[1] Collective communications library with various primitives for multi-machine training, 2023. URL https://github.com/facebookincubator/gloo.
[2] Ryan Alweiss, Yang P Liu, and Mehtaab Sawhney. Discrepancy minimization via a self-balancing walk. In Proceedings of the 53rd Annual ACM SIGACT Symposium on Theory of Computing, pages 14-20, 2021.
[3] Alessandro Barp, Carl-Johann Simon-Gabriel, Mark Girolami, and Lester Mackey. Targeted separation and convergence with kernel discrepancies. arXiv preprint arXiv:2209.12835, 2022.
[4] Dimitri P. Bertsekas. Incremental Gradient, Subgradient, and Proximal Methods for Convex Optimization: A Survey. In *Optimization for Machine Learning*. The MIT Press, 2011.
[5] Léon Bottou. Stochastic gradient descent tricks. In Neural networks: Tricks of the trade, pages 421-436. Springer, 2012.
[6] Jaeyoung Cha, Jaewook Lee, and Chulhee Yun. Tighter Lower Bounds for Shuffling SGD: Random Permutations and Beyond. In Proceedings of the 40th International Conference on Machine Learning, ICML'23. JMLR.org, 2023.
[7] A. Feder Cooper, Katherine Lee, Solon Barocas, Christopher De Sa, Siddhartha Sen, and Baobao Zhang. Is My Prediction Arbitrary? Measuring Self-Consistency in Fair Classification. arXiv preprint arXiv:2301.11562, 2023.
[8] Christopher De Sa. Random Reshuffling is Not Always Better. In Advances in Neural Information Processing Systems, 2020.
[9] Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding. In Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pages 4171–4186, 2019.
[10] Raaz Dwivedi and Lester Mackey. Kernel thinning. arXiv preprint arXiv:2105.05842, 2021.
[11] Raaz Dwivedi and Lester Mackey. Generalized Kernel Thinning. In Tenth International Conference on Learning Representations, 2022.
[12] Alex Graves, Marc G Bellemare, Jacob Menick, Remi Munos, and Koray Kavukcuoglu. Automated curriculum learning for neural networks. In international conference on machine learning, pages 1311-1320. PMLR, 2017.
[13] Mert Gürbüzbalaban, Asuman E. Ozdaglar, and Pablo A. Parrilo. Convergence Rate of Incremental Gradient and Incremental Newton Methods. SIAM Journal on Optimization, 29 (4):2542-2565, 2019.
[14] Mert Gurbuzbalaban, Asu Ozdaglar, and Pablo A Parrilo. Why random reshuffling beats stochastic gradient descent. Mathematical Programming, 186(1):49-84, 2021.
[15] Jeff Z. HaoChen and Suvrit Sra. Random Shuffling Beats SGD after Finite Epochs. In Proceedings of the International Conference on Machine Learning, volume 97, pages 2624-2633, 2019.
[16] Nick Harvey and Samira Samadi. Near-Optimal Herding. In Proceedings of The 27th Conference on Learning Theory, volume 35, pages 1165–1182, 2014.
[17] Sepp Hochreiter and Jürgen Schmidhuber. Long short-term memory. Neural computation, 9 (8):1735-1780, 1997.
[18] Kun Huang, Xiao Li, Andre Milzarek, Shi Pu, and Junwen Qiu. Distributed Random Reshuffling over Networks. arXiv preprint arXiv:2112.15287, 2021.
[19] Hakan Inan, Khashayar Khosravi, and Richard Socher. Tying word vectors and word classifiers: A loss framework for language modeling. arXiv preprint arXiv:1611.01462, 2016.