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

@inproceedings{neurips2018_spider_57595,
  title = {{SPIDER: Near-Optimal Non-Convex Optimization via Stochastic Path-Integrated Differential Estimator}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2018},
  url = {https://nips.cc/virtual/2018/poster/11091},
}

@inproceedings{neurips2016_a_55529,
  title = {{A Comprehensive Linear Speedup Analysis for Asynchronous Stochastic Parallel Optimization from Zeroth-Order to First-Order}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2016},
  url = {https://nips.cc/virtual/2016/poster/6993},
}

@inproceedings{neurips2018_graph_57186,
  title = {{Graph Oracle Models, Lower Bounds, and Gaps for Parallel Stochastic Optimization}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2018},
  url = {https://nips.cc/virtual/2018/poster/11812},
}

@inproceedings{aaai2019_differentially_100204,
  title = {{Differentially Private Empirical Risk Minimization with Smooth Non-Convex Loss Functions: A Non-Stationary View}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2019},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/3912},
}

@inproceedings{neurips2018_how_57209,
  title = {{How To Make the Gradients Small Stochastically: Even Faster Convex and Nonconvex SGD}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2018},
  url = {https://nips.cc/virtual/2018/poster/11134},
}

@inproceedings{icml2021_page_39763,
  title = {{PAGE: A Simple and Optimal Probabilistic Gradient Estimator for Nonconvex Optimization}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2021},
  url = {https://icml.cc/virtual/2021/poster/8449},
}

@inproceedings{neurips2016_accelerating_55555,
  title = {{Accelerating Stochastic Composition Optimization}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2016},
  url = {https://nips.cc/virtual/2016/poster/7395},
}

@inproceedings{neurips2017_gradient_56349,
  title = {{Gradient Methods for Submodular Maximization}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2017},
  url = {https://nips.cc/virtual/2017/poster/9357},
}

@inproceedings{aaai2018_accelerated_102528,
  title = {{Accelerated Method for Stochastic Composition Optimization With Nonsmooth Regularization}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2018},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/11795},
}

@inproceedings{neurips2019_the_59072,
  title = {{The Randomized Midpoint Method for Log-Concave Sampling}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2019},
  url = {https://nips.cc/virtual/2019/poster/14500},
}

@inproceedings{aaai2022_policy_2560,
  title = {{Policy Optimization with Stochastic Mirror Descent}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2022},
  url = {https://aaai.org/papers/08823-policy-optimization-with-stochastic-mirror-descent/},
}

@inproceedings{icml2018_faster_36695,
  title = {{Faster Derivative-Free Stochastic Algorithm for Shared Memory Machines}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2018},
  url = {https://icml.cc/virtual/2018/poster/2022},
}

@inproceedings{neurips2017_stochastic_56688,
  title = {{Stochastic Approximation for Canonical Correlation Analysis}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2017},
  url = {https://nips.cc/virtual/2017/poster/9254},
}

@inproceedings{neurips2020_biased_59465,
  title = {{Biased Stochastic First-Order Methods for Conditional Stochastic Optimization and Applications in Meta Learning}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2020},
  url = {https://nips.cc/virtual/2020/poster/17482},
}

@inproceedings{aaai2020_d_100655,
  title = {{D-SPIDER-SFO: A Decentralized Optimization Algorithm with Faster Convergence Rate for Nonconvex Problems}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2020},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/5523},
}

@inproceedings{aaai2023_proximal_4442,
  title = {{Proximal Stochastic Recursive Momentum Methods for Nonconvex Composite Decentralized Optimization}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2023},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/26087},
}

@inproceedings{icml2020_on_38529,
  title = {{On Differentially Private Stochastic Convex Optimization with Heavy-tailed Data}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2020},
  url = {https://icml.cc/virtual/2020/poster/5948},
}

@inproceedings{neurips2019_efficient_58198,
  title = {{Efficient Smooth Non-Convex Stochastic Compositional Optimization via Stochastic Recursive Gradient Descent}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2019},
  url = {https://nips.cc/virtual/2019/poster/13794},
}

@inproceedings{icml2021_private_39812,
  title = {{Private Stochastic Convex Optimization: Optimal Rates in L1 Geometry}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2021},
  url = {https://icml.cc/virtual/2021/poster/9989},
}

@inproceedings{neurips2019_stochastic_59023,
  title = {{Stochastic Continuous Greedy ++:  When Upper and Lower Bounds Match}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2019},
  url = {https://nips.cc/virtual/2019/poster/14251},
}

