% Key papers for Theory of inverse RL (scime atlas, https://atlas.scime.ai). Authors are not included: add them from the paper pages.

@inproceedings{neurips2016_cooperative_55651,
  title = {{Cooperative Inverse Reinforcement Learning}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2016},
  url = {https://nips.cc/virtual/2016/poster/6933},
}

@inproceedings{neurips2019_meta_58591,
  title = {{Meta-Inverse Reinforcement Learning with Probabilistic Context Variables}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2019},
  url = {https://nips.cc/virtual/2019/poster/14152},
}

@inproceedings{aaai2016_inverse_105141,
  title = {{Inverse Reinforcement Learning through Policy Gradient Minimization}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2016},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/10313},
}

@inproceedings{neurips2019_learner_58468,
  title = {{Learner-aware Teaching: Inverse Reinforcement Learning with Preferences and Constraints}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2019},
  url = {https://nips.cc/virtual/2019/poster/13573},
}

@inproceedings{iclr2020_maximum_29313,
  title = {{Maximum Likelihood Constraint Inference for Inverse Reinforcement Learning}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2020},
  url = {https://openreview.net/forum?id=BJliakStvH},
}

@inproceedings{neurips2019_smile_58931,
  title = {{SMILe: Scalable Meta Inverse Reinforcement Learning through Context-Conditional Policies}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2019},
  url = {https://nips.cc/virtual/2019/poster/13865},
}

@inproceedings{neurips2017_compatible_56203,
  title = {{Compatible Reward Inverse Reinforcement Learning}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2017},
  url = {https://nips.cc/virtual/2017/poster/8993},
}

@inproceedings{aaai2019_model_101591,
  title = {{Model-Free IRL Using Maximum Likelihood Estimation}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2019},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/4286},
}

@inproceedings{aaai2017_fast_105720,
  title = {{Fast Inverse Reinforcement Learning with Interval Consistent Graph for Driving Behavior Prediction}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2017},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/10762},
}

@inproceedings{icml2019_learning_37492,
  title = {{Learning a Prior over Intent via Meta-Inverse Reinforcement Learning}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2019},
  url = {https://icml.cc/virtual/2019/poster/3995},
}

@inproceedings{icml2021_inverse_39480,
  title = {{Inverse Constrained Reinforcement Learning}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2021},
  url = {https://icml.cc/virtual/2021/poster/9667},
}

@inproceedings{neurips2022_active_65018,
  title = {{Active Exploration for Inverse Reinforcement Learning}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2022},
  url = {https://nips.cc/virtual/2022/poster/53423},
}

@inproceedings{aaai2023_misspecification_4157,
  title = {{Misspecification in Inverse Reinforcement Learning}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2023},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/26766},
}

@inproceedings{aaai2024_meta_6629,
  title = {{Meta-Inverse Reinforcement Learning for Mean Field Games via Probabilistic Context Variables}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2024},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/29021},
}

@inproceedings{aaai2021_inverse_808,
  title = {{Inverse Reinforcement Learning with Explicit Policy Estimates}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2021},
  url = {https://aaai.org/papers/09472-inverse-reinforcement-learning-with-explicit-policy-estimates/},
}

@inproceedings{neurips2022_maximum_66512,
  title = {{Maximum-Likelihood Inverse Reinforcement Learning with Finite-Time Guarantees}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2022},
  url = {https://nips.cc/virtual/2022/poster/53639},
}

@inproceedings{iclr2023_learning_32148,
  title = {{Learning Soft Constraints From Constrained Expert Demonstrations}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2023},
  url = {https://iclr.cc/virtual/2023/poster/11754},
}

@inproceedings{icml2023_towards_42636,
  title = {{Towards Theoretical Understanding of Inverse Reinforcement Learning}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2023},
  url = {https://icml.cc/virtual/2023/poster/24193},
}

@inproceedings{neurips2024_in_73737,
  title = {{In-Trajectory Inverse Reinforcement Learning: Learn Incrementally Before an Ongoing Trajectory Terminates}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2024},
  url = {https://neurips.cc/virtual/2024/poster/93762},
}

