% Key papers for Robust and surrogate-loss learning bounds (scime atlas, https://atlas.scime.ai). Authors are not included: add them from the paper pages.

@inproceedings{neurips2018_lipschitz_57342,
  title = {{Lipschitz regularity of deep neural networks: analysis and efficient estimation}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2018},
  url = {https://nips.cc/virtual/2018/poster/11382},
}

@inproceedings{neurips2019_certainty_57985,
  title = {{Certainty Equivalence is Efficient for Linear Quadratic Control}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2019},
  url = {https://nips.cc/virtual/2019/poster/14036},
}

@inproceedings{icml2017_analytical_36067,
  title = {{Analytical Guarantees on Numerical Precision of Deep Neural Networks}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2017},
  url = {https://icml.cc/virtual/2017/poster/743},
}

@inproceedings{neurips2019_finite_58280,
  title = {{Finite-Time Performance Bounds and Adaptive Learning Rate Selection for Two Time-Scale Reinforcement Learning}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2019},
  url = {https://nips.cc/virtual/2019/poster/13619},
}

@inproceedings{neurips2020_universally_61049,
  title = {{Universally Quantized Neural Compression}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2020},
  url = {https://nips.cc/virtual/2020/poster/17161},
}

@inproceedings{neurips2018_adversarially_56853,
  title = {{Adversarially Robust Optimization with Gaussian Processes}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2018},
  url = {https://nips.cc/virtual/2018/poster/11561},
}

@inproceedings{neurips2016_active_55558,
  title = {{Active Learning from Imperfect Labelers}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2016},
  url = {https://nips.cc/virtual/2016/poster/6972},
}

@inproceedings{iclr2020_lipschitz_29773,
  title = {{Lipschitz constant estimation of Neural Networks via sparse polynomial optimization}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2020},
  url = {https://openreview.net/forum?id=rJe4_xSFDB},
}

@inproceedings{cvpr2020_robustness_16142,
  title = {{Robustness Guarantees for Deep Neural Networks on Videos}},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2020},
}

@inproceedings{neurips2017_noise_56514,
  title = {{Noise-Tolerant Interactive Learning Using Pairwise Comparisons}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2017},
  url = {https://nips.cc/virtual/2017/poster/9030},
}

@inproceedings{icml2017_diameter_36149,
  title = {{Diameter-Based Active Learning}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2017},
  url = {https://icml.cc/virtual/2017/poster/804},
}

@inproceedings{neurips2020_self_60796,
  title = {{Self-Imitation Learning via Generalized Lower Bound Q-learning}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2020},
  url = {https://nips.cc/virtual/2020/poster/17477},
}

@inproceedings{neurips2022_hidden_64113,
  title = {{Hidden Progress in Deep Learning: SGD Learns Parities Near the Computational Limit}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2022},
  url = {https://nips.cc/virtual/2022/poster/54049},
}

@inproceedings{neurips2020_statistical_60871,
  title = {{Statistical-Query Lower Bounds via Functional Gradients}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2020},
  url = {https://nips.cc/virtual/2020/poster/18234},
}

@inproceedings{icml2024_watermarks_44807,
  title = {{Watermarks in the Sand: Impossibility of Strong Watermarking for Language Models}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2024},
  url = {https://icml.cc/virtual/2024/poster/33645},
}

@inproceedings{neurips2021_towards_61688,
  title = {{Towards a Unified Information-Theoretic Framework for Generalization}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2021},
  url = {https://nips.cc/virtual/2021/poster/26524},
}

@inproceedings{icml2017_boosted_36086,
  title = {{Boosted Fitted Q-Iteration}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2017},
  url = {https://icml.cc/virtual/2017/poster/676},
}

@inproceedings{neurips2022_hardness_64470,
  title = {{Hardness of Noise-Free Learning for Two-Hidden-Layer Neural Networks}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2022},
  url = {https://nips.cc/virtual/2022/poster/54682},
}

@inproceedings{aaai2021_norm_1072,
  title = {{Norm-Based Generalisation Bounds for Deep Multi-Class Convolutional Neural Networks}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2021},
  url = {https://aaai.org/papers/08279-norm-based-generalisation-bounds-for-deep-multi-class-convolutional-neural-networks/},
}

@inproceedings{cvpr2020_an_15143,
  title = {{An Internal Covariate Shift Bounding Algorithm for Deep Neural Networks by Unitizing Layers' Outputs}},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2020},
}

