% Key papers for Dynamic and adaptive regret (scime atlas, https://atlas.scime.ai). Authors are not included: add them from the paper pages.

@inproceedings{neurips2018_adaptive_56839,
  title = {{Adaptive Online Learning in Dynamic Environments}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2018},
  url = {https://nips.cc/virtual/2018/poster/11149},
}

@inproceedings{neurips2017_improved_56374,
  title = {{Improved Dynamic Regret for Non-degenerate Functions}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2017},
  url = {https://nips.cc/virtual/2017/poster/8868},
}

@inproceedings{neurips2019_logarithmic_58559,
  title = {{Logarithmic Regret for Online Control}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2019},
  url = {https://nips.cc/virtual/2019/poster/13234},
}

@inproceedings{icml2019_cautious_37212,
  title = {{Cautious Regret Minimization: Online Optimization with Long-Term Budget Constraints}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2019},
  url = {https://icml.cc/virtual/2019/poster/4210},
}

@inproceedings{icml2020_a_37889,
  title = {{A new regret analysis for Adam-type algorithms}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2020},
  url = {https://icml.cc/virtual/2020/poster/6501},
}

@inproceedings{icml2016_tracking_36014,
  title = {{Tracking Slowly Moving Clairvoyant: Optimal Dynamic Regret of Online Learning with True and Noisy Gradient}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2016},
  url = {https://proceedings.mlr.press/v48/yangb16.html},
}

@inproceedings{icml2019_adaptive_37133,
  title = {{Adaptive Regret of Convex and Smooth Functions}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2019},
  url = {https://icml.cc/virtual/2019/poster/3815},
}

@inproceedings{aaai2021_on_1089,
  title = {{On Online Optimization: Dynamic Regret Analysis of Strongly Convex and Smooth Problems}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2021},
  url = {https://aaai.org/papers/06966-on-online-optimization-dynamic-regret-analysis-of-strongly-convex-and-smooth-problems/},
}

@inproceedings{neurips2020_dynamic_59748,
  title = {{Dynamic Regret of Convex and Smooth Functions}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2020},
  url = {https://nips.cc/virtual/2020/poster/17396},
}

@inproceedings{neurips2016_optimistic_55925,
  title = {{Optimistic Bandit Convex Optimization}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2016},
  url = {https://nips.cc/virtual/2016/poster/6953},
}

@inproceedings{aaai2020_trading_102287,
  title = {{Trading-Off Static and Dynamic Regret in Online Least-Squares and Beyond}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2020},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/6149},
}

@inproceedings{neurips2017_regret_56618,
  title = {{Regret Analysis for Continuous Dueling Bandit}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2017},
  url = {https://nips.cc/virtual/2017/poster/8940},
}

@inproceedings{neurips2021_dual_63141,
  title = {{Dual Adaptivity: A Universal Algorithm for Minimizing the Adaptive Regret of Convex Functions}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2021},
  url = {https://nips.cc/virtual/2021/poster/27846},
}

@inproceedings{neurips2020_non_60434,
  title = {{Non-Stochastic Control with Bandit Feedback}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2020},
  url = {https://nips.cc/virtual/2020/poster/18210},
}

@inproceedings{cvpr2023_a_20283,
  title = {{A General Regret Bound of Preconditioned Gradient Method for DNN Training}},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2023},
  url = {https://cvpr.thecvf.com/virtual/2023/poster/21965},
}

@inproceedings{icml2021_non_39669,
  title = {{Non-Exponentially Weighted Aggregation: Regret Bounds for Unbounded Loss Functions}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2021},
  url = {https://icml.cc/virtual/2021/poster/8841},
}

@inproceedings{aaai2024_cumulative_5536,
  title = {{Cumulative Regret Analysis of the Piyavskii–Shubert Algorithm and Its Variants for Global Optimization}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2024},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/30057},
}

@inproceedings{icml2021_optimal_39750,
  title = {{Optimal regret algorithm for Pseudo-1d Bandit Convex Optimization}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2021},
  url = {https://icml.cc/virtual/2021/poster/10431},
}

@inproceedings{neurips2020_bandit_59426,
  title = {{Bandit Linear Control}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2020},
  url = {https://nips.cc/virtual/2020/poster/17239},
}