@inproceedings{aaai2024_recent_7075,
  title = {{Recent Advancements in Inverse Reinforcement Learning}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2024},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/30296},
}

@inproceedings{neurips2024_towards_73638,
  title = {{Towards the Transferability of Rewards Recovered via Regularized Inverse Reinforcement Learning}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2024},
  url = {https://neurips.cc/virtual/2024/poster/93854},
}

@inproceedings{aaai2025_partial_10272,
  title = {{Partial Identifiability in Inverse Reinforcement Learning for Agents with Non-Exponential Discounting}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2025},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/34977},
}

@inproceedings{iclr2024_quantifying_35316,
  title = {{Quantifying the Sensitivity of Inverse Reinforcement Learning to Misspecification}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2024},
  url = {https://iclr.cc/virtual/2024/poster/17757},
}

@inproceedings{icml2024_offline_43331,
  title = {{Offline Inverse RL: New Solution Concepts and Provably Efficient Algorithms}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2024},
  url = {https://icml.cc/virtual/2024/poster/35121},
}

@inproceedings{icml2024_confidence_43532,
  title = {{Confidence Aware Inverse Constrained Reinforcement Learning}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2024},
  url = {https://icml.cc/virtual/2024/poster/34920},
}

@inproceedings{icml2024_is_43544,
  title = {{Is Inverse Reinforcement Learning Harder than Standard Reinforcement Learning? A Theoretical Perspective}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2024},
  url = {https://icml.cc/virtual/2024/poster/34908},
}

@inproceedings{icml2025_provably_48018,
  title = {{Provably Efficient Exploration in Inverse Constrained Reinforcement Learning}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2025},
  url = {https://icml.cc/virtual/2025/poster/44588},
}

@inproceedings{icml2026_trust_52599,
  title = {{Trust Region Inverse Reinforcement Learning: Explicit Dual Ascent using Local Policy Updates}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2026},
  url = {https://openreview.net/forum?id=XSYX75R6RC},
}

@inproceedings{neurips2024_sub_70719,
  title = {{Sub-optimal Experts mitigate Ambiguity in Inverse Reinforcement Learning}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2024},
  url = {https://neurips.cc/virtual/2024/poster/96405},
}

@inproceedings{neurips2025_trust_75075,
  title = {{Trust Region Reward Optimization and Proximal Inverse Reward Optimization Algorithm}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2025},
  url = {https://openreview.net/forum?id=40M1uJl2GX},
}

@inproceedings{iclr2025_non_81816,
  title = {{Non-Adversarial Inverse Reinforcement Learning via Successor Feature Matching}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2025},
  url = {https://iclr.cc/virtual/2025/poster/29965},
}

@inproceedings{iclr2024_uncertainty_34107,
  title = {{Uncertainty-aware Constraint Inference in Inverse Constrained Reinforcement Learning}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2024},
  url = {https://iclr.cc/virtual/2024/poster/18968},
}

@inproceedings{icml2024_learning_43707,
  title = {{Learning Constraints from Offline Demonstrations via Superior Distribution Correction Estimation}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2024},
  url = {https://icml.cc/virtual/2024/poster/34745},
}

@inproceedings{icml2024_robust_45422,
  title = {{Robust Inverse Constrained Reinforcement Learning under Model Misspecification}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2024},
  url = {https://icml.cc/virtual/2024/poster/33029},
}

@inproceedings{iclr2025_toward_80511,
  title = {{Toward Exploratory Inverse Constraint Inference with Generative Diffusion Verifiers}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2025},
  url = {https://iclr.cc/virtual/2025/poster/31251},
}

@inproceedings{iclr2025_understanding_81174,
  title = {{Understanding Constraint Inference in Safety-Critical Inverse Reinforcement Learning}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2025},
  url = {https://iclr.cc/virtual/2025/poster/30596},
}

@inproceedings{neurips2026_direcl_93378,
  title = {{DiReCL: Learning Differentiable Reward Code with Inverse Reinforcement Learning}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2026},
  url = {https://neurips.cc/virtual/2026/poster/148348},
}