Acceleration

Journal Papers

  • T. Cao, X. Chen, and G. Scutari, “DCatalyst: A Unified Accelerated Framework for Decentralized Optimization,” J. of Machine Learning Research, vol. 27, no. 47, pp. 1–57, 2026.
  • M. Maros and G. Scutari, “Acceleration in Distributed Sparse Regression,” Proc. Conference on Neural Information Processing Systems (NeurIPS 2022), 2022.
  • D. Kovalev, A. Beznosikov, E. Borodich, A. Gasnikov, and G. Scutari, “Optimal Gradient Sliding and Its Application to Optimal Distributed Optimization under Similarity,” Proc. Conference on Neural Information Processing Systems (NeurIPS 2022), 2022 (Spotlight, acceptance rate 4%).
  • Y. Tian, G. Scutari, T. Cao, and A. Gasnikov, “Acceleration in Distributed Optimization under Similarity,” Proc. International Conference on Artificial Intelligence and Statistics (AISTATS 2022), 2022.
  • J. Xu, Y. Tian, Y. Sun, and G. Scutari, “Accelerated Primal-Dual Algorithms for Distributed Smooth Convex Optimization over Networks,” Proc. International Conference on Artificial Intelligence and Statistics (AISTATS 2020), 2020.

Refereed Conference Papers

  • A. Agafonov, P. Dvurechensky, G. Scutari, A. Gasnikov, D. Kamzolov, A. Lukashevich, and A. Daneshmand, “An Accelerated Second-Order Method for Distributed Stochastic Optimization,” Proc. of the 60th IEEE Conference on Decision and Control (CDC), 14–17 Dec. 2021, Austin, TX, USA.