@inproceedings{cvpr2024_towards_25040,
  title = {{Towards General Robustness Verification of MaxPool-based Convolutional Neural Networks via Tightening Linear Approximation}},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2024},
  url = {https://cvpr.thecvf.com/virtual/2024/poster/30195},
}

@inproceedings{iclr2024_some_33658,
  title = {{Some Fundamental Aspects about Lipschitz Continuity of Neural Networks}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2024},
  url = {https://iclr.cc/virtual/2024/poster/19417},
}

@inproceedings{iclr2024_estimating_35117,
  title = {{Estimating Shape Distances on Neural Representations with Limited Samples}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2024},
  url = {https://iclr.cc/virtual/2024/poster/17956},
}

@inproceedings{icml2024_bottleneck_44204,
  title = {{Bottleneck-Minimal Indexing for Generative Document Retrieval}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2024},
  url = {https://icml.cc/virtual/2024/poster/34248},
}

@inproceedings{neurips2024_efficient_73906,
  title = {{Efficient Discrepancy Testing for Learning with Distribution Shift}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2024},
  url = {https://neurips.cc/virtual/2024/poster/93605},
}

@inproceedings{icml2024_uniformly_45374,
  title = {{Uniformly Stable Algorithms for Adversarial Training and Beyond}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2024},
  url = {https://icml.cc/virtual/2024/poster/33077},
}

@inproceedings{icml2024_defining_45463,
  title = {{Defining Neural Network Architecture through Polytope Structures of Datasets}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2024},
  url = {https://icml.cc/virtual/2024/poster/32988},
}

@inproceedings{icml2026_multi_50510,
  title = {{Multi-task Linear Regression without Eigenvalue Lower Bounds: Adaptivity, Robustness, and Safety}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2026},
  url = {https://openreview.net/forum?id=D5Ijcnz1L9},
}

@inproceedings{icml2026_realizable_50679,
  title = {{Realizable Bayes-Consistency for General Metric Losses}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2026},
  url = {https://openreview.net/forum?id=EmqsPzyNHh},
}

@inproceedings{icml2026_distortion_55257,
  title = {{Distortion of AI Alignment Revisited: RLHF is a Decent Utilitarian Aligner}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2026},
  url = {https://openreview.net/forum?id=x4qXiDogRm},
}

@inproceedings{neurips2024_smoothed_71858,
  title = {{Smoothed Online Classification can be Harder than Batch Classification}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2024},
  url = {https://neurips.cc/virtual/2024/poster/95442},
}

@inproceedings{neurips2024_a_73489,
  title = {{A Universal Growth Rate for Learning with Smooth Surrogate Losses}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2024},
  url = {https://neurips.cc/virtual/2024/poster/93985},
}

@inproceedings{neurips2024_optimal_74126,
  title = {{Optimal Parallelization of Boosting}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2024},
  url = {https://neurips.cc/virtual/2024/poster/93411},
}

@inproceedings{neurips2024_lower_74259,
  title = {{Lower Bounds of Uniform Stability in Gradient-Based Bilevel Algorithms for Hyperparameter Optimization}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2024},
  url = {https://neurips.cc/virtual/2024/poster/93297},
}

@inproceedings{neurips2025_the_76486,
  title = {{The Cost of Robustness: Tighter Bounds on Parameter Complexity for Robust Memorization in ReLU Nets}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2025},
  url = {https://openreview.net/forum?id=Jsln9ZyMl4},
}

@inproceedings{neurips2025_agnostic_76506,
  title = {{Agnostic Learning under Targeted Poisoning: Optimal Rates and the Role of Randomness}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2025},
  url = {https://openreview.net/forum?id=K3xaVpSHkV},
}

@inproceedings{neurips2026_autoregressive_91923,
  title = {{Autoregressive Learning in Joint KL: Sharp Oracle Bounds and Lower Bounds}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2026},
  url = {https://neurips.cc/virtual/2026/poster/154406},
}

@inproceedings{neurips2026_scale_94621,
  title = {{Scale-Sensitive Shattering: Learnability and Evaluability at Optimal Scale}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2026},
  url = {https://neurips.cc/virtual/2026/oral/149341},
}

@inproceedings{iclr2024_fast_35351,
  title = {{Fast Equilibrium of SGD in Generic Situations}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2024},
  url = {https://iclr.cc/virtual/2024/poster/17722},
}