Wierman, Adam

Monograph from CaltechAUTHORS

• Christianson, Nicolas and Handina, Tinashe, et el. (2023) Chasing Convex Bodies and Functions with Black-Box Advice
• Sun, Bo and Yang, Lin, et el. (2023) The Online Knapsack Problem with Departures
• Zhou, Zhaoyi and Chen, Zaiwei, et el. (2023) Convergence Rates for Localized Actor-Critic in Networked Markov Potential Games
• Zhang, Yizhou and Qu, Guannan, et el. (2023) Global Convergence of Localized Policy Iteration in Networked Multi-Agent Reinforcement Learning
• Chen, Zaiwei and Zhang, Kaiqing, et el. (2023) A Finite-Sample Analysis of Payoff-Based Independent Learning in Zero-Sum Stochastic Games
• Lale, Sahin and Shi, Yuanyuan, et el. (2022) KCRL: Krasovskii-Constrained Reinforcement Learning with Guaranteed Stability in Nonlinear Dynamical Systems; 10.48550/arXiv.arXiv.2206.01704
• Hu, Yang and Wierman, Adam, et el. (2022) On the Sample Complexity of Stabilizing LTI Systems on a Single Trajectory; 10.48550/arXiv.arXiv.2202.07187
• Yu, Chenkai and Ma, Hongyao, et el. (2022) Price Cycles in Ridesharing Platforms; 10.48550/arXiv.arXiv.2202.07086
• Rutten, Daan and Christianson, Nicolas, et el. (2022) Online Optimization with Untrusted Predictions; 10.48550/arXiv.2202.03519
• Shi, Yuanyuan and Qu, Guannan, et el. (2022) Stability Constrained Reinforcement Learning for Real-Time Voltage Control; 10.48550/arXiv.2109.14854
• Sun, Bo and Lee, Russell, et el. (2022) Pareto-Optimal Learning-Augmented Algorithms for Online Conversion Problems; 10.48550/arXiv.2109.01556
• Tseng, Shih-Hao and Han, SooJean, et el. (2021) Trading Throughput for Freshness: Freshness-Aware Traffic Engineering and In-Network Freshness Control; 10.48550/arXiv.2106.02156
• Lin, Yiheng and Hu, Yang, et el. (2021) Perturbation-based Regret Analysis of Predictive Control in Linear Time Varying Systems; 10.48550/arXiv.2106.10497
• Zocca, Alessandro and Liang, Chen, et el. (2021) A Spectral Representation of Power Systems with Applications to Adaptive Grid Partitioning and Cascading Failure Localization; 10.48550/arXiv.2105.05234
• Yu, Chenkai and Shi, Guanya, et el. (2020) Competitive Control with Delayed Imperfect Information; 10.48550/arXiv.2010.11637
• Lin, Yiheng and Qu, Guannan, et el. (2020) Distributed Reinforcement Learning in Multi-Agent Networked Systems; 10.48550/arXiv.2006.06555
• Yu, Chenkai and Shi, Guanya, et el. (2020) The Power of Predictions in Online Control; 10.48550/arXiv.2006.07569
• Li, Tongxin and Low, Steven H., et el. (2020) Real-time Flexibility Feedback for Closed-loop Aggregator and System Operator Coordination; 10.48550/arXiv.2006.13814
• Qu, Guannan and Lin, Yiheng, et el. (2020) Scalable Multi-Agent Reinforcement Learning for Networked Systems with Average Reward; 10.48550/arXiv.2006.06626
• Qu, Guannan and Yu, Chenkai, et el. (2020) Combining Model-Based and Model-Free Methods for Nonlinear Control: A Provably Convergent Policy Gradient Approach; 10.48550/arXiv.2006.07476
• Guo, Linqi and Liang, Chen, et el. (2020) Localization & Mitigation of Cascading Failures in Power Systems, Part III: Real-time Mitigation; 10.48550/arXiv.2005.11319
• Su, Yu and Ren, Xiaoqi, et el. (2020) Communication-Aware Scheduling of Precedence-Constrained Tasks on Related Machines; 10.48550/arXiv.2004.14639
• Shi, Guanya and Lin, Yiheng, et el. (2020) Online Optimization with Memory and Competitive Control; 10.48550/arXiv.2002.05318
• Qu, Guannan and Wierman, Adam (2020) Finite-Time Analysis of Asynchronous Stochastic Approximation and Q-Learning; 10.48550/arXiv.2002.00260
• Qu, Guannan and Wierman, Adam, et el. (2020) Scalable Reinforcement Learning of Localized Policies for Multi-Agent Networked Systems; 10.48550/arXiv.1912.02906
• Chen, Niangjun and Goel, Gautam, et el. (2019) Smoothed Online Convex Optimization in High Dimensions via Online Balanced Descent; 10.48550/arXiv.1803.10366
• Chen, Yue and Mei, Shengwei, et el. (2019) Buy or Sell? Energy Sharing of Prosumers on Constrained Networks; 10.48550/arXiv.1906.09891
• Echenique, Federico and Wierman, Adam (2017) Finding a Walrasian equilibrium is easy for a fixed number of agents; 10.7907/wwxc6-hbb24
• Barman, Siddharth and Bhaskar, Umang, et el. (2016) The Complexity of Nash Equilibria as Revealed by Data; 10.48550/arXiv.1311.2655
• Bose, Subhonmesh and Bodine-Baron, Elizabeth, et el. (2015) The Cost of an Epidemic over a Complex Network: A Random Matrix Approach; 10.48550/arXiv.1309.2236
• Echenique, Federico and Golovin, Daniel, et el. (2010) A Revealed Preference Approach to Computational Complexity in Economics; 10.7907/cm0gg-20z31