% Key papers for Online linear-quadratic control regret (scime atlas, https://atlas.scime.ai). Authors are not included: add them from the paper pages.

@inproceedings{icml2019_online_37616,
  title = {{Online Control with Adversarial Disturbances}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2019},
  url = {https://icml.cc/virtual/2019/poster/4313},
}

@inproceedings{neurips2019_online_58754,
  title = {{Online Optimal Control with Linear Dynamics and Predictions: Algorithms and Regret Analysis}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2019},
  url = {https://nips.cc/virtual/2019/poster/14397},
}

@inproceedings{neurips2020_logarithmic_60268,
  title = {{Logarithmic Regret Bound in Partially Observable Linear Dynamical Systems}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2020},
  url = {https://nips.cc/virtual/2020/poster/18354},
}

@inproceedings{neurips2020_information_60086,
  title = {{Information Theoretic Regret Bounds for Online Nonlinear Control}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2020},
  url = {https://nips.cc/virtual/2020/poster/18968},
}

@inproceedings{icml2017_near_36301,
  title = {{Near-Optimal Design of Experiments via Regret Minimization}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2017},
  url = {https://icml.cc/virtual/2017/poster/609},
}

@inproceedings{icml2019_learning_37483,
  title = {{Learning Linear-Quadratic Regulators Efficiently with only \$sqrtT\$ Regret}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2019},
  url = {https://icml.cc/virtual/2019/poster/3803},
}

@inproceedings{icml2020_logarithmic_38426,
  title = {{Logarithmic Regret for Adversarial Online Control}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2020},
  url = {https://icml.cc/virtual/2020/poster/6619},
}

@inproceedings{icml2020_naive_38489,
  title = {{Naive Exploration is Optimal for Online LQR}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2020},
  url = {https://icml.cc/virtual/2020/poster/5930},
}

@inproceedings{neurips2018_regret_57557,
  title = {{Regret Bounds for Robust Adaptive Control of the Linear Quadratic Regulator}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2018},
  url = {https://nips.cc/virtual/2018/poster/11415},
}

@inproceedings{icml2018_online_36899,
  title = {{Online Linear Quadratic Control}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2018},
  url = {https://icml.cc/virtual/2018/poster/2142},
}

@inproceedings{icml2020_logarithmic_38427,
  title = {{Logarithmic Regret for Learning Linear Quadratic Regulators Efficiently}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2020},
  url = {https://icml.cc/virtual/2020/poster/6054},
}

@inproceedings{neurips2021_perturbation_63543,
  title = {{Perturbation-based Regret Analysis of Predictive Control in Linear Time Varying Systems}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2021},
  url = {https://nips.cc/virtual/2021/poster/27792},
}

@inproceedings{neurips2020_the_60957,
  title = {{The Power of Predictions in Online Control}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2020},
  url = {https://nips.cc/virtual/2020/poster/17079},
}

@inproceedings{icml2020_efficient_38147,
  title = {{Efficient Optimistic Exploration in Linear-Quadratic Regulators via Lagrangian Relaxation}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2020},
  url = {https://icml.cc/virtual/2020/poster/6453},
}

@inproceedings{neurips2020_making_60285,
  title = {{Making Non-Stochastic Control (Almost) as Easy as Stochastic}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2020},
  url = {https://nips.cc/virtual/2020/poster/18348},
}

@inproceedings{icml2021_a_38979,
  title = {{A Regret Minimization Approach to Iterative Learning Control}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2021},
  url = {https://icml.cc/virtual/2021/poster/9817},
}

@inproceedings{neurips2022_bounded_65750,
  title = {{Bounded-Regret MPC via Perturbation Analysis: Prediction Error, Constraints, and Nonlinearity}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2022},
  url = {https://nips.cc/virtual/2022/poster/53913},
}

@inproceedings{icml2021_online_39734,
  title = {{Online Policy Gradient for Model Free Learning of Linear Quadratic Regulators with \$sqrt\$T Regret}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2021},
  url = {https://icml.cc/virtual/2021/poster/10103},
}

@inproceedings{neurips2021_online_61579,
  title = {{Online Control of Unknown Time-Varying Dynamical Systems}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2021},
  url = {https://nips.cc/virtual/2021/poster/26417},
}

@inproceedings{neurips2025_online_74940,
  title = {{Online Inverse Linear Optimization: Efficient Logarithmic-Regret Algorithm, Robustness to Suboptimality, and Lower Bound}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2025},
  url = {https://openreview.net/forum?id=2iNwD8EzOr},
}

@inproceedings{iclr2025_regret_82364,
  title = {{Regret Bounds for Episodic Risk-Sensitive Linear Quadratic Regulator}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2025},
  url = {https://iclr.cc/virtual/2025/poster/29427},
}

@inproceedings{icml2024_infinite_45048,
  title = {{Infinite-Horizon Distributionally Robust Regret-Optimal Control}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2024},
  url = {https://icml.cc/virtual/2024/poster/33404},
}

@inproceedings{icml2024_best_45106,
  title = {{Best of Both Worlds Guarantees for Smoothed Online Quadratic Optimization}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2024},
  url = {https://icml.cc/virtual/2024/poster/33346},
}

@inproceedings{icml2024_predictive_45240,
  title = {{Predictive Linear Online Tracking for Unknown Targets}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2024},
  url = {https://icml.cc/virtual/2024/poster/33212},
}

@inproceedings{icml2026_finite_53504,
  title = {{Finite and Corruption-Robust Regret Bounds in Online Inverse Linear Optimization under M-Convex Action Sets}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2026},
  url = {https://openreview.net/forum?id=g7LE4mukGq},
}

@inproceedings{neurips2024_online_71575,
  title = {{Online Control with Adversarial Disturbance for Continuous-time Linear Systems}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2024},
  url = {https://neurips.cc/virtual/2024/poster/95695},
}

@inproceedings{neurips2025_optimal_76351,
  title = {{Optimal Dynamic Regret by Transformers for Non-Stationary Reinforcement Learning}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2025},
  url = {https://openreview.net/forum?id=ILP0eZAor6},
}

@inproceedings{neurips2025_high_77498,
  title = {{High-Dimensional Calibration from Swap Regret}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2025},
  url = {https://openreview.net/forum?id=UVDihUz0iT},
}

@inproceedings{neurips2025_finite_77860,
  title = {{Finite Sample Analyses for Continuous-time Linear Systems: System Identification and Online Control}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2025},
  url = {https://openreview.net/forum?id=YJok3mWB0q},
}

@inproceedings{iclr2026_a_85178,
  title = {{A New Approach to Controlling Linear Dynamical Systems}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2026},
  url = {https://openreview.net/forum?id=BQIzu1T6F0},
}

@inproceedings{neurips2026_a_95496,
  title = {{A Memory Efficient Unified Algorithm for Online Learning of Linear Dynamical Systems}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2026},
  url = {https://neurips.cc/virtual/2026/poster/155619},
}

@inproceedings{icml2024_finite_45587,
  title = {{Finite Time Logarithmic Regret Bounds for Self-Tuning Regulation}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2024},
  url = {https://icml.cc/virtual/2024/poster/32865},
}

@inproceedings{neurips2026_online_90016,
  title = {{Online Imitation Learning for Stabilizing Vlasov--Poisson Plasmas}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2026},
  url = {https://neurips.cc/virtual/2026/poster/155670},
}

@inproceedings{neurips2026_finite_93538,
  title = {{Finite-Resolution Decision Sufficiency for Linear Optimization}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2026},
  url = {https://neurips.cc/virtual/2026/poster/154190},
}