Двинских Дарина Михайловна

Публикации¹⁵

• Глава книги Kornilov N., Shamir O., Lobanov A., Dvinskikh D., Alexander Gasnikov, Shibaev I., Gorbunov E., Horváth S. Accelerated zeroth-order method for non-smooth stochastic convex optimization problem with infinite variance, in: Advances in Neural Information Processing Systems 36 (NeurIPS 2023). Curran Associates, Inc., 2023.
• Статья Rogozin A., Beznosikov A., Dvinskikh D., Kovalev D., Dvurechensky P., Gasnikov A. Decentralized saddle point problems via non-Euclidean mirror prox // Optimization Methods and Software. 2023. P. 1-26. doi
• Статья Alashqar B., Gasnikov A., Dvinskikh D., Lobanov A. Gradient-free Federated Learning Methods with l1 and l2-randomization for Non-smooth Convex Stochastic Optimization Problems // Computational Mathematics and Mathematical Physics. 2023. P. 1600-1653.
• Статья Sadiev A., Borodich E., Beznosikov A., Dvinskikh D., Chezhegov S., Tappenden R., Takáč M., Alexander Gasnikov. Decentralized personalized federated learning: Lower bounds and optimal algorithm for all personalization modes // EURO Journal on Computational Optimization. 2022. Vol. 10. Article 100041. doi
• Глава книги Dvinskikh D., Tominin V., Tominin I., Gasnikov Alexander. Noisy Zeroth-Order Optimization for Non-smooth Saddle Point Problems, in: Mathematical Optimization Theory and Operations Research, 21st International Conference, MOTOR 2022, Petrozavodsk, Russia, July 2–6, 2022, Proceedings Vol. 13367. Cham: Springer, 2022. doi Ch. 279899. P. 18-33. doi
• Статья Ivanova A., Dvurechensky P., Vorontsova E., Pasechnyuk D., Gasnikov A., Dvinskikh D., Tyurin A. Oracle Complexity Separation in Convex Optimization // Journal of Optimization Theory and Applications. 2022. Vol. 193. No. 1-3. P. 462-490. doi
• Глава книги Gorbunov E., Rogozin A., Beznosikov A., Dvinskikh D., Gasnikov A. Recent Theoretical Advances in Decentralized Distributed Convex Optimization, in: High-Dimensional Optimization and Probability: With a View Towards Data Science. Springer, 2022. Ch. 191. P. 253-325. doi
• Глава книги Dvinskikh D., Tiapkin D. Improved Complexity Bounds in Wasserstein Barycenter Problem, in: Proceedings of Machine Learning Research Volume 130: International Conference on Artificial Intelligence and Statistics. , 2021. P. 1738-1746.
• Статья Stonyakin F., Tyurin A., Gasnikov A., Dvurechensky P., Agafonov A., Dvinskikh D., Alkousa M., Pasechnyuk D., Artamonov S., Piskunova V. Inexact model: a framework for optimization and variational inequalities // Optimization Methods and Software. 2021. Vol. 36. No. 6. P. 1155-1201. doi
• Статья Dvinskikh D. Stochastic approximation versus sample average approximation for Wasserstein barycenters // Optimization Methods and Software. 2021 doi
• Препринт Ivanova A., Gasnikov A., Dvurechensky P., Тюрин А. И., Воронцова Е., Пасечнюк Д., Dvinskikh D. Oracle Complexity Separation in Convex Optimization / Working papers by Cornell University.. Series - "Optimization and Control". 2020. (в печати)
• Глава книги Stonyakin F., Dvinskikh D., Dvurechensky P., Kroshnin A., Kuznetsova O., Agafonov A., Gasnikov A., Tyurin A., Uribe C., Pasechnyuk D., Artamonov S. Gradient Methods for Problems with Inexact Model of the Objective, in: Mathematical Optimization Theory and Operations Research, 18th International Conference, MOTOR 2019 Ekaterinburg, Russia, July 8–12, 2019 / Ed. by М. Ю. Хачай, Ю. А. Кочетов, P. M. Pardalos. Vol. 11548. Springer, 2019. P. 97-114. doi
• Глава книги Dvinskikh D., Gorbunov E., Gasnikov A., Dvurechensky P., Uribe C. On Primal and Dual Approaches for Distributed Stochastic Convex Optimization over Networks, in: 2019 IEEE 58th Conference on Decision and Control (CDC). IEEE, 2019. doi P. 7435-7440. doi
• Глава книги Kroshnin A., Tupitsa Nazarii, Dvinskikh D., Dvurechensky P., Gasnikov Alexander, Uribe C. A. On the Complexity of Approximating Wasserstein Barycenters, in: Proceedings of Machine Learning Research Vol. 97: International Conference on Machine Learning, 9-15 June 2019, Long Beach, California, USA. PMLR, 2019. P. 3530-3540.
• Глава книги Dvurechensky P., Dvinskikh D., Gasnikov A., Uribe C., Nedic A. Decentralize and randomize: Faster algorithm for Wasserstein barycenters, in: Advances in Neural Information Processing Systems 31 (NeurIPS 2018). Neural Information Processing Systems Foundation, 2018. P. 10760-10770.