% Key papers for Linear function approximation in RL (scime atlas, https://atlas.scime.ai). Authors are not included: add them from the paper pages.

@inproceedings{neurips2017_unifying_56746,
  title = {{Unifying PAC and Regret: Uniform PAC Bounds for Episodic Reinforcement Learning}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2017},
  url = {https://nips.cc/virtual/2017/poster/9345},
}

@inproceedings{icml2020_learning_38382,
  title = {{Learning Near Optimal Policies with Low Inherent Bellman Error}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2020},
  url = {https://icml.cc/virtual/2020/poster/6784},
}

@inproceedings{iclr2021_optimism_30098,
  title = {{Optimism in Reinforcement Learning with Generalized Linear Function Approximation}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2021},
  url = {https://iclr.cc/virtual/2021/poster/2563},
}

@inproceedings{icml2020_learning_38415,
  title = {{Learning with Good Feature Representations in Bandits and in RL with a Generative Model}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2020},
  url = {https://icml.cc/virtual/2020/poster/6647},
}

@inproceedings{icml2020_provable_38652,
  title = {{Provable Self-Play Algorithms for Competitive Reinforcement Learning}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2020},
  url = {https://icml.cc/virtual/2020/poster/6303},
}

@inproceedings{icml2020_reinforcement_38689,
  title = {{Reinforcement Learning in Feature Space: Matrix Bandit, Kernels, and Regret Bound}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2020},
  url = {https://icml.cc/virtual/2020/poster/6224},
}

@inproceedings{icml2020_optimistic_38592,
  title = {{Optimistic Policy Optimization with Bandit Feedback}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2020},
  url = {https://icml.cc/virtual/2020/poster/5890},
}

@inproceedings{neurips2020_adaptive_59320,
  title = {{Adaptive Discretization for Model-Based Reinforcement Learning}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2020},
  url = {https://nips.cc/virtual/2020/poster/18943},
}

@inproceedings{neurips2021_online_62874,
  title = {{Online learning in MDPs with linear function approximation and bandit feedback.}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2021},
  url = {https://nips.cc/virtual/2021/poster/28630},
}

@inproceedings{icml2021_near_39651,
  title = {{Near-Optimal Representation Learning for Linear Bandits and Linear RL}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2021},
  url = {https://icml.cc/virtual/2021/poster/8469},
}

@inproceedings{neurips2022_provably_64457,
  title = {{Provably Efficient Model-Free Constrained RL with Linear Function Approximation}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2022},
  url = {https://nips.cc/virtual/2022/poster/53679},
}

@inproceedings{icml2021_risk_39887,
  title = {{Risk-Sensitive Reinforcement Learning with Function Approximation: A Debiasing Approach}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2021},
  url = {https://icml.cc/virtual/2021/poster/9329},
}

@inproceedings{neurips2021_provably_63007,
  title = {{Provably Efficient Reinforcement Learning with Linear Function Approximation under Adaptivity Constraints}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2021},
  url = {https://nips.cc/virtual/2021/poster/28052},
}

@inproceedings{iclr2022_towards_31744,
  title = {{Towards General Function Approximation in Zero-Sum Markov Games}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2022},
  url = {https://iclr.cc/virtual/2022/poster/6810},
}

@inproceedings{neurips2021_provable_62773,
  title = {{Provable Model-based Nonlinear Bandit and Reinforcement Learning: Shelve Optimism, Embrace Virtual Curvature}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2021},
  url = {https://nips.cc/virtual/2021/poster/28584},
}

@inproceedings{icml2021_randomized_39854,
  title = {{Randomized Exploration in Reinforcement Learning with General Value Function Approximation}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2021},
  url = {https://icml.cc/virtual/2021/poster/9803},
}

@inproceedings{aaai2023_online_4299,
  title = {{Online Reinforcement Learning with Uncertain Episode Lengths}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2023},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/26088},
}

@inproceedings{iclr2022_a_30968,
  title = {{A Reduction-Based Framework for Conservative Bandits and Reinforcement Learning}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2022},
  url = {https://iclr.cc/virtual/2022/poster/7122},
}

@inproceedings{icml2021_kernel_39495,
  title = {{Kernel-Based Reinforcement Learning: A Finite-Time Analysis}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2021},
  url = {https://icml.cc/virtual/2021/poster/8573},
}

@inproceedings{icml2021_safe_39914,
  title = {{Safe Reinforcement Learning with Linear Function Approximation}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2021},
  url = {https://icml.cc/virtual/2021/poster/9847},
}

