SlowGuess/NeurIPS_2023_OCR huggingface dataset:
[25] Rishi Saket. Algorithms and hardness for learning linear thresholds from label proportions. In Proc. NeurIPS'22, 2022.
[26] C. Scott and J. Zhang. Learning from label proportions: A mutual contamination framework. In Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, NeurIPS 2020, 2020.
[27] Ohad Shamir and Tong Zhang. Stochastic gradient descent for non-smooth optimization: Convergence results and optimal averaging schemes. In Proceedings of the 30th International Conference on Machine Learning, ICML 2013, volume 28 of JMLR Workshop and Conference Proceedings, pages 71-79. JMLR.org, 2013.
[28] Y. Shi, L. Cui, Z. Chen, and Z. Qi. Learning from label proportions with pinball loss. International Journal of Machine Learning and Cybernetics, 10(1):187-205, 2019.
[29] Marco Stolpe and Katharina Morik. Learning from label proportions by optimizing cluster model selection. In Machine Learning and Knowledge Discovery in Databases, pages 349-364. Springer Berlin Heidelberg, 2011.
[30] T. Sun, D. Sheldon, and B. O'Connor. A probabilistic approach for learning with label proportions applied to the us presidential election. In IEEE International Conference on Data Mining (ICDM), pages 445-454, 2017.
[31] Kuen-Han Tsai and Hsuan-Tien Lin. Learning from label proportions with consistency regularization. In Asian Conference on Machine Learning, 2020.
[32] F. Yu, D. Liu, S. Kumar, T Jebara, and S.F. Chang.  $\alpha$ -svm for learning with label proportions. In ICML, Proceedings of the 30th International Conference on Machine Learning, pages 504-512, 2013.
[33] Felix X. Yu, Krzysztof Choromanski, Sanjiv Kumar, Tony Jebara, and Shih-Fu Chang. On learning from label proportions, 2015.
[34] J. Zhang, Y. Wang, and C. Scott. Learning from label proportions by learning with label noise. In Proc. NeurIPS'22, 2022.