SlowGuess/NeurIPS_2023_OCR huggingface dataset:
[1] Artem Agafonov, Pavel Dvurechensky, Gesualdo Scutari, Alexander Gasnikov, Dmitry Kamzolov, Aleksandr Lukashevich, and Amir Daneshmand. An accelerated second-order method for distributed stochastic optimization. In 2021 60th IEEE Conference on Decision and Control (CDC), pages 2407-2413. IEEE, 2021.
[2] Alekh Agarwal and Leon Bottou. A lower bound for the optimization of finite sums. In ICML, 2015.
[3] Masoud Ahookhosh and Yurii Nesterov. High-order methods beyond the classical complexity bounds, ii: inexact high-order proximal-point methods with segment search. arXiv preprint arXiv:2109.12303, 2021.
[4] Mohammad S Alkousa, Alexander Vladimirovich Gasnikov, Darina Mikhailovna Dvinskikh, Dmitry A Kovalev, and Fedor Sergeevich Stonyakin. Accelerated methods for saddle-point problem. Computational Mathematics and Mathematical Physics, 60:1787-1809, 2020.
[5] Zeyuan Allen-Zhu. Katyusha: the first direct acceleration of stochastic gradient methods. The Journal of Machine Learning Research, 18(1):8194-8244, 2017.
[6] Zeyuan Allen-Zhu. Katyusha x: Practical momentum method for stochastic sum-of-nonconvex optimization. arXiv preprint arXiv:1802.03866, 2018.
[7] Yossi Arjevani and Ohad Shamir. Communication complexity of distributed convex learning and optimization. Advances in neural information processing systems, 28, 2015.
[8] Syreen Banabilah, Moayad Aloqaily, Eitaa Alsayed, Nida Malik, and Yaser Jararweh. Federated learning review: Fundamentals, enabling technologies, and future applications. Information processing & management, 59(6):103061, 2022.
[9] Ron Bekkerman, Mikhail Bilenko, and John Langford. Scaling up machine learning: Parallel and distributed approaches. Cambridge University Press, 2011.
[10] Aleksandr Beznosikov and Alexander Gasnikov. Compression and data similarity: Combination of two techniques for communication-efficient solving of distributed variational inequalities. In International Conference on Optimization and Applications, pages 151-162. Springer, 2022.
[11] Aleksandr Beznosikov and Alexander Gasnikov. Similarity, compression and local steps: Three pillars of efficient communications for distributed variational inequalities. arXiv preprint arXiv:2302.07615, 2023.
[12] Aleksandr Beznosikov, Eduard Gorbunov, and Alexander Gasnikov. Derivative-free method for composite optimization with applications to decentralized distributed optimization. IFAC-PapersOnLine, 53(2):4038-4043, 2020.
[13] Aleksandr Beznosikov, Gesualdo Scutari, Alexander Rogozin, and Alexander Gasnikov. Distributed saddle-point problems under data similarity. Advances in Neural Information Processing Systems, 34:8172-8184, 2021.
[14] Yair Carmon, John C Duchi, Oliver Hinder, and Aaron Sidford. Accelerated methods for nonconvex optimization. SIAM Journal on Optimization, 28(2):1751-1772, 2018.
[15] Yair Carmon, John C Duchi, Oliver Hinder, and Aaron Sidford. Lower bounds for finding stationary points ii: first-order methods. Mathematical Programming, 185(1-2):315-355, 2021.
[51] Ohad Shamir, Nati Srebro, and Tong Zhang. Communication-efficient distributed optimization using an approximate newton-type method. In International conference on machine learning, pages 1000-1008. PMLR, 2014.
[52] Danny C Sorensen. Numerical methods for large eigenvalue problems. Acta Numerica, 11: 519-584, 2002.
[53] Ivan Stepanov, Artyom Voronov, Aleksandr Beznosikov, and Alexander Gasnikov. One-point gradient-free methods for composite optimization with applications to distributed optimization. arXiv preprint arXiv:2107.05951, 2021.
[54] Ying Sun, Gesualdo Scutari, and Amir Daneshmand. Distributed optimization based on gradient tracking revisited: Enhancing convergence rate via surrogation. SIAM Journal on Optimization, 32(2):354-385, 2022.
[55] Ye Tian, Gesualdo Scutari, Tianyu Cao, and Alexander Gasnikov. Acceleration in distributed optimization under similarity. In International Conference on Artificial Intelligence and Statistics, pages 5721-5756. PMLR, 2022.
[56] Blake E Woodworth and Nati Srebro. Tight complexity bounds for optimizing composite objectives. Advances in neural information processing systems, 29, 2016.
[57] Lin Xiao and Tong Zhang. A proximal stochastic gradient method with progressive variance reduction. SIAM Journal on Optimization, 24(4):2057-2075, 2014.
[58] Xiao-Tong Yuan and Ping Li. On convergence of distributed approximate newton methods: Globalization, sharper bounds and beyond. The Journal of Machine Learning Research, 21(1): 8502-8552, 2020.
[59] Junyu Zhang, Mingyi Hong, and Shuzhong Zhang. On lower iteration complexity bounds for the convex concave saddle point problems. Mathematical Programming, 194(1-2):901-935, 2022.
[60] Sai Qian Zhang, Jieyu Lin, and Qi Zhang. A multi-agent reinforcement learning approach for efficient client selection in federated learning. In Proceedings of the AAAI Conference on Artificial Intelligence, volume 36, pages 9091-9099, 2022.
[61] Siqi Zhang, Junchi Yang, Cristóbal Guzmán, Negar Kiyavash, and Niao He. The complexity of nonconvex-strongly-concave minimax optimization. In Uncertainty in Artificial Intelligence, pages 482-492. PMLR, 2021.
[62] Yuchen Zhang and Xiao Lin. Disco: Distributed optimization for self-concordant empirical loss. In International conference on machine learning, pages 362-370. PMLR, 2015.
[63] Dongruo Zhou and Quanquan Gu. Lower bounds for smooth nonconvex finite-sum optimization. In International Conference on Machine Learning, pages 7574-7583. PMLR, 2019.