[32] Michael B. Cohen, Cameron Musco, and Christopher Musco. Input sparsity time low-rank approximation via ridge leverage score sampling. In ACM-SIAM Symposium on Discrete Algorithms, 2017. https://dl.acm.org/doi/10.5555/3039686.3039801.
[33] C Coleman, C Yeh, S Mussmann, B Mirzasoleiman, P Bailis, P Liang, J Leskovec, and M Zaharia. Selection via proxy: Efficient data selection for deep learning. In International Conference on Learning Representations (ICLR), 2020. https://arxiv.org/abs/1906.11829.
[34] Alexis Conneau, Kartikay Khandelwal, Naman Goyal, Vishrav Chaudhary, Guillaume Wenzek, Francisco Guzmán, Edouard Grave, Myle Ott, Luke Zettlemoyer, and Veselin Stoyanov. Unsupervised cross-lingual representation learning at scale. In Annual Meeting of the Association for Computational Linguistics (ACL), 2019. https://arxiv.org/abs/1911.02116.
[35] R Dennis Cook. Detection of influential observation in linear. Technometrics, 19(1):15-18, 1977.
[36] Achal Dave, Tarasha Khurana, Pavel Tokmakov, Cordelia Schmid, and Deva Ramanan. Tao: A large-scale benchmark for tracking any object. In European Conference on Computer Vision (ECCV), 2020. https://arxiv.org/abs/2005.10356.
[37] Jia Deng, Wei Dong, Richard Socher, Li-Jia Li, Kai Li, and Li Fei-Fei. Imagenet: A large-scale hierarchical image database. In Conference on Computer Vision and Pattern Recognition (CVPR), 2009. https://ieeexplore.ieee.org/abstract/document/5206848.
[38] Karan Desai, Gaurav Kaul, Zubin Aysola, and Justin Johnson. Redcaps: Web-curated image-text data created by the people, for the people, 2021. https://arxiv.org/abs/2111.11431.
[39] Alexey Dosovitskiy, Lucas Beyer, Alexander Kolesnikov, Dirk Weissenborn, Xiaohua Zhai, Thomas Unterthiner, Mostafa Dehghani, Matthias Minderer, Georg Heigold, Sylvain Gelly, Jakob Uszkoreit, and Neil Houlsby. An image is worth 16x16 words: Transformers for image recognition at scale. In International Conference on Learning Representations (ICLR), 2021. https://openreview.net/forum?id=YicbFdNTTy.
[40] Matthijs Douze, Giorgos Tolias, Ed Pizzi, Zoë Papakipos, Lowik Chanussot, Filip Radenovic, Tomas Jenicek, Maxim Maximov, Laura Leal-Taixe, Ismail Elezi, Ondrej Chum, and Cristian Canton-Ferrer. The 2021 image similarity dataset and challenge, 2021. https://arxiv.org/abs/2106.09672.
[41] Kawin Ethayarajh, Yejin Choi, and Swabha Swayamdipta. Understanding dataset difficulty with v-usable information. In International Conference on Machine Learning (ICML), 2022. https://arxiv.org/abs/2110.08420.
[42] M. Everingham, L. Van Gool, C. K. I. Williams, J. Winn, and A. Zisserman. The PASCAL Visual Object Classes Challenge 2007 (VOC2007) Results, 2007. http://www.pascal-network.org/challenges/VOC/voc2007/workshop/index.html.
[43] Sabri Eyuboglu, Bojan Karlas, Christopher Ré, Ce Zhang, and James Zou. dcbench: a benchmark for data-centric ai systems. In Proceedings of the Sixth Workshop on Data Management for End-To-End Machine Learning, 2022. https://dl.acm.org/doi/abs/10.1145/3533028.3533310.
[44] Alex Fang, Gabriel Ilharco, Mitchell Wortsman, Yuhao Wan, Vaishaal Shankar, Achal Dave, and Ludwig Schmidt. Data determines distributional robustness in contrastive language image pre-training (clip). In International Conference on Machine Learning (ICML), 2022. https://arxiv.org/abs/2205.01397.
[45] Li Fei-Fei, Rob Fergus, and Pietro Perona. Learning generative visual models from few training examples: An incremental Bayesian approach tested on 101 object categories. Conference on Computer Vision and Pattern Recognition (CVPR) Workshop, 2004. https://ieeexplore.ieee.org/document/1384978.
[46] Dan Feldman, Matthew Faulkner, and Andreas Krause. Scalable training of mixture models via coresets. In Advances in Neural Information Processing Systems (NeuIPS), 2011. https://proceedings.neurips.cc/paper_files/paper/2011/file/2b6d65b9a9445c4271ab9076ead5605a-Paper.pdf.