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

@inproceedings{neurips2020_reinforcement_60695,
  title = {{Reinforcement Learning with General Value Function Approximation: Provably Efficient Approach via Bounded Eluder Dimension}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2020},
  url = {https://nips.cc/virtual/2020/poster/17506},
}

@inproceedings{neurips2019_finite_58281,
  title = {{Finite-time Analysis of Approximate Policy Iteration for the Linear Quadratic Regulator}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2019},
  url = {https://nips.cc/virtual/2019/poster/13912},
}

@inproceedings{neurips2019_provably_58850,
  title = {{Provably Efficient Q-learning with Function Approximation via Distribution Shift Error Checking Oracle}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2019},
  url = {https://nips.cc/virtual/2019/poster/13880},
}

@inproceedings{iclr2020_is_29754,
  title = {{Is a Good Representation Sufficient for Sample Efficient Reinforcement Learning?}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2020},
  url = {https://openreview.net/forum?id=r1genAVKPB},
}

@inproceedings{neurips2021_bellman_62653,
  title = {{Bellman Eluder Dimension: New Rich Classes of RL Problems, and Sample-Efficient Algorithms}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2021},
  url = {https://nips.cc/virtual/2021/poster/26566},
}

@inproceedings{icml2020_a_37866,
  title = {{A Finite-Time Analysis of Q-Learning with Neural Network Function Approximation}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2020},
  url = {https://icml.cc/virtual/2020/poster/6076},
}

@inproceedings{neurips2020_provably_60639,
  title = {{Provably Efficient Reward-Agnostic Navigation with Linear Value Iteration}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2020},
  url = {https://nips.cc/virtual/2020/poster/19040},
}

@inproceedings{neurips2020_provably_60638,
  title = {{Provably Efficient Reinforcement Learning with Kernel and Neural Function Approximations}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2020},
  url = {https://papers.nips.cc/paper_files/paper/2020/hash/9fa04f87c9138de23e92582b4ce549ec-Abstract.html},
}

@inproceedings{icml2022_pessimistic_41008,
  title = {{Pessimistic Q-Learning for Offline Reinforcement Learning: Towards Optimal Sample Complexity}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2022},
  url = {https://icml.cc/virtual/2022/poster/16129},
}

@inproceedings{neurips2019_on_58726,
  title = {{On the Correctness and Sample Complexity of Inverse Reinforcement Learning}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2019},
  url = {https://nips.cc/virtual/2019/poster/13806},
}

@inproceedings{neurips2020_is_60114,
  title = {{Is Plug-in Solver Sample-Efficient for Feature-based Reinforcement Learning?}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2020},
  url = {https://nips.cc/virtual/2020/poster/18245},
}

@inproceedings{iclr2021_greedy_30023,
  title = {{Greedy-GQ with Variance Reduction: Finite-time Analysis and Improved Complexity}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2021},
  url = {https://iclr.cc/virtual/2021/poster/2968},
}

@inproceedings{neurips2021_sample_61257,
  title = {{Sample-Efficient Reinforcement Learning Is Feasible for Linearly Realizable MDPs with Limited Revisiting}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2021},
  url = {https://nips.cc/virtual/2021/poster/28838},
}

@inproceedings{neurips2021_online_61870,
  title = {{Online Robust Reinforcement Learning with Model Uncertainty}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2021},
  url = {https://nips.cc/virtual/2021/poster/28416},
}

@inproceedings{neurips2021_finite_62406,
  title = {{Finite Sample Analysis of Average-Reward TD Learning and \$Q\$-Learning}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2021},
  url = {https://nips.cc/virtual/2021/poster/26368},
}

@inproceedings{iclr2022_representation_31120,
  title = {{Representation Learning for Online and Offline RL in Low-rank MDPs}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2022},
  url = {https://iclr.cc/virtual/2022/poster/7191},
}

@inproceedings{iclr2024_provable_35226,
  title = {{Provable and Practical: Efficient Exploration in Reinforcement Learning via Langevin Monte Carlo}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2024},
  url = {https://iclr.cc/virtual/2024/poster/17847},
}

@inproceedings{icml2021_pc_39765,
  title = {{PC-MLP: Model-based Reinforcement Learning with Policy Cover Guided Exploration}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2021},
  url = {https://icml.cc/virtual/2021/poster/9705},
}

@inproceedings{neurips2021_sample_61877,
  title = {{Sample-Efficient Reinforcement Learning for Linearly-Parameterized MDPs with a Generative Model}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2021},
  url = {https://nips.cc/virtual/2021/poster/28525},
}

