% Key papers for Randomized sketching for matrices (scime atlas, https://atlas.scime.ai). Authors are not included: add them from the paper pages.

@inproceedings{neurips2017_polynomial_56575,
  title = {{Polynomial Codes: an Optimal Design for High-Dimensional Coded Matrix Multiplication}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2017},
  url = {https://nips.cc/virtual/2017/poster/9219},
}

@inproceedings{cvpr2016_efficient_11519,
  title = {{Efficient Temporal Sequence Comparison and Classification Using Gram Matrix Embeddings on a Riemannian Manifold}},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2016},
}

@inproceedings{cvpr2017_geodesic_12311,
  title = {{Geodesic Distance Descriptors}},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2017},
}

@inproceedings{icml2019_learning_37479,
  title = {{Learning Fast Algorithms for Linear Transforms Using Butterfly Factorizations}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2019},
  url = {https://icml.cc/virtual/2019/poster/3573},
}

@inproceedings{neurips2017_the_56723,
  title = {{The Unreasonable Effectiveness of Structured Random Orthogonal Embeddings}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2017},
  url = {https://nips.cc/virtual/2017/poster/8819},
}

@inproceedings{neurips2016_normalized_55898,
  title = {{Normalized Spectral Map Synchronization}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2016},
  url = {https://nips.cc/virtual/2016/poster/6977},
}

@inproceedings{neurips2022_dagma_64128,
  title = {{DAGMA: Learning DAGs via M-matrices and a Log-Determinant Acyclicity Characterization}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2022},
  url = {https://nips.cc/virtual/2022/poster/55411},
}

@inproceedings{icml2016_binary_35757,
  title = {{Binary embeddings with structured hashed projections}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2016},
  url = {https://proceedings.mlr.press/v48/choromanska16.html},
}

@inproceedings{neurips2019_asymptotics_57919,
  title = {{Asymptotics for Sketching in Least Squares Regression}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2019},
  url = {https://nips.cc/virtual/2019/poster/13535},
}

@inproceedings{neurips2019_optimal_58764,
  title = {{Optimal Sketching for Kronecker Product Regression and Low Rank Approximation}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2019},
  url = {https://nips.cc/virtual/2019/poster/13622},
}

@inproceedings{icml2016_the_36011,
  title = {{The Variational Nystrom method for large-scale spectral problems}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2016},
  url = {https://proceedings.mlr.press/v48/vladymyrov16.html},
}

@inproceedings{aaai2018_randomized_102347,
  title = {{Randomized Clustered Nystrom for Large-Scale Kernel Machines}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2018},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/11614},
}

@inproceedings{aaai2018_locality_101095,
  title = {{Locality Preserving Projection Based on F-norm}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2018},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/11518},
}

@inproceedings{neurips2017_polynomial_56576,
  title = {{Polynomial time algorithms for dual volume sampling}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2017},
  url = {https://nips.cc/virtual/2017/poster/9280},
}

@inproceedings{icml2017_frame_36200,
  title = {{Frame-based Data Factorizations}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2017},
  url = {https://icml.cc/virtual/2017/poster/722},
}

@inproceedings{neurips2018_sublinear_57664,
  title = {{Sublinear Time Low-Rank Approximation of Distance Matrices}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2018},
  url = {https://nips.cc/virtual/2018/poster/11377},
}

@inproceedings{icml2018_near_36862,
  title = {{Near Optimal Frequent Directions for Sketching Dense and Sparse Matrices}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2018},
  url = {https://icml.cc/virtual/2018/poster/2125},
}

@inproceedings{icml2016_computationally_35767,
  title = {{Computationally Efficient Nyström Approximation using Fast Transforms}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2016},
  url = {https://proceedings.mlr.press/v48/si16.html},
}

@inproceedings{neurips2020_coded_59541,
  title = {{Coded Sequential Matrix Multiplication For Straggler Mitigation}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2020},
  url = {https://nips.cc/virtual/2020/poster/18042},
}

@inproceedings{icml2018_low_36833,
  title = {{Low-Rank Riemannian Optimization on Positive Semidefinite Stochastic Matrices with Applications to Graph Clustering}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2018},
  url = {https://icml.cc/virtual/2018/poster/2061},
}

@inproceedings{cvpr2024_robust_24672,
  title = {{Robust Self-calibration of Focal Lengths from the Fundamental Matrix}},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2024},
  url = {https://cvpr.thecvf.com/virtual/2024/poster/29945},
}

