% Key papers for Constrained neural network optimization (scime atlas, https://atlas.scime.ai). Authors are not included: add them from the paper pages.

@inproceedings{neurips2019_neural_58664,
  title = {{Neural Lyapunov Control}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2019},
  url = {https://nips.cc/virtual/2019/poster/13504},
}

@inproceedings{iclr2020_augmenting_29430,
  title = {{Augmenting Genetic Algorithms with Deep Neural Networks for Exploring the Chemical Space}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2020},
  url = {https://openreview.net/forum?id=H1lmyRNFvr},
}

@inproceedings{icml2019_cheap_37217,
  title = {{Cheap Orthogonal Constraints in Neural Networks: A Simple Parametrization of the Orthogonal and Unitary Group}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2019},
  url = {https://icml.cc/virtual/2019/poster/3707},
}

@inproceedings{aaai2016_derivative_105117,
  title = {{Derivative-Free Optimization via Classification}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2016},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/10289},
}

@inproceedings{iclr2017_hyperband_28381,
  title = {{Hyperband: Bandit-Based Configuration Evaluation for Hyperparameter Optimization}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2017},
  url = {https://openreview.net/forum?id=ry18Ww5ee},
}

@inproceedings{cvpr2018_a_12789,
  title = {{A Constrained Deep Neural Network for Ordinal Regression}},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2018},
  url = {https://openaccess.thecvf.com/content_cvpr_2018/html/Liu_A_Constrained_Deep_CVPR_2018_paper.html},
}

@inproceedings{iclr2021_dc3_30364,
  title = {{DC3: A learning method for optimization with hard constraints}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2021},
  url = {https://iclr.cc/virtual/2021/poster/2868},
}

@inproceedings{iclr2018_measuring_28728,
  title = {{Measuring the Intrinsic Dimension of Objective Landscapes}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2018},
  url = {https://iclr.cc/virtual/2018/poster/122},
}

@inproceedings{neurips2020_on_60485,
  title = {{On the Role of Sparsity and DAG Constraints for Learning Linear DAGs}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2020},
  url = {https://nips.cc/virtual/2020/poster/17401},
}

@inproceedings{neurips2020_on_60473,
  title = {{On Warm-Starting Neural Network Training}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2020},
  url = {https://nips.cc/virtual/2020/poster/17983},
}

@inproceedings{cvpr2019_ecc_14123,
  title = {{ECC: Platform-Independent Energy-Constrained Deep Neural Network Compression via a Bilinear Regression Model}},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2019},
}

@inproceedings{aaai2017_deep_103404,
  title = {{Deep Learning Quadcopter Control via Risk-Aware Active Learning}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2017},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/11041},
}

@inproceedings{aaai2019_learning_101629,
  title = {{Learning Logistic Circuits}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2019},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/4336},
}

@inproceedings{neurips2020_dags_59631,
  title = {{DAGs with No Fears: A Closer Look at Continuous Optimization for Learning Bayesian Networks}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2020},
  url = {https://nips.cc/virtual/2020/poster/18218},
}

@inproceedings{icml2020_optimizer_38596,
  title = {{Optimizer Benchmarking Needs to Account for Hyperparameter Tuning}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2020},
  url = {https://icml.cc/virtual/2020/poster/6589},
}

@inproceedings{iclr2018_hyperparameter_28504,
  title = {{Hyperparameter optimization: a spectral approach}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2018},
  url = {https://iclr.cc/virtual/2018/poster/280},
}

@inproceedings{iclr2019_detecting_28964,
  title = {{Detecting Egregious Responses in Neural Sequence-to-sequence Models}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2019},
  url = {https://iclr.cc/virtual/2019/poster/861},
}

@inproceedings{neurips2020_benchmarking_59451,
  title = {{Benchmarking Deep Inverse Models over time, and the Neural-Adjoint method}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2020},
  url = {https://nips.cc/virtual/2020/poster/17990},
}

@inproceedings{iclr2020_chameleon_29880,
  title = {{Chameleon: Adaptive Code Optimization for Expedited Deep Neural Network Compilation}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2020},
  url = {https://openreview.net/forum?id=rygG4AVFvH},
}

@inproceedings{neurips2022_do_64179,
  title = {{Do Current Multi-Task Optimization Methods in Deep Learning Even Help?}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2022},
  url = {https://nips.cc/virtual/2022/poster/52900},
}

