% Key papers for Active learning and subset selection (scime atlas, https://atlas.scime.ai). Authors are not included: add them from the paper pages.

@inproceedings{neurips2018_catboost_56933,
  title = {{CatBoost: unbiased boosting with categorical features}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2018},
  url = {https://nips.cc/virtual/2018/poster/11641},
}

@inproceedings{iclr2019_efficient_28935,
  title = {{Efficient Lifelong Learning with A-GEM}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2019},
  url = {https://iclr.cc/virtual/2019/poster/715},
}

@inproceedings{neurips2017_optimized_56558,
  title = {{Optimized Pre-Processing for Discrimination Prevention}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2017},
  url = {https://nips.cc/virtual/2017/poster/9180},
}

@inproceedings{iclr2020_deep_29885,
  title = {{Deep Batch Active Learning by Diverse, Uncertain Gradient Lower Bounds}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2020},
  url = {https://openreview.net/forum?id=ryghZJBKPS},
}

@inproceedings{neurips2021_contemporary_63526,
  title = {{Contemporary Symbolic Regression Methods and their Relative Performance}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2021},
  url = {https://openreview.net/forum?id=xVQMrDLyGst},
}

@inproceedings{neurips2017_learning_56412,
  title = {{Learning Active Learning from Data}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2017},
  url = {https://nips.cc/virtual/2017/poster/9202},
}

@inproceedings{icml2017_learning_36247,
  title = {{Learning Algorithms for Active Learning}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2017},
  url = {https://icml.cc/virtual/2017/poster/880},
}

@inproceedings{icml2017_iterative_36235,
  title = {{Iterative Machine Teaching}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2017},
  url = {https://icml.cc/virtual/2017/poster/858},
}

@inproceedings{neurips2019_bayesian_57938,
  title = {{Bayesian Batch Active Learning as Sparse Subset Approximation}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2019},
  url = {https://nips.cc/virtual/2019/poster/13749},
}

@inproceedings{iclr2019_learning_29129,
  title = {{Learning-Based Frequency Estimation Algorithms}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2019},
  url = {https://iclr.cc/virtual/2019/poster/874},
}

@inproceedings{iclr2019_janossy_28779,
  title = {{Janossy Pooling: Learning Deep Permutation-Invariant Functions for Variable-Size Inputs}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2019},
  url = {https://iclr.cc/virtual/2019/poster/923},
}

@inproceedings{neurips2018_a_56802,
  title = {{A Model for Learned Bloom Filters and Optimizing by Sandwiching}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2018},
  url = {https://nips.cc/virtual/2018/poster/11070},
}

@inproceedings{icml2016_geometric_35854,
  title = {{Geometric Mean Metric Learning}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2016},
  url = {https://proceedings.mlr.press/v48/zadeh16.html},
}

@inproceedings{icml2020_searching_38739,
  title = {{Searching to Exploit Memorization Effect in Learning with Noisy Labels}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2020},
  url = {https://icml.cc/virtual/2020/poster/6326},
}

@inproceedings{neurips2016_higher_55773,
  title = {{Higher-Order Factorization Machines}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2016},
  url = {https://nips.cc/virtual/2016/poster/8505},
}

@inproceedings{neurips2021_batch_63628,
  title = {{Batch Active Learning at Scale}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2021},
  url = {https://nips.cc/virtual/2021/poster/26685},
}

@inproceedings{cvpr2022_towards_20063,
  title = {{Towards Robust and Reproducible Active Learning Using Neural Networks}},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2022},
}

@inproceedings{neurips2020_curriculum_59627,
  title = {{Curriculum Learning by Dynamic Instance Hardness}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2020},
  url = {https://nips.cc/virtual/2020/poster/18575},
}

@inproceedings{icml2017_active_36046,
  title = {{Active Learning for Cost-Sensitive Classification}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2017},
  url = {https://icml.cc/virtual/2017/poster/795},
}

@inproceedings{icml2020_deltagrad_38098,
  title = {{DeltaGrad: Rapid retraining of machine learning models}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2020},
  url = {https://icml.cc/virtual/2020/poster/5915},
}