@inproceedings{cvpr2024_revisiting_24655,
  title = {{Revisiting Sampson Approximations for Geometric Estimation Problems}},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2024},
  url = {https://cvpr.thecvf.com/virtual/2024/poster/31653},
}

@inproceedings{neurips2024_neuc_70758,
  title = {{Neuc-MDS: Non-Euclidean Multidimensional Scaling Through Bilinear Forms}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2024},
  url = {https://neurips.cc/virtual/2024/poster/96368},
}

@inproceedings{iclr2024_how_35518,
  title = {{How to Capture Higher-order Correlations? Generalizing Matrix Softmax Attention to Kronecker Computation}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2024},
  url = {https://iclr.cc/virtual/2024/poster/17555},
}

@inproceedings{neurips2024_distributed_74119,
  title = {{Distributed Least Squares in Small Space via Sketching and Bias Reduction}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2024},
  url = {https://neurips.cc/virtual/2024/poster/93417},
}

@inproceedings{iclr2025_matrix_82864,
  title = {{Matrix Product Sketching via Coordinated Sampling}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2025},
  url = {https://iclr.cc/virtual/2025/poster/28936},
}

@inproceedings{iclr2024_faster_35522,
  title = {{Faster Sampling from Log-Concave Densities over Polytopes via Efficient Linear Solvers}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2024},
  url = {https://iclr.cc/virtual/2024/poster/17551},
}

@inproceedings{icml2024_random_45624,
  title = {{Random matrix theory improved Fréchet mean of symmetric positive definite matrices}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2024},
  url = {https://icml.cc/virtual/2024/poster/32828},
}

@inproceedings{icml2024_high_45781,
  title = {{High-Dimensional Geometric Streaming for Nearly Low Rank Data}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2024},
  url = {https://icml.cc/virtual/2024/poster/32671},
}

@inproceedings{icml2025_understanding_45980,
  title = {{Understanding the Kronecker Matrix-Vector Complexity of Linear Algebra}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2025},
  url = {https://icml.cc/virtual/2025/poster/46566},
}

@inproceedings{icml2025_metricembedding_46478,
  title = {{MetricEmbedding: Accelerate Metric Nearness by Tropical Inner Product}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2025},
  url = {https://icml.cc/virtual/2025/poster/46088},
}

@inproceedings{neurips2024_generalization_73506,
  title = {{Generalization Bound and Learning Methods for Data-Driven Projections in Linear Programming}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2024},
  url = {https://neurips.cc/virtual/2024/poster/93970},
}

@inproceedings{neurips2024_john_73646,
  title = {{John Ellipsoids via Lazy Updates}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2024},
  url = {https://neurips.cc/virtual/2024/poster/93846},
}

@inproceedings{neurips2025_faster_75380,
  title = {{Faster Algorithms for Structured John Ellipsoid Computation}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2025},
  url = {https://openreview.net/forum?id=7RErGDbO6K},
}

@inproceedings{neurips2025_johnson_76824,
  title = {{Johnson-Lindenstrauss Lemma Beyond Euclidean Geometry}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2025},
  url = {https://openreview.net/forum?id=NLLLkiF3xa},
}

@inproceedings{neurips2025_the_79819,
  title = {{The Structural Complexity of Matrix-Vector Multiplication}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2025},
  url = {https://openreview.net/forum?id=tGLZj8GWx3},
}

@inproceedings{iclr2025_improved_83971,
  title = {{Improved Algorithms for Kernel Matrix-Vector Multiplication Under Sparsity Assumptions}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2025},
  url = {https://iclr.cc/virtual/2025/poster/27853},
}

@inproceedings{iclr2026_dataless_84477,
  title = {{Dataless Weight Disentanglement in Task Arithmetic via Kronecker-Factored Approximate Curvature}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2026},
  url = {https://openreview.net/forum?id=32mrjmaeMP},
}

@inproceedings{iclr2026_group_88087,
  title = {{Group Representational Position Encoding}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2026},
  url = {https://openreview.net/forum?id=itoNJ3gJl2},
}

@inproceedings{neurips2026_the_91721,
  title = {{The Geometry of Linear Program Compression: An Exact Characterization and Learning Algorithm}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2026},
  url = {https://neurips.cc/virtual/2026/poster/154900},
}