@inproceedings{icml2024_physics_45546,
  title = {{Physics-Informed Neural Network Policy Iteration: Algorithms, Convergence, and Verification}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2024},
  url = {https://icml.cc/virtual/2024/poster/32906},
}

@inproceedings{iclr2024_guaranteed_34395,
  title = {{Guaranteed Approximation Bounds for Mixed-Precision Neural Operators}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2024},
  url = {https://iclr.cc/virtual/2024/poster/18680},
}

@inproceedings{neurips2024_towards_73359,
  title = {{Towards Scalable and Stable Parallelization of Nonlinear RNNs}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2024},
  url = {https://neurips.cc/virtual/2024/poster/94089},
}

@inproceedings{aaai2025_a_7896,
  title = {{A Hybrid Framework for Airfoil Optimization: Combining PINNs and Genetic Algorithm (Student Abstract)}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2025},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/35293},
}

@inproceedings{iclr2024_colide_34882,
  title = {{CoLiDE: Concomitant Linear DAG Estimation}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2024},
  url = {https://iclr.cc/virtual/2024/poster/18192},
}

@inproceedings{icml2024_autonomous_43248,
  title = {{Autonomous Sparse Mean-CVaR Portfolio Optimization}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2024},
  url = {https://icml.cc/virtual/2024/poster/35204},
}

@inproceedings{icml2024_a_43585,
  title = {{A General Framework for Learning from Weak Supervision}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2024},
  url = {https://icml.cc/virtual/2024/poster/34867},
}

@inproceedings{icml2024_trained_44867,
  title = {{Trained Random Forests Completely Reveal your Dataset}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2024},
  url = {https://icml.cc/virtual/2024/poster/33585},
}

@inproceedings{icml2025_discrete_46855,
  title = {{Discrete Neural Algorithmic Reasoning}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2025},
  url = {https://icml.cc/virtual/2025/poster/45721},
}

@inproceedings{neurips2024_learning_73931,
  title = {{Learning to Solve Quadratic Unconstrained Binary Optimization in a Classification Way}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2024},
  url = {https://neurips.cc/virtual/2024/poster/93580},
}

@inproceedings{neurips2024_a_73934,
  title = {{A Globally Optimal Portfolio for m-Sparse Sharpe Ratio Maximization}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2024},
  url = {https://neurips.cc/virtual/2024/poster/93577},
}

@inproceedings{neurips2024_optimization_74390,
  title = {{Optimization Can Learn Johnson Lindenstrauss Embeddings}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2024},
  url = {https://neurips.cc/virtual/2024/poster/93177},
}

@inproceedings{neurips2024_rethinking_74659,
  title = {{Rethinking Deep Thinking: Stable Learning of Algorithms using Lipschitz Constraints}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2024},
  url = {https://neurips.cc/virtual/2024/poster/92936},
}

@inproceedings{neurips2025_enforcing_78638,
  title = {{Enforcing Hard Linear Constraints in Deep Learning Models with Decision Rules}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2025},
  url = {https://openreview.net/forum?id=gjiCml2CNG},
}

@inproceedings{neurips2025_fast_79064,
  title = {{Fast Computation and Optimization for Opinion-Based Quantities of Friedkin-Johnsen Model}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2025},
  url = {https://openreview.net/forum?id=lCsVtkMusN},
}

@inproceedings{neurips2026_position_91470,
  title = {{Position: Adopt Constraints Over Fixed Penalties in Deep Learning}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2026},
  url = {https://neurips.cc/virtual/2026/poster/140086},
}

@inproceedings{iclr2024_learning_33666,
  title = {{Learning to Relax: Setting Solver Parameters Across a Sequence of Linear System Instances}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2024},
  url = {https://iclr.cc/virtual/2024/poster/19409},
}

@inproceedings{iclr2024_neur2ro_34491,
  title = {{Neur2RO: Neural Two-Stage Robust Optimization}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2024},
  url = {https://iclr.cc/virtual/2024/poster/18583},
}

@inproceedings{iclr2024_near_34881,
  title = {{Near-Optimal Solutions of Constrained Learning Problems}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2024},
  url = {https://iclr.cc/virtual/2024/poster/18193},
}

@inproceedings{icml2024_symmetry_43560,
  title = {{Symmetry Induces Structure and Constraint of Learning}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2024},
  url = {https://icml.cc/virtual/2024/poster/34892},
}