% Key papers for Saddle-point escape algorithms (scime atlas, https://atlas.scime.ai). Authors are not included: add them from the paper pages.

@inproceedings{icml2017_how_36221,
  title = {{How to Escape Saddle Points Efficiently}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2017},
  url = {https://icml.cc/virtual/2017/poster/640},
}

@inproceedings{icml2018_escaping_36674,
  title = {{Escaping Saddles with Stochastic Gradients}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2018},
  url = {https://icml.cc/virtual/2018/poster/2265},
}

@inproceedings{neurips2019_efficiently_58207,
  title = {{Efficiently escaping saddle points on manifolds}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2019},
  url = {https://nips.cc/virtual/2019/poster/13718},
}

@inproceedings{neurips2018_escaping_57101,
  title = {{Escaping Saddle Points in Constrained Optimization}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2018},
  url = {https://nips.cc/virtual/2018/poster/11363},
}

@inproceedings{neurips2019_escaping_58224,
  title = {{Escaping from saddle points on Riemannian manifolds}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2019},
  url = {https://nips.cc/virtual/2019/poster/13817},
}

@inproceedings{icml2019_escaping_37340,
  title = {{Escaping Saddle Points with Adaptive Gradient Methods}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2019},
  url = {https://icml.cc/virtual/2019/poster/4059},
}

@inproceedings{neurips2018_first_57137,
  title = {{First-order Stochastic Algorithms for Escaping From Saddle Points in Almost Linear Time}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2018},
  url = {https://nips.cc/virtual/2018/poster/11540},
}

@inproceedings{icml2019_multiplicative_37558,
  title = {{Multiplicative Weights Updates as a distributed constrained optimization algorithm: Convergence to second-order stationary points almost always}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2019},
  url = {https://icml.cc/virtual/2019/poster/3976},
}

@inproceedings{neurips2019_efficiently_58206,
  title = {{Efficiently avoiding saddle points with zero order methods: No gradients required}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2019},
  url = {https://nips.cc/virtual/2019/poster/14028},
}

@inproceedings{neurips2018_adaptive_56838,
  title = {{Adaptive Negative Curvature Descent with Applications in Non-convex Optimization}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2018},
  url = {https://nips.cc/virtual/2018/poster/11476},
}

@inproceedings{iclr2019_efficiently_28981,
  title = {{Efficiently testing local optimality and escaping saddles for ReLU networks}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2019},
  url = {https://iclr.cc/virtual/2019/poster/957},
}

@inproceedings{neurips2021_escape_63051,
  title = {{Escape saddle points by a simple gradient-descent based algorithm}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2021},
  url = {https://nips.cc/virtual/2021/poster/26393},
}

@inproceedings{neurips2020_escaping_59795,
  title = {{Escaping Saddle-Point Faster under Interpolation-like Conditions}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2020},
  url = {https://nips.cc/virtual/2020/poster/17323},
}

@inproceedings{neurips2023_projection_68400,
  title = {{Projection-Free Methods for Solving Nonconvex-Concave Saddle Point Problems}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2023},
  url = {https://nips.cc/virtual/2023/poster/71413},
}

@inproceedings{icml2023_escaping_42985,
  title = {{Escaping saddle points in zeroth-order optimization: the power of two-point estimators}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2023},
  url = {https://icml.cc/virtual/2023/poster/24925},
}

@inproceedings{icml2024_convergence_43565,
  title = {{Convergence and Complexity Guarantee for Inexact First-order Riemannian Optimization Algorithms}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2024},
  url = {https://icml.cc/virtual/2024/poster/34887},
}

@inproceedings{icml2024_riemannian_44987,
  title = {{Riemannian Accelerated Zeroth-order Algorithm: Improved Robustness and Lower Query Complexity}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2024},
  url = {https://icml.cc/virtual/2024/poster/33465},
}

@inproceedings{neurips2019_ssrgd_58933,
  title = {{SSRGD: Simple Stochastic Recursive Gradient Descent for Escaping Saddle Points}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2019},
  url = {https://nips.cc/virtual/2019/poster/13368},
}

@inproceedings{neurips2022_zeroth_64985,
  title = {{Zeroth-Order Negative Curvature Finding: Escaping Saddle Points without Gradients}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2022},
  url = {https://nips.cc/virtual/2022/poster/54629},
}

@inproceedings{neurips2025_hessian_78602,
  title = {{Hessian-guided Perturbed Wasserstein Gradient Flows for Escaping Saddle Points}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2025},
  url = {https://openreview.net/forum?id=gOUAak6f2P},
}

@inproceedings{neurips2025_efficiently_78732,
  title = {{Efficiently Escaping Saddle Points under Generalized Smoothness via Self-Bounding Regularity}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2025},
  url = {https://openreview.net/forum?id=hhjnvIfvpa},
}

@inproceedings{iclr2025_second_83127,
  title = {{Second-Order Min-Max Optimization with Lazy Hessians}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2025},
  url = {https://iclr.cc/virtual/2025/poster/28680},
}