Publications

Google Scholar

Preprints

• Wasserstein Distributionally Robust Nash Equilibrium Seeking with Heterogeneous Data: A Lagrangian Approach

  • Z. Wang, G. Pantazis, S. Grammatico, M. M. Zavlanos, and K. H. Johansson

• Exact Variance of Random Return in Distributional LQR and Its Application to Mean-Variance Optimal Control

  • R. Teng, Z. Wang, Y. Gao

• Distributionally Robust Federated Learning with Outlier Resilience

  • Z. Wang, X. Yi, X. Konti, M. M. Zavlanos, and K. H. Johansson

• Federated Flow Matching

  • Z. Wang, A. Dong, M. Selim, M. M. Zavlanos, and K. H. Johansson

• Group Distributionally Robust Machine Learning under Group Level Distributional Uncertainty

  • X. Konti, Y. Shen, Z. Wang, K. H. Johansson, M. J. Pencina, N. J. Economou-Zavlanos, and M. M. Zavlanos

• Source-Guided Flow Matching

  • Z. Wang, A. Harting, M. Barreau, M. M. Zavlanos, and K. H. Johansson

• Risk-averse learning with delayed feedback

  • S. Wang, Z. Wang, K. H. Johansson, and S. Hirche

• First- and Zeroth-Order Learning in Asynchronous Games

  • Z. Wang, X. Yi, M. M. Zavlanos, and K. H. Johansson

• Projected Gradient Descent for Constrained Decision-Dependent Optimization

  • Z. Wang, C. Liu, T. Parisini, M. M. Zavlanos, and K. H. Johansson

Machine Learning Conference Papers

• Risk-Averse Certification of Bayesian Neural Networks

  • X. Zhang, Z. Wang, Y. Gao, L. Romao, A. Abate, and M. Kwiatkowska

  • The Symposium on Software Engineering: Theories, Tools, and Applications (SETTA), 2025.

• Outlier-Robust Distributionally Robust Optimization via Unbalanced Optimal Transport

  • Z. Wang, Y. Shen, M. M. Zavlanos, and K. H. Johansson

  • NeurIPS-24. Annual Conference on Neural Information Processing Systems, 2024.

• Policy Evaluation in Distributional LQR

  • Z. Wang, Y. Gao, S. Wang, M. M. Zavlanos, A. Abate, and K. H. Johansson

  • L4DC-23. Learning for Dynamics and Control, 2023. （Oral Presentation)

• Risk-Averse No-Regret Learning in Online Convex Games

  • Z. Wang, Y. Shen, and M. M. Zavlanos

  • ICML-22. International Conference on Machine Learning, 2022.

Journal Papers

• Risk-averse Learning with Non-Stationary Distributions

  • S. Wang, Z. Wang, X. Yi, M. M. Zavlanos, K. H. Johansson, S. Hirche

  • Automatica, accepted.

• Policy Evaluation in Distributional LQR (Extended Version)

  • Z. Wang, Y. Gao, S. Wang, M. M. Zavlanos, A. Abate, and K. H. Johansson

  • IEEE Transactions on Automatic Control, 2025.

• Asymmetric Feedback Learning in Online Convex Games

  • Z. Wang, X. Yi, Y. Shen, M. M. Zavlanos, and K. H. Johansson

  • IEEE Transactions on Automatic Control, accepted.

• Constrained Optimization with Decision-Dependent Distributions

  • Z. Wang, C. Liu, T. Parisini, M. M. Zavlanos, and K. H. Johansson

  • IEEE Transactions on Automatic Control, 2025.

• Distributed Dynamic Event-Triggered Communication and Control for Multi-Agent Consensus: A Hybrid System Approach

  • Z. Wang, Y. Gao, Y. Liu, S. Wang, and L. Wu

  • Information Science. 618: 191-208. 2022.

Control Conference Papers

• Online Learning of Nash Equilibria in Risk-Averse Games

  • Z. Wang, Y. Shen, M. M. Zavlanos, and K. H. Johansson

  • ACC-24. American Control Conference, 2024.

• Convergence Analysis of the Best Response Algorithm for Time-Varying Games

  • Z. Wang, Y. Shen, M. M. Zavlanos, and K. H. Johansson

  • CDC-23. IEEE Conference on Decision and Control, 2023.

• A Zeroth-Order Momentum Method for Risk-Averse Online Convex Games

  • Z. Wang, Y. Shen, Z. I. Bell, S. Nivison, M. M. Zavlanos, and K. H. Johansson

  • CDC-22. IEEE Conference on Decision and Control, 2022.