% Key papers for Losses for decision-making (scime atlas, https://atlas.scime.ai). Authors are not included: add them from the paper pages.

@inproceedings{aaai2019_melding_100626,
  title = {{Melding the Data-Decisions Pipeline: Decision-Focused Learning for Combinatorial Optimization}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2019},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/3982},
}

@inproceedings{aaai2021_glister_647,
  title = {{GLISTER: Generalization based Data Subset Selection for Efficient and Robust Learning}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2021},
  url = {https://aaai.org/papers/08110-glister-generalization-based-data-subset-selection-for-efficient-and-robust-learning/},
}

@inproceedings{aaai2021_is_812,
  title = {{Is the Most Accurate AI the Best Teammate? Optimizing AI for Teamwork}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2021},
  url = {https://aaai.org/papers/11405-is-the-most-accurate-ai-the-best-teammate-optimizing-ai-for-teamwork/},
}

@inproceedings{icml2016_minimizing_35905,
  title = {{Minimizing the Maximal Loss: How and Why}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2016},
  url = {https://proceedings.mlr.press/v48/shalev-shwartzb16.html},
}

@inproceedings{icml2020_consistent_38037,
  title = {{Consistent Estimators for Learning to Defer to an Expert}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2020},
  url = {https://icml.cc/virtual/2020/poster/6448},
}

@inproceedings{icml2024_model_45100,
  title = {{Model Alignment as Prospect Theoretic Optimization}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2024},
  url = {https://icml.cc/virtual/2024/poster/33352},
}

@inproceedings{icml2021_outside_39759,
  title = {{Outside the Echo Chamber: Optimizing the Performative Risk}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2021},
  url = {https://icml.cc/virtual/2021/poster/9029},
}

@inproceedings{icml2021_don_39267,
  title = {{Don’t Just Blame Over-parametrization for Over-confidence: Theoretical Analysis of Calibration in Binary Classification}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2021},
  url = {https://icml.cc/virtual/2021/poster/8711},
}

@inproceedings{icml2022_decision_40415,
  title = {{Decision-Focused Learning: Through the Lens of Learning to Rank}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2022},
  url = {https://icml.cc/virtual/2022/poster/18375},
}

@inproceedings{iclr2021_influence_30749,
  title = {{Influence Functions in Deep Learning Are Fragile}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2021},
  url = {https://iclr.cc/virtual/2021/poster/2841},
}

@inproceedings{aaai2022_the_2833,
  title = {{The Perils of Learning Before Optimizing}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2022},
  url = {https://aaai.org/papers/03708-the-perils-of-learning-before-optimizing/},
}

@inproceedings{neurips2019_on_58709,
  title = {{On Human-Aligned Risk Minimization}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2019},
  url = {https://nips.cc/virtual/2019/poster/14411},
}

@inproceedings{aaai2024_leaving_6516,
  title = {{Leaving the Nest: Going beyond Local Loss Functions for Predict-Then-Optimize}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2024},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/29410},
}

@inproceedings{icml2021_how_39431,
  title = {{How Important is the Train-Validation Split in Meta-Learning?}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2021},
  url = {https://icml.cc/virtual/2021/poster/9855},
}

@inproceedings{icml2020_uniform_38904,
  title = {{Uniform Convergence of Rank-weighted Learning}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2020},
  url = {https://icml.cc/virtual/2020/poster/5826},
}

@inproceedings{neurips2020_on_60461,
  title = {{On Correctness of Automatic Differentiation for Non-Differentiable Functions}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2020},
  url = {https://nips.cc/virtual/2020/poster/17600},
}

@inproceedings{neurips2019_on_58724,
  title = {{On the Calibration of Multiclass Classification  with Rejection}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2019},
  url = {https://nips.cc/virtual/2019/poster/13449},
}

@inproceedings{neurips2021_reliable_62037,
  title = {{Reliable Decisions with Threshold Calibration}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2021},
  url = {https://nips.cc/virtual/2021/poster/26203},
}

