/2024/poster/94492},
}

@inproceedings{neurips2024_leveraging_73196,
  title = {{Leveraging Drift to Improve Sample Complexity of Variance Exploding Diffusion Models}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2024},
  url = {https://neurips.cc/virtual/2024/poster/94238},
}

@inproceedings{neurips2024_replicable_73645,
  title = {{Replicable Uniformity Testing}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2024},
  url = {https://neurips.cc/virtual/2024/poster/93847},
}

@inproceedings{neurips2024_optimal_74196,
  title = {{Optimal Algorithms for Augmented Testing of Discrete Distributions}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2024},
  url = {https://neurips.cc/virtual/2024/poster/93354},
}

@inproceedings{neurips2024_sample_74299,
  title = {{Sample-Efficient Agnostic Boosting}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2024},
  url = {https://neurips.cc/virtual/2024/poster/93259},
}

@inproceedings{neurips2024_the_74619,
  title = {{The Many Faces of Optimal Weak-to-Strong Learning}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2024},
  url = {https://neurips.cc/virtual/2024/poster/92972},
}

@inproceedings{neurips2025_revisiting_78022,
  title = {{Revisiting Agnostic Boosting}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2025},
  url = {https://openreview.net/forum?id=aFf30XJpl4},
}

@inproceedings{neurips2025_sample_78621,
  title = {{Sample-Adaptivity Tradeoff in On-Demand Sampling}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2025},
  url = {https://openreview.net/forum?id=gaHjGx1cMh},
}

@inproceedings{neurips2025_improved_79448,
  title = {{Improved Bounds for Swap Multicalibration and Swap Omniprediction}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2025},
  url = {https://openreview.net/forum?id=pXoiIDdynI},
}

@inproceedings{neurips2025_replicable_79553,
  title = {{Replicable Distribution Testing}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2025},
  url = {https://openreview.net/forum?id=qYDBgSeAlU},
}

@inproceedings{iclr2025_how_81510,
  title = {{How to Verify Any (Reasonable) Distribution Property: Computationally Sound Argument Systems for Distributions}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2025},
  url = {https://iclr.cc/virtual/2025/poster/30266},
}