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Publications

My Google Scholar and Research Gate Page.

Under Review

  1. Y. Wan, X. Wang, S. Yang, W. Xu, Decentralized Natural Policy Gradient with Directed Communication Networks, 2024.

  2. S. Zhang, S. Yang, W. Xu, J. Cao, Decentralized Local SGDA for Nonconvex-PL Minimax Optimization, 2024.

  3. Z. Chang, Z. Zhang, S. Yang, J. Cao, A Flexible Framework for Decentralized Composite Optimization with Compressed Communication, 2024.

  4. W. He, Z. Yang, S. Yang, Differentially Private Distributed Optimization Over Time-Varying Unbalanced Networks With Linear Convergence Rates, 2024.

Selected Journal Papers

  1. L. Wang, L. Guo, S. Yang, X. Shi, Differentially private decentralized optimization with relay communication, IEEE Transactions on Information Forensics and Security, 2024, accepted.

  2. W. Xu, Z. Wang, S. Yang, W. Yu, Data-driven adaptive consensus for general linear multi-agent systems: A fully distributed protocol, Automatica, 2024, accepted. (Regular Paper)

  3. S. Yang, Y. Shen, J. Cao, and T. Huang, Distributed heavy-ball over time-varying digraphs with Barzilai-Borwein step sizes, IEEE Transactions on Emerging Topics in Computational Intelligence, 2024, in press.

  4. S. Yang, W. Xu, W. He, and J. Cao, Distributed generalized Nash equilibrium seeking: Event-triggered coding-decoding-based secure communication, Science China: Information Sciences, vol. 67, no. 7, p. 172205, 2024.

  5. Z. Zhang, K. Che, S. Yang, and W. Xu, Communication-efficient distributed cubic Newton with compressed lazy Hessian, Neural Networks, vol. 174, p. 106212, 2024.

  6. Z. Zhang, S. Yang, W. Xu, and K. Di, Privacy-preserving distributed ADMM with event-triggered communication, IEEE Transactions on Neural Networks and Learning Systems, vol. 35, no. 2, pp. 2835-2847, 2024.

  7. X. Wang, S. Yang, Z. Guo, Q. Ge, S. Wen, T. Huang, A distributed k-winners-take-all model with binary consensus protocols, IEEE Transactions on Cybernetics, vol. 54, no. 5, pp. 3327-3337, 2024.

  8. L. Guo, X. Shi, S. Yang, J. Cao, DISA: A dual inexact splitting algorithm for distributed convex composite optimization, IEEE Transactions on Automatic Control, vol. 69, no. 5, pp. 2995–3010, 2024. (Full Paper)

  9. Z. Zhang, S. Yang, and W. Xu, Decentralized ADMM with compressed and event-triggered communication, Neural Networks, vol. 165, pp. 472–482, 2023.

  10. Y. Ye, H. Wang, T. Cui, X. Yang, S. Yang, M. Zhang, Identifying generalizable equilibrium pricing strategies for charging service providers in coupled power and transportation networks, Advances in Applied Energy, vol. 12, pp. 100151, 2023.

  11. X. Rao, W. Xu, S. Yang, and W. Yu, A distributed coding-decoding-based Nash equilibrium seeking algorithm over directed communication network, Science China: Technological Sciences, vol. 66, 2023.

  12. X. Wang, S. Yang, Z. Guo, and T. Huang, A second-order projected primal-dual dynamical system for distributed optimization and learning, IEEE Transactions on Neural Networks and Learning Systems, vol. 34, no. 9, pp. 6568-6577, 2023.

  13. X. Wang, S. Yang, Z. Guo, S. Wen, and T. Huang, A distributed network system for nonsmooth coupled-constrained optimization, IEEE Transactions on Network Science and Engineering, vol. 9, no. 5, pp. 3691-3700, 2022.

  14. X. Wang, S. Yang, Z. Guo, M. Lian, and T. Huang, A distributed dynamical system for optimal resource allocation over state-dependent networks, IEEE Transactions on Network Science and Engineering, vol. 9, no. 4, pp. 2940–2951, 2022.

  15. X. Wang, S. Yang, Z. Guo, and T. Huang, Distributed k-winners-take-all via multiple neural networks with inertia, Neural Networks, vol. 151, pp. 385–397, 2022.

  16. K. Di, Y. Zhou, F. Yan, J. Jiang, S. Yang, and Y. Jiang, A foraging strategy with risk response for individual robots in adversarial environments, ACM Transactions on Intelligent Systems and Technology, vol. 13, no. 5, Article 83:1-29, 2022.

  17. K. Di, Y. Zhou, J. Jiang, F. Yan, S. Yang, and Y. Jiang, Risk-aware collection strategies for multirobot foraging in hazardous environments, ACM Transactions on Autonomous and Adaptive Systems, vol. 16, no. 3–4, Article 8:1–38, 2022.

