Publications

(2024/08) Tianyu Chen, Yiheng Lin, Nicolas Christianson, Zahaib Akhtar, Sharath Dharmaji, Mohammad Hajiesmaili, Adam Wierman, and Ramesh K. Sitaraman, “SODA: An Adaptive Bitrate Controller for Consistent High-Quality Video Streaming.” Proceedings of the ACM SIGCOMM 2024 Conference (2024).

(2024/05) Ruiyang Jin, Zaiwei Chen, Yiheng Lin, Jie Song, and Adam Wierman, “Approximate Global Convergence of Independent Learning in Multi-Agent Systems.” Under submission (2024).

(2024/04) Yiheng Lin, James A. Preiss, Fengze Xie, Emile Anand, Soon-Jo Chung, Yisong Yue, and Adam Wierman, “Online Policy Optimization in Unknown Nonlinear Systems.” Proceedings of Thirty Seventh Conference on Learning Theory, PMLR (2024).

(2024/03) Lauren Conger, Yiheng Lin, Adam Wierman, and Eric Mazumdar, “Characterizing Controllability and Observability for Systems with Locality, Communication, and Actuation Constraints.” To appear at the 63rd IEEE Conference on Decision and Control (2024).

(2023/07) Tongxin Li, Yiheng Lin, Shaolei Ren, and Adam Wierman, “Beyond Black-Box Advice: Learning-Augmented Algorithms for MDPs with Q-Value Predictions.” Advances in Neural Information Processing Systems 36 (2023).

(2023/07) Zhaoyi Zhou, Zaiwei Chen, Yiheng Lin, and Adam Wierman, “Convergence Rates for Localized Actor-Critic in Networked Markov Potential Games.” The 39th Conference on Uncertainty in Artificial Intelligience (2023).

(2023/01) Yingying Li, James A. Preiss, Na Li, Yiheng Lin, Adam Wierman, and Jeff Shamma, “Online Switching Control with Stability and Regret Guarantees.” The fifth Annual Learning for Dynamics \& Control Conference (2023).

(2022/11) Yizhou Zhang, Guannan Qu, Pan Xu, Yiheng Lin, Zaiwei Chen, and Adam Wierman, “Global Convergence of Localized Policy Iteration in Networked Multi-Agent Reinforcement Learning.” ACM SIGMETRICS (2023).

(2022/10) Yiheng Lin, James A. Preiss, Emile Anand, Yingying Li, Yisong Yue, and Adam Wierman, “Online Adaptive Policy Selection in Time-Varying Systems: No-Regret via Contractive Perturbations” Advances in Neural Information Processing Systems 36 (2023).

(2022/10) Yiheng Lin, Hu Yang, Guannan Qu, and Adam Wierman, “Bounded-Regret MPC via Perturbation Analysis: Prediction Error, Constraints, and Nonlinearity” Advances in Neural Information Processing Systems 35 (2022).

(2022/07) Yiheng Lin^*, Judy Gan^*, Guannan Qu, Yash Kanoria, and Adam Wierman, “Decentralized Online Convex Optimization in Networked Systems” In International Conference on Machine Learning (2022).

(2022/06) Tongxin Li, Ruixiao Yang, Guannan Qu, Yiheng Lin, Steven Low, and Adam Wierman, “Equipping Black-Box Policies with Model-Based Advice for Stable Nonlinear Control” IEEE Open Journal of Control System (2022).

(2022/04) Sungho Shin, Yiheng Lin, Guannan Qu, Adam Wierman, and Mihai Anitescu, “Near-Optimal Distributed Linear-Quadratic Regulator for Networked Systems” SIAM Journal on Control and Optimization (2022).

(2021/11) Weici Pan, Guanya Shi, Yiheng Lin, and Adam Wierman, “Online optimization with feedback delay and nonlinear switching cost” Proceedings of the ACM on Measurement and Analysis of Computing Systems, 6(1), pp.1-34 (2022).

(2021/06) Yiheng Lin^*, Yang Hu^*, Haoyuan Sun^*, Guanya Shi^*, Guannan Qu^*, and A. Wierman, “Perturbation-based Regret Analysis of Predictive Control in Linear Time Varying Systems” Advances in Neural Information Processing Systems 34 (2021). (Spotlight, <3%).

(2020/06) Yiheng Lin, Guannan Qu, Longbo Huang, and Adam Wierman, “Multi-Agent Reinforcement Learning in Stochastic Networked Systems” Advances in Neural Information Processing Systems 34 (2021).

(2020/06) Guannan Qu, Yiheng Lin, Adam Wierman, and Na Li, “Scalable Multi-Agent Reinforcement Learning for Networked Systems with Average Reward” Advances in Neural Information Processing Systems 33 (2020).

(2020/02) Guanya Shi^*, Yiheng Lin^*, Soon-Jo Chung, Yisong Yue, and Adam Wierman, “Online Optimization with Memory and Competitive Control” Advances in Neural Information Processing Systems 33 (2020).

(2019/11) Yiheng Lin, Gautam Goel, and Adam Wierman, “Online Optimization with Predictions and Non-convex Losses” Proceedings of the ACM on Measurement and Analysis of Computing Systems, 4(1), pp.1-32 (2020).

(2019/05) Gautam Goel^*, Yiheng Lin^*, Haoyuan Sun^*, and Adam Wierman, “Beyond Online Balanced Descent: An Optimal Algorithm for Smoothed Online Optimization” Advances in Neural Information Processing Systems 32 (2019). (Spotlight, <3%).