@inproceedings{aaai2024_trust_7657,
  title = {{Trust Region Methods for Nonconvex Stochastic Optimization beyond Lipschitz Smoothness}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2024},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/29537},
}

@inproceedings{neurips2024_on_74474,
  title = {{On Convergence of Adam for Stochastic Optimization under Relaxed Assumptions}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2024},
  url = {https://neurips.cc/virtual/2024/poster/93100},
}

@inproceedings{aaai2024_large_6425,
  title = {{Large-Scale Non-convex Stochastic Constrained Distributionally Robust Optimization}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2024},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/28662},
}

@inproceedings{iclr2024_advancing_35270,
  title = {{Advancing the Lower Bounds: an Accelerated, Stochastic, Second-order Method with Optimal Adaptation to Inexactness}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2024},
  url = {https://iclr.cc/virtual/2024/poster/17803},
}

@inproceedings{icml2024_tuning_43666,
  title = {{Tuning-Free Stochastic Optimization}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2024},
  url = {https://icml.cc/virtual/2024/poster/34786},
}

@inproceedings{neurips2024_gradient_72388,
  title = {{Gradient-Free Methods for Nonconvex Nonsmooth Stochastic Compositional Optimization}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2024},
  url = {https://neurips.cc/virtual/2024/poster/94965},
}

@inproceedings{aaai2025_differential_8735,
  title = {{Differential Private Stochastic Optimization with Heavy-tailed Data: Towards Optimal Rates}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2025},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/34440},
}

@inproceedings{aaai2025_zeroth_11329,
  title = {{Zeroth-Order Methods for Nonconvex Stochastic Problems with Decision-Dependent Distributions}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2025},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/33890},
}

@inproceedings{icml2024_how_43525,
  title = {{How Free is Parameter-Free Stochastic Optimization?}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2024},
  url = {https://icml.cc/virtual/2024/poster/34927},
}

@inproceedings{icml2024_faster_43578,
  title = {{Faster Sampling via Stochastic Gradient Proximal Sampler}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2024},
  url = {https://icml.cc/virtual/2024/poster/34874},
}

@inproceedings{icml2024_two_43695,
  title = {{Two-timescale Derivative Free Optimization for Performative Prediction with Markovian Data}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2024},
  url = {https://icml.cc/virtual/2024/poster/34757},
}

@inproceedings{icml2024_information_43803,
  title = {{Information Complexity of Stochastic Convex Optimization: Applications to Generalization, Memorization, and Tracing}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2024},
  url = {https://icml.cc/virtual/2024/poster/34649},
}

@inproceedings{icml2024_how_44648,
  title = {{How to Make the Gradients Small Privately: Improved Rates for Differentially Private Non-Convex Optimization}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2024},
  url = {https://icml.cc/virtual/2024/poster/33804},
}

@inproceedings{icml2025_global_46200,
  title = {{Global Optimization with a Power-Transformed Objective and Gaussian Smoothing}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2025},
  url = {https://icml.cc/virtual/2025/poster/46360},
}

@inproceedings{icml2025_a_46889,
  title = {{A Parameter-Free and Near-Optimal Zeroth-Order Algorithm for Stochastic Convex Optimization}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2025},
  url = {https://icml.cc/virtual/2025/poster/45690},
}

@inproceedings{icml2026_all_51422,
  title = {{All ERMs Can Fail in Stochastic Convex Optimization Lower Bounds in Linear Dimension}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2026},
  url = {https://openreview.net/forum?id=Lzwp2KXedc},
}

@inproceedings{icml2026_gradient_52015,
  title = {{Gradient-Free Approaches is a Key to an Efficient Interaction with Markovian Stochasticity}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2026},
  url = {https://openreview.net/forum?id=S1TwW9szYm},
}

@inproceedings{neurips2024_the_70263,
  title = {{The Sample Complexity of Gradient Descent in Stochastic Convex Optimization}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2024},
  url = {https://neurips.cc/virtual/2024/poster/96826},
}

@inproceedings{neurips2024_stochastic_70272,
  title = {{Stochastic Optimization Algorithms for Instrumental Variable Regression with Streaming Data}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2024},
  url = {https://neurips.cc/virtual/2024/poster/96817},
}

@inproceedings{neurips2024_high_72603,
  title = {{High-probability complexity bounds for stochastic non-convex minimax optimization}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2024},
  url = {https://neurips.cc/virtual/2024/poster/94774},
}