% Key papers for Gradient descent ascent for minimax (scime atlas, https://atlas.scime.ai). Authors are not included: add them from the paper pages.

@inproceedings{icml2020_on_38532,
  title = {{On Gradient Descent Ascent for Nonconvex-Concave Minimax Problems}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2020},
  url = {https://icml.cc/virtual/2020/poster/5790},
}

@inproceedings{icml2018_the_37038,
  title = {{The Mechanics of n-Player Differentiable Games}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2018},
  url = {https://icml.cc/virtual/2018/poster/2030},
}

@inproceedings{iclr2018_training_28599,
  title = {{Training GANs with Optimism}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2018},
  url = {https://iclr.cc/virtual/2018/poster/127},
}

@inproceedings{iclr2019_optimistic_28800,
  title = {{Optimistic mirror descent in saddle-point problems: Going the extra (gradient) mile}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2019},
  url = {https://iclr.cc/virtual/2019/poster/840},
}

@inproceedings{icml2020_what_38933,
  title = {{What is Local Optimality in Nonconvex-Nonconcave Minimax Optimization?}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2020},
  url = {https://icml.cc/virtual/2020/poster/6298},
}

@inproceedings{neurips2020_optimal_60532,
  title = {{Optimal Epoch Stochastic Gradient Descent Ascent Methods for Min-Max Optimization}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2020},
  url = {https://nips.cc/virtual/2020/poster/18568},
}

@inproceedings{iclr2020_towards_29655,
  title = {{Towards Better Understanding of Adaptive Gradient Algorithms in Generative Adversarial Nets}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2020},
  url = {https://openreview.net/forum?id=SJxIm0VtwH},
}

@inproceedings{neurips2020_a_59272,
  title = {{A Single-Loop Smoothed Gradient Descent-Ascent Algorithm for Nonconvex-Concave Min-Max Problems}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2020},
  url = {https://nips.cc/virtual/2020/poster/18215},
}

@inproceedings{neurips2018_the_57689,
  title = {{The Limit Points of (Optimistic) Gradient Descent in Min-Max Optimization}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2018},
  url = {https://nips.cc/virtual/2018/poster/11880},
}

@inproceedings{neurips2020_optimistic_60544,
  title = {{Optimistic Dual Extrapolation for Coherent Non-monotone Variational Inequalities}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2020},
  url = {https://nips.cc/virtual/2020/poster/17896},
}

@inproceedings{iclr2021_proximal_30204,
  title = {{Proximal Gradient Descent-Ascent: Variable Convergence under KŁ Geometry}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2021},
  url = {https://iclr.cc/virtual/2021/poster/2697},
}

@inproceedings{icml2021_stability_39978,
  title = {{Stability and Generalization of Stochastic Gradient Methods for Minimax Problems}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2021},
  url = {https://icml.cc/virtual/2021/poster/10015},
}

@inproceedings{icml2020_generative_38250,
  title = {{Generative Adversarial Imitation Learning with Neural Network Parameterization: Global Optimality and Convergence Rate}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2020},
  url = {https://icml.cc/virtual/2020/poster/6313},
}

@inproceedings{icml2021_train_40054,
  title = {{Train simultaneously, generalize better: Stability of gradient-based minimax learners}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2021},
  url = {https://icml.cc/virtual/2021/poster/10111},
}

@inproceedings{neurips2021_fast_61562,
  title = {{Fast Extra Gradient Methods for Smooth Structured Nonconvex-Nonconcave Minimax Problems}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2021},
  url = {https://nips.cc/virtual/2021/poster/27781},
}

@inproceedings{icml2018_k_36766,
  title = {{K-Beam Minimax: Efficient Optimization for Deep Adversarial Learning}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2018},
  url = {https://icml.cc/virtual/2018/poster/1962},
}

@inproceedings{iclr2022_hidden_31507,
  title = {{Hidden Convexity of Wasserstein GANs: Interpretable Generative Models with Closed-Form Solutions}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2022},
  url = {https://iclr.cc/virtual/2022/poster/6284},
}

@inproceedings{neurips2022_a_65007,
  title = {{A Communication-efficient Algorithm with Linear Convergence for Federated Minimax Learning}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2022},
  url = {https://nips.cc/virtual/2022/poster/52831},
}

@inproceedings{neurips2022_what_66361,
  title = {{What is a Good Metric to Study Generalization of Minimax Learners?}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2022},
  url = {https://nips.cc/virtual/2022/poster/52959},
}

@inproceedings{neurips2023_universal_67206,
  title = {{Universal Gradient Descent Ascent Method for Nonconvex-Nonconcave Minimax Optimization}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2023},
  url = {https://nips.cc/virtual/2023/poster/72508},
}

@inproceedings{icml2024_fundamental_45527,
  title = {{Fundamental Benefit of Alternating Updates in Minimax Optimization}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2024},
  url = {https://icml.cc/virtual/2024/poster/32925},
}

@inproceedings{iclr2024_weaker_34430,
  title = {{Weaker MVI Condition: Extragradient Methods with Multi-Step Exploration}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2024},
  url = {https://iclr.cc/virtual/2024/poster/18645},
}

@inproceedings{icml2024_delving_44367,
  title = {{Delving into the Convergence of Generalized Smooth Minimax Optimization}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2024},
  url = {https://icml.cc/virtual/2024/poster/34085},
}

@inproceedings{icml2025_convergence_48927,
  title = {{Convergence of Mean-Field Langevin Stochastic Descent-Ascent for Distributional Minimax Optimization}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2025},
  url = {https://icml.cc/virtual/2025/poster/43696},
}