@inproceedings{neurips2024_benchmark_72714,
  title = {{Benchmark Data Repositories for Better Benchmarking}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2024},
  url = {https://neurips.cc/virtual/2024/poster/97636},
}

@inproceedings{aaai2024_active_5116,
  title = {{Active Learning Guided by Efficient Surrogate Learners}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2024},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/28961},
}

@inproceedings{aaai2024_learning_6454,
  title = {{Learning GAI-Decomposable Utility Models for Multiattribute Decision Making}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2024},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/30024},
}

@inproceedings{neurips2024_data_74523,
  title = {{Data curation via joint example selection further accelerates multimodal learning}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2024},
  url = {https://neurips.cc/virtual/2024/poster/97437},
}

@inproceedings{aaai2024_generator_6134,
  title = {{Generator Assisted Mixture of Experts for Feature Acquisition in Batch}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2024},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/28967},
}

@inproceedings{neurips2024_scaling_71837,
  title = {{Scaling laws for learning with real and surrogate data}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2024},
  url = {https://neurips.cc/virtual/2024/poster/95461},
}

@inproceedings{neurips2025_what_79460,
  title = {{What Does It Take to Build a Performant Selective Classifier?}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2025},
  url = {https://openreview.net/forum?id=pcwI5cNFJK},
}

@inproceedings{aaai2024_simple_7345,
  title = {{Simple Weak Coresets for Non-decomposable Classification Measures}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2024},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/29341},
}

@inproceedings{iclr2024_dropout_34479,
  title = {{Dropout-Based Rashomon Set Exploration for Efficient Predictive Multiplicity Estimation}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2024},
  url = {https://iclr.cc/virtual/2024/poster/18595},
}

@inproceedings{iclr2024_querying_34495,
  title = {{Querying Easily Flip-flopped Samples for Deep Active Learning}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2024},
  url = {https://iclr.cc/virtual/2024/poster/18579},
}

@inproceedings{icml2025_learning_48646,
  title = {{Learning with Selectively Labeled Data from Multiple Decision-makers}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2025},
  url = {https://icml.cc/virtual/2025/poster/43975},
}

@inproceedings{neurips2025_a_77410,
  title = {{A Reliable Cryptographic Framework for Empirical Machine Unlearning Evaluation}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2025},
  url = {https://openreview.net/forum?id=TYoYJStuN9},
}

@inproceedings{aaai2025_smle_10616,
  title = {{SMLE: Safe Machine Learning via Embedded Overapproximation}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2025},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/34938},
}

@inproceedings{iclr2024_towards_34095,
  title = {{Towards a statistical theory of data selection under weak supervision}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2024},
  url = {https://iclr.cc/virtual/2024/poster/18980},
}

@inproceedings{icml2024_early_43257,
  title = {{Early Time Classification with Accumulated Accuracy Gap Control}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2024},
  url = {https://icml.cc/virtual/2024/poster/35195},
}

@inproceedings{icml2024_diversified_43473,
  title = {{Diversified Batch Selection for Training Acceleration}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2024},
  url = {https://icml.cc/virtual/2024/poster/34979},
}

@inproceedings{icml2024_on_43608,
  title = {{On Stronger Computational Separations Between Multimodal and Unimodal Machine Learning}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2024},
  url = {https://icml.cc/virtual/2024/poster/34844},
}

@inproceedings{icml2024_a_44752,
  title = {{A New Computationally Efficient Algorithm to solve Feature Selection for Functional Data Classification in High-dimensional Spaces}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2024},
  url = {https://icml.cc/virtual/2024/poster/33700},
}

@inproceedings{icml2024_priorboost_45466,
  title = {{PriorBoost: An Adaptive Algorithm for Learning from Aggregate Responses}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2024},
  url = {https://icml.cc/virtual/2024/poster/32985},
}

@inproceedings{icml2024_refined_45789,
  title = {{Refined Coreset Selection: Towards Minimal Coreset Size under Model Performance Constraints}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2024},
  url = {https://icml.cc/virtual/2024/poster/32663},
}