% Key papers for Differentiating through inner loops (scime atlas, https://atlas.scime.ai). Authors are not included: add them from the paper pages.

@inproceedings{icml2018_learning_36815,
  title = {{Learning to Reweight Examples for Robust Deep Learning}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2018},
  url = {https://icml.cc/virtual/2018/poster/1991},
}

@inproceedings{icml2019_sever_37738,
  title = {{Sever: A Robust Meta-Algorithm for Stochastic Optimization}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2019},
  url = {https://icml.cc/virtual/2019/poster/3572},
}

@inproceedings{icml2016_hyperparameter_35869,
  title = {{Hyperparameter optimization with approximate gradient}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2016},
  url = {https://proceedings.mlr.press/v48/pedregosa16.html},
}

@inproceedings{icml2017_forward_36198,
  title = {{Forward and Reverse Gradient-Based Hyperparameter Optimization}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2017},
  url = {https://icml.cc/virtual/2017/poster/704},
}

@inproceedings{icml2020_implicit_38301,
  title = {{Implicit differentiation of Lasso-type models for hyperparameter optimization}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2020},
  url = {https://icml.cc/virtual/2020/poster/6062},
}

@inproceedings{cvpr2020_learning_15820,
  title = {{Learning to Optimize Non-Rigid Tracking}},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2020},
}

@inproceedings{neurips2021_nonsmooth_61734,
  title = {{Nonsmooth Implicit Differentiation for Machine-Learning and Optimization}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2021},
  url = {https://nips.cc/virtual/2021/poster/27817},
}

@inproceedings{neurips2019_towards_59109,
  title = {{Towards closing the gap between the theory and practice of SVRG}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2019},
  url = {https://nips.cc/virtual/2019/poster/13298},
}

@inproceedings{icml2019_efficient_37331,
  title = {{Efficient optimization of loops and limits with randomized telescoping sums}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2019},
  url = {https://icml.cc/virtual/2019/poster/3852},
}

@inproceedings{icml2020_almost_37930,
  title = {{Almost Tune-Free Variance Reduction}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2020},
  url = {https://icml.cc/virtual/2020/poster/5825},
}

@inproceedings{aaai2022_efficient_2027,
  title = {{Efficient Riemannian Meta-Optimization by Implicit Differentiation}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2022},
  url = {https://aaai.org/papers/03733-efficient-riemannian-meta-optimization-by-implicit-differentiation/},
}

@inproceedings{neurips2020_learning_60230,
  title = {{Learning to Mutate with Hypergradient Guided Population}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2020},
  url = {https://nips.cc/virtual/2020/poster/17534},
}

@inproceedings{aaai2021_learning_902,
  title = {{Learning a Gradient-free Riemannian Optimizer on Tangent Spaces}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2021},
  url = {https://aaai.org/papers/07377-learning-a-gradient-free-riemannian-optimizer-on-tangent-spaces/},
}

@inproceedings{iclr2022_online_30791,
  title = {{Online Hyperparameter Meta-Learning with Hypergradient Distillation}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2022},
  url = {https://iclr.cc/virtual/2022/poster/6204},
}

@inproceedings{neurips2021_gradient_61360,
  title = {{Gradient-based Hyperparameter Optimization Over Long Horizons}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2021},
  url = {https://nips.cc/virtual/2021/poster/28059},
}

@inproceedings{neurips2022_the_65414,
  title = {{The Curse of Unrolling: Rate of Differentiating Through Optimization}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2022},
  url = {https://nips.cc/virtual/2022/poster/54166},
}

@inproceedings{neurips2023_neuroevobench_69651,
  title = {{NeuroEvoBench: Benchmarking Evolutionary Optimizers for Deep Learning Applications}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2023},
  url = {https://nips.cc/virtual/2023/poster/73445},
}

@inproceedings{icml2023_low_42044,
  title = {{Low-Variance Gradient Estimation in Unrolled Computation Graphs with ES-Single}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2023},
  url = {https://icml.cc/virtual/2023/poster/23675},
}

@inproceedings{neurips2016_efficient_55700,
  title = {{Efficient Globally Convergent Stochastic Optimization for Canonical Correlation Analysis}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2016},
  url = {https://nips.cc/virtual/2016/poster/7344},
}

@inproceedings{icml2022_optimization_40970,
  title = {{Optimization-Derived Learning with Essential Convergence Analysis of Training and Hyper-training}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2022},
  url = {https://icml.cc/virtual/2022/poster/17659},
}

@inproceedings{neurips2026_understanding_89599,
  title = {{Understanding the Curse of Unrolling}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2026},
  url = {https://neurips.cc/virtual/2026/poster/149289},
}