  18. W. Xu, S. Yang, and J. Cao, Fully distributed self-triggered control for second-order consensus of multiagent systems, IEEE Transactions on Systems, Man, and Cybernetics: Systems, vol. 51, no. 6, pp. 3541–3551, 2021.

  19. S. Yang, J. Wang, and Q. Liu, Consensus of heterogeneous nonlinear multiagent systems with duplex control laws, IEEE Transactions on Automatic Control, vol. 64, no. 12, pp. 5140–5147, 2019.

  20. S. Yang, J. Wang, and Q. Liu, Cooperative-competitive multiagent systems for distributed minimax optimization subject to bounded constraints, IEEE Transactions on Automatic Control, vol. 64, no. 4, pp. 1358–1372, 2019. (Full Paper)

  21. S. Yang, Q. Liu, and J. Wang, A collaborative neurodynamic approach to multiple-objective distributed optimization, IEEE Transactions on Neural Networks and Learning Systems, vol. 29, no. 4, pp. 981–992, 2018.

  22. S. Yang, Z. Guo, and J. Wang, Global synchronization of multiple recurrent neural networks with time delays via impulsive interactions, IEEE Transactions on Neural Networks and Learning Systems, vol. 28, no. 7, pp. 1657–1667, 2017.

  23. Q. Liu, S. Yang, and Y. Hong, Constrained consensus algorithms with fixed step size for distributed convex optimization over multiagent networks, IEEE Transactions on Automatic Control, vol. 62, no. 8, pp. 4259–4265, 2017.

  24. S. Yang, Q. Liu, and J. Wang, A multi-agent system with a proportional-integral protocol for distributed constrained optimization, IEEE Transactions on Automatic Control, vol. 62, no. 7, pp. 3461–3467, 2017.

  25. Q. Liu, S. Yang, and J. Wang, A collective neurodynamic approach to distributed constrained optimization, IEEE Transactions on Neural Networks and Learning Systems, vol. 28, no. 8, pp. 1747–1758, 2017.

  26. S. Yang, Q. Liu, and J. Wang, Distributed optimization based on a multiagent system in the presence of communication delays, IEEE Transactions on Systems, Man, and Cybernetics: Systems, vol. 47, no. 5, pp. 717–728, 2017.

  27. S. Yang, Z. Guo, and J. Wang, Robust synchronization of multiple memristive neural networks with uncertain parameters via nonlinear coupling, IEEE Transactions on Systems, Man, and Cybernetics: Systems, vol. 45, no. 7, pp. 1077–1086, 2015.

  28. Z. Guo, S. Yang, and J. Wang, Global exponential synchronization of multiple memristive neural networks with time delay via nonlinear coupling, IEEE Transactions on Neural Networks and Learning Systems, vol. 26, no. 6, pp. 1300–1311, 2015.

Selected Conference Papers

  1. L. Yang, Z. Zhang, K. Che, S. Yang, and S. Wang, Communication-efficient distributed minimax optimization via Markov compression, in Proceeding of the 30th International Conference on Neural Information Processing (ICONIP), 2024, pp. 540-551. (Best Student Paper Award Finalist)

  2. K. Che, S. Yang, and Z. Guo, Decentralized gradient tracking with fixed-time local updates, in Proceedings of International Conference on Information Science and Technology (ICIST), 2023, pp. 601–607.

  3. K. Che, S. Yang, A snapshot gradient tracking for distributed optimization over digraphs, in Proceedings of the 2nd CAAI International Conference on Artificial Intelligence (CICAI), 2022, pp. 348-360.

  4. Y. Wan, Y. Qu, Z. Zhao, S. Yang, Asynchronous Byzantine-resilient distributed optimization with momentum, in Proceedings of the 41th Chinese Control Conference (CCC), 2022, pp. 2022-2027.

  5. W. Xu, S. Yang, S. Grammatico, and W. He, An event-triggered distributed generalized Nash equilibrium seeking algorithm, in Proceedings of the 60th IEEE Conference on Decision and Control (CDC), 2021, pp. 4301–4306.

  6. Y. Shen and S. Yang, A heavy-ball distributed optimization algorithm over digraphs with row-stochastic matrices, in Proceedings of the 39th Chinese Control Conference (CCC), 2020, pp. 4977–4982.

  7. C. Song, C. Wu, Z. Lv, F. Zhang, J. Li, and S. Yang, Distributed heavy-ball Nash equilibrium seeking algorithm in aggregative games, in Proceedings of the 39th Chinese Control Conference (CCC), 2020, pp. 5019–5024.

  8. S. Yang, W. Xu, and Z. Guo, Distributed convergence to saddle-points over general directed multi-agent networks, in Proceedings of the 14th International Conference on Control and Automation (ICCA), 2018, pp. 538–543.