Full Publication List →

• K. Ding and G. Scutari, “From Mixing to Tearing: Graph Decomposition in Decentralized Optimization via Message Passing,” preprint, arXiv:2610.03709, 2026.
• X. Chen, I. Kuruzov, and G. Scutari, “A Line-search-free Method for Adaptive Decentralized Optimization,” preprint, arXiv:2605.00711, 2026.
• X. Chen, I. Kuruzov, and G. Scutari, “Adaptive Decentralized Composite Optimization via Three-Operator Splitting,” preprint, arXiv:2602.17545, 2026.

• I. Kuruzov, G. Scutari, and A. Gasnikov, “Achieving Linear Convergence with Parameter-Free Algorithms in Decentralized Optimization,” Proc. Conference on Neural Information Processing Systems (NeurIPS), 2024.
• M. Maros, G. Scutari, Y. Sun, G. Cheng, “Decentralized Sparse Linear Regression via Gradient-Tracking,” J. of Machine Learning Research, vol. 26, no. 197, pp. 1–56, 2025. The order of the first three authors is alphabetical.
• Y. Ji, G. Scutari, Y. Sun, and H. Honnappa, “Distributed Sparse Regression via Penalization,” J. of Machine Learning Research, vol. 24, no. 272, pp. 1–62, 2023.
• M. Maros and G. Scutari, “Decentralized Matrix Sensing: Statistical Guarantees and Fast Convergence,” Proc. Conference on Neural Information Processing Systems (NeurIPS), 2023.
• Y. Ji, G. Scutari, Y. Sun, and H. Honnappa, “Distributed (ATC) Gradient Descent for High-Dimension Sparse Regression,” IEEE Trans. on Information Theory, vol. 69, no. 8, pp. 5253–5276, 2023 [DOI].
• N. Michelusi, G. Scutari, and C.-S. Lee, “Finite-Bit Quantization for Distributed Algorithms with Linear Convergence,” IEEE Trans. on Information Theory, vol. 68, no. 11, pp. 7254–7280, Nov. 2022 [DOI].
• K. Slavakis, G. Shetty, L. Cannelli, G. Scutari, U. Nakarmi, and L. Ying, “Kernel Regression Imputation in Manifolds via Bi-linear Modeling: The Dynamic MRI case,” IEEE Trans. on Computational Imaging, vol. 8, pp. 133–147, 2022 [DOI].
• Y. Sun, G. Scutari, and A. Daneshmand, “Distributed Optimization Based on Gradient-Tracking Revisited: Enhancing Convergence Rate via Surrogation,” SIAM J. on Optimization, vol. 32, no. 2, pp. 354–385, 2022 [DOI].
• M. Maros and G. Scutari, “Acceleration in Distributed Sparse Regression,” Proc. Conference on Neural Information Processing Systems (NeurIPS 2022), 2022.
• M. Maros and G. Scutari, “DGD²: A Linearly Convergent Distributed Algorithm for High-dimensional Statistical Recovery,” Proc. Conference on Neural Information Processing Systems (NeurIPS 2022), 2022.
• A. Daneshmand, G. Scutari, P. Dvurechensky, A. Gasnikov, “Newton Method over Networks is Fast up to the Statistical Precision,” Proc. International Conference on Machine Learning (ICML 2021), PMLR vol. 139, pp. 2398–2409, 2021.
• L. Cannelli, F. Facchinei, G. Scutari, and V. Kungurtsev, “Asynchronous Optimization over Graphs: Linear Convergence under Error Bound Conditions,” IEEE Trans. on Automatic Control, vol. 66, no. 10, pp. 4604–4619, Oct. 2021 [DOI].
• Y. Tian, Y. Sun, and G. Scutari, “Achieving Linear Convergence in Distributed Asynchronous Multiagent Optimization,” IEEE Trans. on Automatic Control, vol. 65, no. 12, pp. 5264–5279, 2020 [DOI].

• X. Chen, I. Kuruzov, and G. Scutari, “A Line-search-free Method for Adaptive Decentralized Optimization,” Proc. of the 60th Asilomar Conference on Signals, Systems, and Computers, Oct. 25–28, 2026, Pacific Grove, CA, USA.
  Best Student Paper Award (1st Place).
• M. Maros and G. Scutari, “A Unified View of Decentralized Algorithms for Sparse Linear Regression,” Proc. of the 2023 IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP23), Dec. 10–13, 2023, Los Suenos, Costa Rica.
• Y. Tian, Y. Sun, G. Scutari, and B. Du, “ASY-SONATA: Achieving Linear Convergence in Distributed Asynchronous Multiagent Optimization,” Proc. of the 56th Allerton Conference on Communication, Control, and Computing (Allerton 2018), Oct. 2–5, 2018, Monticello, IL, USA.