@inproceedings{iclr2024_demystifying_34422,
  title = {{Demystifying Linear MDPs and Novel Dynamics Aggregation Framework}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2024},
  url = {https://iclr.cc/virtual/2024/poster/18653},
}

@inproceedings{icml2024_a_44028,
  title = {{A General Framework for Sequential Decision-Making under Adaptivity Constraints}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2024},
  url = {https://icml.cc/virtual/2024/poster/34424},
}

@inproceedings{icml2024_more_45205,
  title = {{More Benefits of Being Distributional: Second-Order Bounds for Reinforcement Learning}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2024},
  url = {https://icml.cc/virtual/2024/poster/33247},
}

@inproceedings{icml2024_prospective_45392,
  title = {{Prospective Side Information for Latent MDPs}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2024},
  url = {https://icml.cc/virtual/2024/poster/33059},
}

@inproceedings{icml2024_near_45408,
  title = {{Near-Optimal Reinforcement Learning with Self-Play under Adaptivity Constraints}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2024},
  url = {https://icml.cc/virtual/2024/poster/33043},
}

@inproceedings{icml2025_logarithmic_46184,
  title = {{Logarithmic Regret for Online KL-Regularized Reinforcement Learning}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2025},
  url = {https://icml.cc/virtual/2025/poster/46376},
}

@inproceedings{icml2025_bellman_46493,
  title = {{Bellman Unbiasedness: Toward Provably Efficient Distributional Reinforcement Learning with General Value Function Approximation}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2025},
  url = {https://icml.cc/virtual/2025/poster/46074},
}

@inproceedings{icml2025_automatic_46818,
  title = {{Automatic Reward Shaping from Confounded Offline Data}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2025},
  url = {https://icml.cc/virtual/2025/poster/45757},
}

@inproceedings{icml2025_concurrent_47050,
  title = {{Concurrent Reinforcement Learning with Aggregated States via Randomized Least Squares Value Iteration}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2025},
  url = {https://icml.cc/virtual/2025/poster/45535},
}

@inproceedings{icml2026_exact_54367,
  title = {{Exact Unlearning in Reinforcement Learning}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2026},
  url = {https://openreview.net/forum?id=oLmwqIhzqj},
}

@inproceedings{neurips2024_randomized_70710,
  title = {{Randomized Exploration for Reinforcement Learning with Multinomial Logistic Function Approximation}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2024},
  url = {https://neurips.cc/virtual/2024/poster/96414},
}

@inproceedings{neurips2024_provably_74612,
  title = {{Provably Efficient Reinforcement Learning with Multinomial Logit Function Approximation}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2024},
  url = {https://neurips.cc/virtual/2024/poster/92978},
}

@inproceedings{neurips2025_preference_79165,
  title = {{Preference-based Reinforcement Learning beyond Pairwise Comparisons: Benefits of Multiple Options}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2025},
  url = {https://openreview.net/forum?id=mPuOMcN9E7},
}

@inproceedings{iclr2025_minimax_83091,
  title = {{Minimax Optimal Reinforcement Learning with Quasi-Optimism}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2025},
  url = {https://iclr.cc/virtual/2025/poster/28715},
}

@inproceedings{iclr2025_misspecified_83405,
  title = {{Misspecified \$Q\$-Learning with Sparse Linear Function Approximation: Tight Bounds on Approximation Error}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2025},
  url = {https://iclr.cc/virtual/2025/poster/28411},
}

@inproceedings{iclr2026_q_87299,
  title = {{Q-learning with Posterior Sampling}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2026},
  url = {https://openreview.net/forum?id=a4z7OlgSxC},
}

@inproceedings{iclr2026_frozen_88220,
  title = {{Frozen Policy Iteration: Computationally Efficient RL under Linear \$Q^pi\$ Realizability for Deterministic Dynamics}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2026},
  url = {https://openreview.net/forum?id=kW0SudrQEQ},
}

@inproceedings{neurips2026_reinforcement_90446,
  title = {{Reinforcement Learning with Multi-Step Lookahead Information Via Adaptive Batching}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2026},
  url = {https://neurips.cc/virtual/2026/poster/151980},
}

@inproceedings{neurips2026_information_95302,
  title = {{Information-Directed Offline-to-Online Reinforcement Learning}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2026},
  url = {https://neurips.cc/virtual/2026/poster/154972},
}

@inproceedings{aaai2026_on_108707,
  title = {{On the Exponential Convergence for Offline RLHF with Pairwise Comparisons}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2026},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/41059},
}