% Key papers for Sparse and robust PCA (scime atlas, https://atlas.scime.ai). Authors are not included: add them from the paper pages.

@inproceedings{neurips2016_dimensionality_55682,
  title = {{Dimensionality Reduction of Massive Sparse Datasets Using Coresets}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2016},
  url = {https://nips.cc/virtual/2016/poster/7122},
}

@inproceedings{icml2016_robust_35969,
  title = {{Robust Principal Component Analysis with Side Information}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2016},
  url = {https://proceedings.mlr.press/v48/chiang16.html},
}

@inproceedings{icml2017_communication_36101,
  title = {{Communication-efficient Algorithms for Distributed Stochastic Principal Component Analysis}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2017},
  url = {https://icml.cc/virtual/2017/poster/559},
}

@inproceedings{aaai2017_two_104421,
  title = {{Two-Dimensional PCA with F-Norm Minimization}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2017},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/10798},
}

@inproceedings{aaai2017_beyond_104413,
  title = {{Beyond RPCA: Flattening Complex Noise in the Frequency Domain}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2017},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/10790},
}

@inproceedings{neurips2019_on_58715,
  title = {{On Robustness of Principal Component Regression}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2019},
  url = {https://nips.cc/virtual/2019/poster/13232},
}

@inproceedings{neurips2021_learned_61762,
  title = {{Learned Robust PCA: A Scalable Deep Unfolding Approach for High-Dimensional Outlier Detection}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2021},
  url = {https://nips.cc/virtual/2021/poster/26814},
}

@inproceedings{aaai2018_robust_102412,
  title = {{Robust Formulation for PCA: Avoiding Mean Calculation With L2,p-norm Maximization}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2018},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/11679},
}

@inproceedings{neurips2019_manifold_58571,
  title = {{Manifold denoising by Nonlinear Robust Principal Component Analysis}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2019},
  url = {https://nips.cc/virtual/2019/poster/14278},
}

@inproceedings{icml2017_doubly_36162,
  title = {{Doubly Accelerated Methods for Faster CCA and Generalized Eigendecomposition}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2017},
  url = {https://icml.cc/virtual/2017/poster/485},
}

@inproceedings{aaai2019_a_100603,
  title = {{A Nonconvex Projection Method for Robust PCA}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2019},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/3959},
}

@inproceedings{cvpr2017_intrinsic_12363,
  title = {{Intrinsic Grassmann Averages for Online Linear and Robust Subspace Learning}},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2017},
}

@inproceedings{cvpr2016_an_11396,
  title = {{An Efficient Exact-PGA Algorithm for Constant Curvature Manifolds}},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2016},
}

@inproceedings{neurips2019_outlier_58777,
  title = {{Outlier Detection and Robust PCA Using a Convex Measure of Innovation}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2019},
  url = {https://nips.cc/virtual/2019/poster/14343},
}

@inproceedings{neurips2021_pca_61731,
  title = {{PCA Initialization for Approximate Message Passing in Rotationally Invariant Models}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2021},
  url = {https://nips.cc/virtual/2021/poster/27778},
}

@inproceedings{neurips2020_robust_60746,
  title = {{Robust Sub-Gaussian Principal Component Analysis and Width-Independent Schatten Packing}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2020},
  url = {https://nips.cc/virtual/2020/poster/19053},
}

@inproceedings{icml2016_principal_35957,
  title = {{Principal Component Projection Without Principal Component Analysis}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2016},
  url = {https://proceedings.mlr.press/v48/frostig16.html},
}

@inproceedings{icml2017_coherence_36096,
  title = {{Coherence Pursuit: Fast, Simple, and Robust Subspace Recovery}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2017},
  url = {https://icml.cc/virtual/2017/poster/527},
}

@inproceedings{icml2017_hyperplane_36222,
  title = {{Hyperplane Clustering via Dual Principal Component Pursuit}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2017},
  url = {https://icml.cc/virtual/2017/poster/736},
}

@inproceedings{aaai2021_self_1352,
  title = {{Self-Paced Two-dimensional PCA}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2021},
  url = {https://aaai.org/papers/08392-self-paced-two-dimensional-pca/},
}

@inproceedings{cvpr2024_fun_23532,
  title = {{Fun with Flags: Robust Principal Directions via Flag Manifolds}},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2024},
  url = {https://cvpr.thecvf.com/virtual/2024/poster/30306},
}

@inproceedings{neurips2024_on_71784,
  title = {{On Sparse Canonical Correlation Analysis}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2024},
  url = {https://neurips.cc/virtual/2024/poster/95509},
}

@inproceedings{neurips2024_the_72728,
  title = {{The tree autoencoder model, with application to hierarchical data visualization}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2024},
  url = {https://neurips.cc/virtual/2024/poster/94661},
}

@inproceedings{neurips2024_oja_73034,
  title = {{Oja's Algorithm for Streaming Sparse PCA}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2024},
  url = {https://neurips.cc/virtual/2024/poster/94389},
}

@inproceedings{icml2024_on_43610,
  title = {{On the Error-Propagation of Inexact Hotelling's Deflation for Principal Component Analysis}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2024},
  url = {https://icml.cc/virtual/2024/poster/34842},
}

@inproceedings{icml2026_mean_51060,
  title = {{Mean-Shift PCA by Knockoff Mean}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2026},
  url = {https://openreview.net/forum?id=ISNSiAC3n1},
}

@inproceedings{icml2026_combinatorial_51305,
  title = {{Combinatorial Sparse PCA Beyond the Spiked Identity Model}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2026},
  url = {https://openreview.net/forum?id=Kk5UZgkWFx},
}

@inproceedings{icml2026_global_52606,
  title = {{Global Convergence of Adaptive Sensing for Principal Eigenvector Estimation}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2026},
  url = {https://openreview.net/forum?id=XXYhEGXPPF},
}

@inproceedings{neurips2025_rethinking_76330,
  title = {{Rethinking PCA Through Duality}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2025},
  url = {https://openreview.net/forum?id=IFQBrEAuQ6},
}

@inproceedings{neurips2025_nonlinear_78689,
  title = {{Nonlinear Laplacians: Tunable principal component analysis under directional prior information}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2025},
  url = {https://openreview.net/forum?id=hLZrqDFugY},
}

@inproceedings{iclr2025_efficient_81411,
  title = {{Efficient Sparse PCA via Block-Diagonalization}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2025},
  url = {https://iclr.cc/virtual/2025/poster/30361},
}

@inproceedings{iclr2025_probabilistic_83362,
  title = {{Probabilistic Geometric Principal Component Analysis with application to neural data}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2025},
  url = {https://iclr.cc/virtual/2025/poster/28454},
}

@inproceedings{aaai2026_fast_100753,
  title = {{Fast Guaranteed Robust Local-Smooth Principal Component Separation}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2026},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/42493},
}

@inproceedings{icml2025_fast_48598,
  title = {{Fast and Provable Algorithms for Sparse PCA with Improved Sample Complexity}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2025},
  url = {https://icml.cc/virtual/2025/poster/44023},
}