@inproceedings{neurips2023_on_67580,
  title = {{On the Convergence and Sample Complexity Analysis of Deep Q-Networks with \$epsilon\$-Greedy Exploration}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2023},
  url = {https://nips.cc/virtual/2023/poster/72160},
}

@inproceedings{aaai2024_sample_7251,
  title = {{Sample Efficient Reinforcement Learning with Partial Dynamics Knowledge}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2024},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/28953},
}

@inproceedings{icml2025_accelerating_46981,
  title = {{Accelerating Quantum Reinforcement Learning with a Quantum Natural Policy Gradient Based Approach}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2025},
  url = {https://icml.cc/virtual/2025/poster/45600},
}

@inproceedings{neurips2024_model_71430,
  title = {{Model-free Low-Rank Reinforcement Learning via Leveraged Entry-wise Matrix Estimation}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2024},
  url = {https://neurips.cc/virtual/2024/poster/95824},
}

@inproceedings{neurips2024_distributionally_72859,
  title = {{Distributionally Robust Reinforcement Learning with Interactive Data Collection: Fundamental Hardness and Near-Optimal Algorithms}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2024},
  url = {https://neurips.cc/virtual/2024/poster/94542},
}

@inproceedings{neurips2024_rethinking_73608,
  title = {{Rethinking Model-based, Policy-based, and Value-based Reinforcement Learning via the Lens of Representation Complexity}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2024},
  url = {https://neurips.cc/virtual/2024/poster/93879},
}

@inproceedings{neurips2024_a_74133,
  title = {{A Nearly Optimal and Low-Switching Algorithm for Reinforcement Learning with General Function Approximation}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2024},
  url = {https://neurips.cc/virtual/2024/poster/93405},
}

@inproceedings{neurips2024_multi_74471,
  title = {{Multi-Reward Best Policy Identification}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2024},
  url = {https://neurips.cc/virtual/2024/poster/93103},
}

@inproceedings{aaai2026_orvit_106921,
  title = {{ORVIT: Near-Optimal Online Distributionally Robust Reinforcement Learning}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2026},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/39273},
}

@inproceedings{iclr2024_provably_33796,
  title = {{Provably Efficient CVaR RL in Low-rank MDPs}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2024},
  url = {https://iclr.cc/virtual/2024/poster/19279},
}

@inproceedings{iclr2024_sample_34871,
  title = {{Sample-Efficiency in Multi-Batch Reinforcement Learning: The Need for Dimension-Dependent Adaptivity}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2024},
  url = {https://iclr.cc/virtual/2024/poster/18203},
}

@inproceedings{iclr2024_demonstration_35129,
  title = {{Demonstration-Regularized RL}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2024},
  url = {https://iclr.cc/virtual/2024/poster/17944},
}

@inproceedings{icml2024_provable_43282,
  title = {{Provable Risk-Sensitive Distributional Reinforcement Learning with General Function Approximation}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2024},
  url = {https://icml.cc/virtual/2024/poster/35170},
}

@inproceedings{icml2024_model_43452,
  title = {{Model-Based RL for Mean-Field Games is not Statistically Harder than Single-Agent RL}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2024},
  url = {https://icml.cc/virtual/2024/poster/35000},
}

@inproceedings{icml2024_towards_44703,
  title = {{Towards Robust Model-Based Reinforcement Learning Against Adversarial Corruption}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2024},
  url = {https://icml.cc/virtual/2024/poster/33749},
}

@inproceedings{icml2025_sample_48604,
  title = {{Sample Complexity of Distributionally Robust Off-Dynamics Reinforcement Learning with Online Interaction}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2025},
  url = {https://icml.cc/virtual/2025/poster/44017},
}

@inproceedings{icml2026_model_50854,
  title = {{Model-Free Robust Average-Reward Reinforcement Learning with Sample Complexity Analysis}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2026},
  url = {https://openreview.net/forum?id=GMIHHrJ6Wp},
}

@inproceedings{icml2026_post_54314,
  title = {{Post-Training with Policy Gradients: Optimality and the Base Model Barrier}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2026},
  url = {https://openreview.net/forum?id=nnWlTi7A7a},
}

@inproceedings{icml2026_breaking_55014,
  title = {{Breaking the Computational Barrier: Provably Efficient Actor–Critic for Low-Rank MDPs}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2026},
  url = {https://openreview.net/forum?id=urlj8fAMrf},
}

@inproceedings{neurips2024_state_72241,
  title = {{State-free Reinforcement Learning}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2024},
  url = {https://neurips.cc/virtual/2024/poster/95101},
}