@inproceedings{neurips2022_decision_65553,
  title = {{Decision-Focused Learning without Decision-Making: Learning Locally Optimized Decision Losses}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2022},
  url = {https://nips.cc/virtual/2022/poster/54962},
}

@inproceedings{icml2022_sample_41136,
  title = {{Sample Efficient Learning of Predictors that Complement Humans}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2022},
  url = {https://icml.cc/virtual/2022/poster/17543},
}

@inproceedings{aaai2024_how_6221,
  title = {{How to Evaluate Behavioral Models}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2024},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/28820},
}

@inproceedings{icml2024_regression_43469,
  title = {{Regression with Multi-Expert Deferral}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2024},
  url = {https://icml.cc/virtual/2024/poster/34983},
}

@inproceedings{neurips2024_stochastic_74436,
  title = {{Stochastic Optimal Control Matching}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2024},
  url = {https://neurips.cc/virtual/2024/poster/93135},
}

@inproceedings{aaai2024_a_5070,
  title = {{A Survey of Learning Criteria Going beyond the Usual Risk (Abstract Reprint)}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2024},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/30598},
}

@inproceedings{aaai2025_altbi_7979,
  title = {{ALTBI: Constructing Improved Outlier Detection Models via Optimization of Inlier-Memorization Effect}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2025},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/33256},
}

@inproceedings{icml2024_understanding_45649,
  title = {{Understanding the Training Speedup from Sampling with Approximate Losses}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2024},
  url = {https://icml.cc/virtual/2024/poster/32803},
}

@inproceedings{icml2025_mastering_45957,
  title = {{Mastering Multiple-Expert Routing: Realizable \$H\$-Consistency and Strong Guarantees for Learning to Defer}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2025},
  url = {https://icml.cc/virtual/2025/poster/46589},
}

@inproceedings{icml2025_balancing_48162,
  title = {{Balancing the Scales: A Theoretical and Algorithmic Framework for Learning from Imbalanced Data}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2025},
  url = {https://icml.cc/virtual/2025/poster/44448},
}

@inproceedings{neurips2024_initializing_71419,
  title = {{Initializing Services in Interactive ML Systems for Diverse Users}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2024},
  url = {https://neurips.cc/virtual/2024/poster/95834},
}

@inproceedings{neurips2024_fastsurvival_72153,
  title = {{FastSurvival: Hidden Computational Blessings in Training Cox Proportional Hazards Models}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2024},
  url = {https://neurips.cc/virtual/2024/poster/95183},
}

@inproceedings{neurips2025_on_75814,
  title = {{On the Entropy Calibration of Language Models}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2025},
  url = {https://openreview.net/forum?id=CGLoEvCllI},
}

@inproceedings{neurips2025_preference_76537,
  title = {{Preference Learning with Response Time: Robust Losses and Guarantees}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2025},
  url = {https://openreview.net/forum?id=KR2zKdlEJ2},
}

@inproceedings{neurips2025_accelerating_76603,
  title = {{Accelerating Optimization via Differentiable Stopping Time}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2025},
  url = {https://openreview.net/forum?id=L51U5RSFBo},
}

@inproceedings{neurips2025_sampled_80267,
  title = {{Sampled Estimators For Softmax Must Be Biased}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2025},
  url = {https://openreview.net/forum?id=xtKNbPTnMA},
}

@inproceedings{iclr2026_generalizing_85091,
  title = {{Generalizing Linear Autoencoder Recommenders with Decoupled Expected Quadratic Loss}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2026},
  url = {https://openreview.net/forum?id=ANH044Wdje},
}

@inproceedings{icml2024_exploiting_44778,
  title = {{Exploiting Human-AI Dependence for Learning to Defer}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2024},
  url = {https://icml.cc/virtual/2024/poster/33674},
}

@inproceedings{icml2024_understanding_44866,
  title = {{Understanding the Impact of Introducing Constraints at Inference Time on Generalization Error}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2024},
  url = {https://icml.cc/virtual/2024/poster/33586},
}

@inproceedings{icml2025_towards_46947,
  title = {{Towards Memorization Estimation: Fast, Formal and Free}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2025},
  url = {https://icml.cc/virtual/2025/poster/45633},
}