@inproceedings{neurips2020_comparator_59556,
  title = {{Comparator-Adaptive Convex Bandits}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2020},
  url = {https://nips.cc/virtual/2020/poster/18756},
}

@inproceedings{aaai2024_no_6777,
  title = {{No Internal Regret with Non-convex Loss Functions}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2024},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/29412},
}

@inproceedings{icml2024_online_43654,
  title = {{Online Linear Regression in Dynamic Environments via Discounting}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2024},
  url = {https://icml.cc/virtual/2024/poster/34798},
}

@inproceedings{neurips2024_improved_72844,
  title = {{Improved Regret for Bandit Convex Optimization with Delayed Feedback}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2024},
  url = {https://neurips.cc/virtual/2024/poster/94556},
}

@inproceedings{iclr2024_adaptive_33819,
  title = {{Adaptive Regret for Bandits Made Possible: Two Queries Suffice}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2024},
  url = {https://iclr.cc/virtual/2024/poster/19256},
}

@inproceedings{icml2025_on_47878,
  title = {{On the Dynamic Regret of Following the Regularized Leader: Optimism with History Pruning}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2025},
  url = {https://icml.cc/virtual/2025/poster/44725},
}

@inproceedings{icml2026_a_52600,
  title = {{A Perturbation Approach to Unconstrained Linear Bandits}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2026},
  url = {https://openreview.net/forum?id=XSpBSHzJAg},
}

@inproceedings{icml2026_parameter_55326,
  title = {{Parameter-free Dynamic Regret: Time-varying Movement Costs, Delayed Feedback, and Memory}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2026},
  url = {https://openreview.net/forum?id=xnj5CyzVVW},
}

@inproceedings{neurips2024_an_73361,
  title = {{An Equivalence Between Static and Dynamic Regret Minimization}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2024},
  url = {https://neurips.cc/virtual/2024/poster/94087},
}

@inproceedings{neurips2024_tight_73774,
  title = {{Tight Rates for Bandit Control Beyond Quadratics}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2024},
  url = {https://neurips.cc/virtual/2024/poster/93732},
}

@inproceedings{neurips2024_online_74263,
  title = {{Online Convex Optimisation: The Optimal Switching Regret for all Segmentations Simultaneously}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2024},
  url = {https://neurips.cc/virtual/2024/poster/93293},
}

@inproceedings{neurips2025_non_74794,
  title = {{Non-stationary Bandit Convex Optimization: A Comprehensive Study}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2025},
  url = {https://openreview.net/forum?id=1GCbcBJt83},
}

@inproceedings{neurips2025_dynamic_75113,
  title = {{Dynamic Regret Reduces to Kernelized Static Regret}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2025},
  url = {https://openreview.net/forum?id=4LSulRbbeL},
}

@inproceedings{neurips2025_establishing_75619,
  title = {{Establishing Linear Surrogate Regret Bounds for Convex Smooth Losses via Convolutional Fenchel–Young Losses}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2025},
  url = {https://openreview.net/forum?id=A4Xx9irvpp},
}

@inproceedings{iclr2026_discounted_84749,
  title = {{Discounted Online Convex Optimization: Uniform Regret Across a Continuous Interval}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2026},
  url = {https://openreview.net/forum?id=65iFtHZ8Cu},
}

@inproceedings{neurips2026_a_91672,
  title = {{A Reduction from Delayed to Immediate Feedback for Online Convex Optimization with Improved Guarantees}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2026},
  url = {https://neurips.cc/virtual/2026/poster/155853},
}

@inproceedings{neurips2026_dynamic_91682,
  title = {{Dynamic Regret in Online Convex Optimization with Indicator Switching Costs}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2026},
  url = {https://neurips.cc/virtual/2026/poster/150727},
}

@inproceedings{icml2024_small_45016,
  title = {{Small-loss Adaptive Regret for Online Convex Optimization}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2024},
  url = {https://icml.cc/virtual/2024/poster/33436},
}

@inproceedings{icml2024_handling_45824,
  title = {{Handling Heterogeneous Curvatures in Bandit LQR Control}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2024},
  url = {https://icml.cc/virtual/2024/poster/32628},
}

@inproceedings{icml2026_logarithmic_51788,
  title = {{Logarithmic Switching Regret for Online Convex Optimization}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2026},
  url = {https://openreview.net/forum?id=Pv5FwOtf8a},
}