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ORCIDAdam Wierman
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- affiliation: California Institute of Technology, Pasadena, CA, USA
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2020 – today
- 2024
[j117]Zhengfei Zhang, Kishan Panaganti, Laixi Shi, Yanan Sui, Adam Wierman, Yisong Yue:
Distributionally Robust Constrained Reinforcement Learning under Strong Duality. RLJ 4: 1793-1821 (2024)- [j116]Palma London
, Shai Vardi, Reza Eghbali, Adam Wierman:
Black-Box Acceleration of Monotone Convex Program Solvers. Oper. Res. 72(2): 796-815 (2024) - [j115]Junjie Qin, Shai Vardi, Adam Wierman:
Minimization Fractional Prophet Inequalities for Sequential Procurement. Math. Oper. Res. 49(2): 928-947 (2024) - [j114]Guocheng Liao, Yu Su, Juba Ziani, Adam Wierman, Jianwei Huang:
The Privacy Paradox and Optimal Bias-Variance Trade-offs in Data Acquisition. Math. Oper. Res. 49(4): 2749-2767 (2024) - [j113]Pengfei Li, Jianyi Yang, Adam Wierman, Shaolei Ren:
Learning-Augmented Decentralized Online Convex Optimization in Networks. Proc. ACM Meas. Anal. Comput. Syst. 8(3): 38:1-38:42 (2024) - [j112]Jie Feng, Yuanyuan Shi, Guannan Qu, Steven H. Low, Anima Anandkumar, Adam Wierman:
Stability Constrained Reinforcement Learning for Decentralized Real-Time Voltage Control. IEEE Trans. Control. Netw. Syst. 11(3): 1370-1381 (2024) - [j111]Yu Su, Vivek Anand, Jannie Yu, Jian Tan, Adam Wierman:
Learning-Augmented Energy-Aware List Scheduling for Precedence-Constrained Tasks. ACM Trans. Model. Perform. Evaluation Comput. Syst. 9(4): 13:1-13:24 (2024) - [j110]Christopher Yeh, Jing Yu, Yuanyuan Shi, Adam Wierman:
Online Learning for Robust Voltage Control Under Uncertain Grid Topology. IEEE Trans. Smart Grid 15(5): 4754-4764 (2024) - [c144]Nicolas Christianson, Bo Sun, Steven H. Low, Adam Wierman:
Risk-Sensitive Online Algorithms (Extended Abstract). COLT 2024: 1140-1141 - [c143]Yiheng Lin, James A. Preiss, Fengze Xie, Emile Anand, Soon-Jo Chung, Yisong Yue, Adam Wierman:
Online Policy Optimization in Unknown Nonlinear Systems. COLT 2024: 3475-3522 - [c142]Bo Sun, Jerry Huang, Nicolas Christianson, Mohammad Hajiesmaili, Adam Wierman, Raouf Boutaba:
Online Algorithms with Uncertainty-Quantified Predictions. ICML 2024 - [c141]Neelkamal Bhuyan, Debankur Mukherjee, Adam Wierman:
Best of Both Worlds Guarantees for Smoothed Online Quadratic Optimization. ICML 2024 - [c140]Adam Lechowicz, Nicolas Christianson, Bo Sun, Noman Bashir, Mohammad Hajiesmaili, Adam Wierman, Prashant J. Shenoy:
Chasing Convex Functions with Long-term Constraints. ICML 2024 - [c139]Yingying Li, Jing Yu, Lauren Conger, Taylan Kargin, Adam Wierman:
Learning the Uncertainty Sets of Linear Control Systems via Set Membership: A Non-asymptotic Analysis. ICML 2024 - [c138]Kishan Panaganti, Adam Wierman, Eric Mazumdar:
Model-Free Robust ϕ-Divergence Reinforcement Learning Using Both Offline and Online Data. ICML 2024 - [c137]Laixi Shi, Eric Mazumdar, Yuejie Chi, Adam Wierman:
Sample-Efficient Robust Multi-Agent Reinforcement Learning in the Face of Environmental Uncertainty. ICML 2024 - [c136]Junxuan Shen, Adam Wierman, Guannan Qu:
Combining model-based controller and ML advice via convex reparameterization. L4DC 2024: 679-693 - [c135]Tianyu Chen, Yiheng Lin, Nicolas Christianson, Zahaib Akhtar, Sharath Dharmaji, Mohammad Hajiesmaili, Adam Wierman, Ramesh K. Sitaraman:
SODA: An Adaptive Bitrate Controller for Consistent High-Quality Video Streaming. SIGCOMM 2024: 613-644 - [c134]Zaiwei Chen, Kaiqing Zhang, Eric Mazumdar, Asuman E. Ozdaglar, Adam Wierman:
Two-Timescale Q-Learning with Function Approximation in Zero-Sum Stochastic Games. EC 2024: 378 - [c133]Adam Lechowicz, Nicolas Christianson, Bo Sun, Noman Bashir, Mohammad Hajiesmaili, Adam Wierman, Prashant J. Shenoy:
Online Conversion with Switching Costs: Robust and Learning-Augmented Algorithms. SIGMETRICS/Performance (Abstracts) 2024: 45-46 - [c132]Adam Lechowicz, Nicolas Christianson, Jinhang Zuo, Noman Bashir, Mohammad Hajiesmaili, Adam Wierman, Prashant J. Shenoy:
The Online Pause and Resume Problem: Optimal Algorithms and An Application to Carbon-Aware Load Shifting. SIGMETRICS/Performance (Abstracts) 2024: 47-48 - [i109]Adam Lechowicz, Nicolas Christianson, Bo Sun, Noman Bashir, Mohammad Hajiesmaili, Adam Wierman, Prashant J. Shenoy:
Chasing Convex Functions with Long-term Constraints. CoRR abs/2402.14012 (2024) - [i108]Laixi Shi, Eric Mazumdar, Yuejie Chi, Adam Wierman:
Sample-Efficient Robust Multi-Agent Reinforcement Learning in the Face of Environmental Uncertainty. CoRR abs/2404.18909 (2024) - [i107]Kishan Panaganti, Adam Wierman, Eric Mazumdar:
Model-Free Robust φ-Divergence Reinforcement Learning Using Both Offline and Online Data. CoRR abs/2405.05468 (2024) - [i106]Nicolas Christianson, Bo Sun, Steven H. Low, Adam Wierman:
Risk-Sensitive Online Algorithms. CoRR abs/2405.09859 (2024) - [i105]Benjamin C. Lee, David Brooks, Arthur van Benthem, Udit Gupta, Gage Hills, Vincent Liu, Benjamin Pierce, Christopher Stewart, Emma Strubell, Gu-Yeon Wei, Adam Wierman, Yuan Yao, Minlan Yu:
Carbon Connect: An Ecosystem for Sustainable Computing. CoRR abs/2405.13858 (2024) - [i104]Ruiyang Jin, Zaiwei Chen, Yiheng Lin, Jie Song, Adam Wierman:
Approximate Global Convergence of Independent Learning in Multi-Agent Systems. CoRR abs/2405.19811 (2024) - [i103]Shangding Gu, Laixi Shi, Yuhao Ding, Alois Knoll, Costas J. Spanos, Adam Wierman, Ming Jin:
Enhancing Efficiency of Safe Reinforcement Learning via Sample Manipulation. CoRR abs/2405.20860 (2024) - [i102]Zhengfei Zhang, Kishan Panaganti, Laixi Shi, Yanan Sui, Adam Wierman, Yisong Yue:
Distributionally Robust Constrained Reinforcement Learning under Strong Duality. CoRR abs/2406.15788 (2024) - [i101]Adam Lechowicz, Nicolas Christianson, Bo Sun, Noman Bashir, Mohammad Hajiesmaili, Adam Wierman, Prashant J. Shenoy:
CarbonClipper: Optimal Algorithms for Carbon-Aware Spatiotemporal Workload Management. CoRR abs/2408.07831 (2024) - [i100]Zaiwei Chen, Kaiqing Zhang, Eric Mazumdar, Asuman E. Ozdaglar, Adam Wierman:
Last-Iterate Convergence of Payoff-Based Independent Learning in Zero-Sum Stochastic Games. CoRR abs/2409.01447 (2024) - [i99]Laixi Shi, Jingchu Gai, Eric Mazumdar, Yuejie Chi, Adam Wierman:
Can We Break the Curse of Multiagency in Robust Multi-Agent Reinforcement Learning? CoRR abs/2409.20067 (2024) - [i98]Christopher Yeh, Nicolas Christianson, Alan Wu, Adam Wierman, Yisong Yue:
End-to-End Conformal Calibration for Optimization Under Uncertainty. CoRR abs/2409.20534 (2024) - [i97]Chengrui Qu, Laixi Shi, Kishan Panaganti, Pengcheng You, Adam Wierman:
Hybrid Transfer Reinforcement Learning: Provable Sample Efficiency from Shifted-Dynamics Data. CoRR abs/2411.03810 (2024) - [i96]Jianyi Yang, Pengfei Li, Adam Wierman, Shaolei Ren:
Online Budgeted Matching with General Bids. CoRR abs/2411.04204 (2024) - [i95]Chenbei Lu, Laixi Shi, Zaiwei Chen, Chenye Wu, Adam Wierman:
Overcoming the Curse of Dimensionality in Reinforcement Learning Through Approximate Factorization. CoRR abs/2411.07591 (2024) - [i94]Tongxin Li, Tinashe Handina, Shaolei Ren, Adam Wierman:
Safe Exploitative Play with Untrusted Type Beliefs. CoRR abs/2411.07679 (2024) - [i93]Neelkamal Bhuyan, Debankur Mukherjee, Adam Wierman:
Communication Efficient Decentralization for Smoothed Online Convex Optimization. CoRR abs/2411.08355 (2024) - [i92]Yuelin Han, Zhifeng Wu, Pengfei Li, Adam Wierman, Shaolei Ren:
The Unpaid Toll: Quantifying the Public Health Impact of AI. CoRR abs/2412.06288 (2024) - 2023
- [j109]Yorie Nakahira, Andrés Ferragut, Adam Wierman:
Generalized Exact Scheduling: A Minimal-Variance Distributed Deadline Scheduler. Oper. Res. 71(2): 433-470 (2023) - [j108]Yu Su, Shai Vardi, Xiaoqi Ren, Adam Wierman:
Communication-aware scheduling of precedence-constrained tasks on related machines. Oper. Res. Lett. 51(6): 709-716 (2023) - [j107]Daan Rutten, Nicolas Christianson, Debankur Mukherjee, Adam Wierman:
Smoothed Online Optimization with Unreliable Predictions. Proc. ACM Meas. Anal. Comput. Syst. 7(1): 12:1-12:36 (2023) - [j106]Yizhou Zhang, Guannan Qu, Pan Xu, Yiheng Lin, Zaiwei Chen, Adam Wierman:
Global Convergence of Localized Policy Iteration in Networked Multi-Agent Reinforcement Learning. Proc. ACM Meas. Anal. Comput. Syst. 7(1): 13:1-13:51 (2023) - [j105]Jing Yu, Dimitar Ho, Adam Wierman:
Online Adversarial Stabilization of Unknown Networked Systems. Proc. ACM Meas. Anal. Comput. Syst. 7(1): 26:1-26:43 (2023) - [j104]Adam Lechowicz, Nicolas Christianson, Jinhang Zuo, Noman Bashir, Mohammad Hajiesmaili, Adam Wierman, Prashant J. Shenoy:
The Online Pause and Resume Problem: Optimal Algorithms and An Application to Carbon-Aware Load Shifting. Proc. ACM Meas. Anal. Comput. Syst. 7(3): 45:1-45:32 (2023) - [j103]Sungho Shin, Yiheng Lin, Guannan Qu, Adam Wierman, Mihai Anitescu:
Near-Optimal Distributed Linear-Quadratic Regulator for Networked Systems. SIAM J. Control. Optim. 61(3): 1113-1135 (2023) - [j102]Chen Liang, Linqi Guo, Alessandro Zocca, Steven H. Low, Adam Wierman:
Adaptive Network Response to Line Failures in Power Systems. IEEE Trans. Control. Netw. Syst. 10(1): 333-344 (2023) - [j101]Shih-Hao Tseng, SooJean Han, Adam Wierman:
Trading Throughput for Freshness: Freshness-aware Traffic Engineering and In-Network Freshness Control. ACM Trans. Model. Perform. Evaluation Comput. Syst. 8(1-2): 1-26 (2023) - [j100]Yue Chen, Changhong Zhao, Steven H. Low, Adam Wierman:
An Energy Sharing Mechanism Considering Network Constraints and Market Power Limitation. IEEE Trans. Smart Grid 14(2): 1027-1041 (2023) - [c131]Nicolas Christianson, Junxuan Shen, Adam Wierman:
Optimal robustness-consistency tradeoffs for learning-augmented metrical task systems. AISTATS 2023: 9377-9399 - [c130]Sahin Lale, Yuanyuan Shi, Guannan Qu, Kamyar Azizzadenesheli, Adam Wierman, Anima Anandkumar:
KCRL: Krasovskii-Constrained Reinforcement Learning with Guaranteed Stability in Nonlinear Discrete-Time Systems. CDC 2023: 1334-1341 - [c129]Lucien Werner, Nicolas Christianson, Alessandro Zocca, Adam Wierman, Steven H. Low:
Pricing Uncertainty in Stochastic Multi-Stage Electricity Markets. CDC 2023: 1580-1587 - [c128]Jing Yu, Varun Gupta, Adam Wierman:
Online Adversarial Stabilization of Unknown Linear Time-Varying Systems. CDC 2023: 8320-8327 - [c127]Xutong Liu, Jinhang Zuo, Siwei Wang, John C. S. Lui, Mohammad Hajiesmaili, Adam Wierman, Wei Chen:
Contextual Combinatorial Bandits with Probabilistically Triggered Arms. ICML 2023: 22559-22593 - [c126]Yingying Li, James A. Preiss, Na Li, Yiheng Lin, Adam Wierman, Jeff S. Shamma:
Online switching control with stability and regret guarantees. L4DC 2023: 1138-1151 - [c125]Zaiwei Chen, Kaiqing Zhang, Eric Mazumdar, Asuman E. Ozdaglar, Adam Wierman:
A Finite-Sample Analysis of Payoff-Based Independent Learning in Zero-Sum Stochastic Games. NeurIPS 2023 - [c124]Tongxin Li, Yiheng Lin, Shaolei Ren, Adam Wierman:
Beyond Black-Box Advice: Learning-Augmented Algorithms for MDPs with Q-Value Predictions. NeurIPS 2023 - [c123]Pengfei Li, Jianyi Yang, Adam Wierman, Shaolei Ren:
Robust Learning for Smoothed Online Convex Optimization with Feedback Delay. NeurIPS 2023 - [c122]Yiheng Lin, James A. Preiss, Emile Anand, Yingying Li, Yisong Yue, Adam Wierman:
Online Adaptive Policy Selection in Time-Varying Systems: No-Regret via Contractive Perturbations. NeurIPS 2023 - [c121]Jianyi Yang, Pengfei Li, Tongxin Li, Adam Wierman, Shaolei Ren:
Anytime-Competitive Reinforcement Learning with Policy Prior. NeurIPS 2023 - [c120]Christopher Yeh, Victor Li, Rajeev Datta, Julio Arroyo, Nicolas Christianson, Chi Zhang, Yize Chen, Mohammad Mehdi Hosseini, Azarang Golmohammadi, Yuanyuan Shi, Yisong Yue, Adam Wierman:
SustainGym: Reinforcement Learning Environments for Sustainable Energy Systems. NeurIPS 2023 - [c119]Jinhang Zuo, Zhiyao Zhang, Zhiyong Wang, Shuai Li, Mohammad Hajiesmaili, Adam Wierman:
Adversarial Attacks on Online Learning to Rank with Click Feedback. NeurIPS 2023 - [c118]Bo Sun, Lin Yang, Mohammad H. Hajiesmaili, Adam Wierman, John C. S. Lui, Don Towsley, Danny H. K. Tsang:
The Online Knapsack Problem with Departures. SIGMETRICS (Abstracts) 2023: 59-60 - [c117]Daan Rutten, Nicolas Christianson, Debankur Mukherjee, Adam Wierman:
Smoothed Online Optimization with Unreliable Predictions. SIGMETRICS (Abstracts) 2023: 71-72 - [c116]Jing Yu, Dimitar Ho, Adam Wierman:
Online Adversarial Stabilization of Unknown Networked Systems. SIGMETRICS (Abstracts) 2023: 73-74 - [c115]Yizhou Zhang, Guannan Qu, Pan Xu, Yiheng Lin, Zaiwei Chen, Adam Wierman:
Global Convergence of Localized Policy Iteration in Networked Multi-Agent Reinforcement Learning. SIGMETRICS (Abstracts) 2023: 83-84 - [c114]Zhaoyi Zhou, Zaiwei Chen, Yiheng Lin, Adam Wierman:
Convergence rates for localized actor-critic in networked Markov potential games. UAI 2023: 2563-2573 - [c113]Chenkai Yu, Hongyao Ma, Adam Wierman:
Price Cycles in Ridesharing Platforms. WINE 2023: 618-636 - [i91]Yingying Li, James A. Preiss, Na Li, Yiheng Lin, Adam Wierman, Jeff S. Shamma:
Online switching control with stability and regret guarantees. CoRR abs/2301.08445 (2023) - [i90]Zaiwei Chen, Kaiqing Zhang, Eric Mazumdar, Asuman E. Ozdaglar, Adam Wierman:
A Finite-Sample Analysis of Payoff-Based Independent Learning in Zero-Sum Stochastic Games. CoRR abs/2303.03100 (2023) - [i89]Zhaoyi Zhou, Zaiwei Chen, Yiheng Lin, Adam Wierman:
Convergence Rates for Localized Actor-Critic in Networked Markov Potential Games. CoRR abs/2303.04865 (2023) - [i88]Xutong Liu, Jinhang Zuo, Siwei Wang, John C. S. Lui, Mohammad H. Hajiesmaili, Adam Wierman, Wei Chen:
Contextual Combinatorial Bandits with Probabilistically Triggered Arms. CoRR abs/2303.17110 (2023) - [i87]Adam Lechowicz, Nicolas Christianson, Jinhang Zuo, Noman Bashir, Mohammad H. Hajiesmaili, Adam Wierman, Prashant J. Shenoy:
The Online Pause and Resume Problem: Optimal Algorithms and An Application to Carbon-Aware Load Shifting. CoRR abs/2303.17551 (2023) - [i86]Jing Yu, Varun Gupta, Adam Wierman:
Online Stabilization of Unknown Linear Time-Varying Systems. CoRR abs/2304.02878 (2023) - [i85]Jinhang Zuo, Zhiyao Zhang, Zhiyong Wang, Shuai Li, Mohammad H. Hajiesmaili, Adam Wierman:
Adversarial Attacks on Online Learning to Rank with Click Feedback. CoRR abs/2305.17071 (2023) - [i84]Pengfei Li, Jianyi Yang, Adam Wierman, Shaolei Ren:
Learning-Augmented Decentralized Online Convex Optimization in Networks. CoRR abs/2306.10158 (2023) - [i83]Christopher Yeh, Jing Yu, Yuanyuan Shi, Adam Wierman:
Online learning for robust voltage control under uncertain grid topology. CoRR abs/2306.16674 (2023) - [i82]Pengfei Li, Jianyi Yang, Adam Wierman, Shaolei Ren:
Towards Environmentally Equitable AI via Geographical Load Balancing. CoRR abs/2307.05494 (2023) - [i81]Tongxin Li, Yiheng Lin, Shaolei Ren, Adam Wierman:
Beyond Black-Box Advice: Learning-Augmented Algorithms for MDPs with Q-Value Predictions. CoRR abs/2307.10524 (2023) - [i80]Yingying Li, Jing Yu, Lauren Conger, Adam Wierman:
Learning the Uncertainty Sets for Control Dynamics via Set Membership: A Non-Asymptotic Analysis. CoRR abs/2309.14648 (2023) - [i79]Bo Sun, Jerry Huang, Nicolas Christianson, Mohammad Hajiesmaili, Adam Wierman:
Online Algorithms with Uncertainty-Quantified Predictions. CoRR abs/2310.11558 (2023) - [i78]Pengfei Li, Jianyi Yang, Adam Wierman, Shaolei Ren:
Robust Learning for Smoothed Online Convex Optimization with Feedback Delay. CoRR abs/2310.20098 (2023) - [i77]Adam Lechowicz, Nicolas Christianson, Bo Sun, Noman Bashir, Mohammad Hajiesmaili, Adam Wierman, Prashant J. Shenoy:
Online Conversion with Switching Costs: Robust and Learning-Augmented Algorithms. CoRR abs/2310.20598 (2023) - [i76]Neelkamal Bhuyan, Debankur Mukherjee, Adam Wierman:
Best of Both Worlds: Stochastic and Adversarial Convex Function Chasing. CoRR abs/2311.00181 (2023) - [i75]Jianyi Yang, Pengfei Li, Tongxin Li, Adam Wierman, Shaolei Ren:
Anytime-Competitive Reinforcement Learning with Policy Prior. CoRR abs/2311.01568 (2023) - [i74]Jinhang Zuo, Zhiyao Zhang, Xuchuang Wang, Cheng Chen, Shuai Li, John C. S. Lui, Mohammad Hajiesmaili, Adam Wierman:
Adversarial Attacks on Cooperative Multi-agent Bandits. CoRR abs/2311.01698 (2023) - [i73]Zaiwei Chen, Kaiqing Zhang, Eric Mazumdar, Asuman E. Ozdaglar, Adam Wierman:
Two-Timescale Q-Learning with Function Approximation in Zero-Sum Stochastic Games. CoRR abs/2312.04905 (2023) - 2022
- [j99]John Z. F. Pang, Weixuan Lin, Hu Fu, Jack Kleeman, Eilyan Bitar, Adam Wierman:
Transparency and Control in Platforms for Networked Markets. Oper. Res. 70(3): 1665-1690 (2022) - [j98]Guannan Qu, Adam Wierman, Na Li:
Scalable Reinforcement Learning for Multiagent Networked Systems. Oper. Res. 70(6): 3601-3628 (2022) - [j97]Weici Pan, Guanya Shi, Yiheng Lin, Adam Wierman:
Online Optimization with Feedback Delay and Nonlinear Switching Cost. Proc. ACM Meas. Anal. Comput. Syst. 6(1): 17:1-17:34 (2022) - [j96]Tongxin Li, Ruixiao Yang, Guannan Qu, Guanya Shi, Chenkai Yu, Adam Wierman, Steven H. Low:
Robustness and Consistency in Linear Quadratic Control with Untrusted Predictions. Proc. ACM Meas. Anal. Comput. Syst. 6(1): 18:1-18:35 (2022) - [j95]Yu Su, Jannie Yu, Vivek Anand, Adam Wierman:
Learning-Augmented Energy-Aware Scheduling of Precedence-Constrained Tasks. SIGMETRICS Perform. Evaluation Rev. 49(2): 3-5 (2022) - [j94]Guocheng Liao, Yu Su, Juba Ziani, Adam Wierman, Jianwei Huang:
The Privacy Paradox and Optimal Bias-Variance Trade-offs in Data Acquisition. SIGMETRICS Perform. Evaluation Rev. 49(2): 6-8 (2022) - [c112]Yang Hu, Adam Wierman, Guannan Qu:
On the Sample Complexity of Stabilizing LTI Systems on a Single Trajectory (Extended Abstract). Allerton 2022: 1-2 - [c111]Chenkai Yu, Guanya Shi, Soon-Jo Chung, Yisong Yue, Adam Wierman:
Competitive Control with Delayed Imperfect Information. ACC 2022: 2604-2610 - [c110]Yuanyuan Shi, Guannan Qu, Steven H. Low, Anima Anandkumar, Adam Wierman:
Stability Constrained Reinforcement Learning for Real-Time Voltage Control. ACC 2022: 2715-2721 - [c109]Chen Liang, Alessandro Zocca, Steven H. Low, Adam Wierman:
Interface Networks for Failure Localization in Power Systems. ACC 2022: 4540-4546 - [c108]Nicolas Christianson, Tinashe Handina, Adam Wierman:
Chasing Convex Bodies and Functions with Black-Box Advice. COLT 2022: 867-908 - [c107]Christopher Yeh, Jing Yu, Yuanyuan Shi, Adam Wierman:
Robust online voltage control with an unknown grid topology. e-Energy 2022: 240-250 - [c106]Yiheng Lin, Judy Gan, Guannan Qu, Yash Kanoria, Adam Wierman:
Decentralized Online Convex Optimization in Networked Systems. ICML 2022: 13356-13393 - [c105]Yang Hu, Adam Wierman, Guannan Qu:
On the Sample Complexity of Stabilizing LTI Systems on a Single Trajectory. NeurIPS 2022 - [c104]Yiheng Lin, Yang Hu, Guannan Qu, Tongxin Li, Adam Wierman:
Bounded-Regret MPC via Perturbation Analysis: Prediction Error, Constraints, and Nonlinearity. NeurIPS 2022 - [c103]Weici Pan, Guanya Shi, Yiheng Lin, Adam Wierman:
Online Optimization with Feedback Delay and Nonlinear Switching Cost. SIGMETRICS (Abstracts) 2022: 81-82 - [c102]Tongxin Li, Ruixiao Yang, Guannan Qu, Guanya Shi, Chenkai Yu, Adam Wierman, Steven H. Low:
Robustness and Consistency in Linear Quadratic Control with Untrusted Predictions. SIGMETRICS (Abstracts) 2022: 107-108 - [i72]Daan Rutten, Nicolas Christianson, Debankur Mukherjee, Adam Wierman:
Online Optimization with Untrusted Predictions. CoRR abs/2202.03519 (2022) - [i71]Chenkai Yu, Hongyao Ma, Adam Wierman:
Price Cycles in Ridesharing Platforms. CoRR abs/2202.07086 (2022) - [i70]Yang Hu, Adam Wierman, Guannan Qu:
On the Sample Complexity of Stabilizing LTI Systems on a Single Trajectory. CoRR abs/2202.07187 (2022) - [i69]Jing Yu, Dimitar Ho, Adam Wierman:
Online Stabilization of Unknown Networked Systems with Communication Constraints. CoRR abs/2203.02630 (2022) - [i68]Yue Chen, Changhong Zhao, Steven H. Low, Adam Wierman:
An Energy Sharing Mechanism Considering Network Constraints and Market Power Limitation. CoRR abs/2203.04503 (2022) - [i67]Sungho Shin, Yiheng Lin, Guannan Qu, Adam Wierman, Mihai Anitescu:
Near-Optimal Distributed Linear-Quadratic Regulator for Networked Systems. CoRR abs/2204.05551 (2022) - [i66]Chen Liang, Alessandro Zocca, Steven H. Low, Adam Wierman:
Interface Networks for Failure Localization in Power Systems. CoRR abs/2205.06315 (2022) - [i65]Tongxin Li, Ruixiao Yang, Guannan Qu, Yiheng Lin, Steven H. Low, Adam Wierman:
Equipping Black-Box Policies with Model-Based Advice for Stable Nonlinear Control. CoRR abs/2206.01341 (2022) - [i64]Sahin Lale, Yuanyuan Shi, Guannan Qu, Kamyar Azizzadenesheli, Adam Wierman, Anima Anandkumar:
KCRL: Krasovskii-Constrained Reinforcement Learning with Guaranteed Stability in Nonlinear Dynamical Systems. CoRR abs/2206.01704 (2022) - [i63]Nicolas Christianson, Tinashe Handina, Adam Wierman:
Chasing Convex Bodies and Functions with Black-Box Advice. CoRR abs/2206.11780 (2022) - [i62]Christopher Yeh, Jing Yu, Yuanyuan Shi, Adam Wierman:
Robust Online Voltage Control with an Unknown Grid Topology. CoRR abs/2206.14369 (2022) - [i61]Jie Feng, Yuanyuan Shi, Guannan Qu, Steven H. Low, Anima Anandkumar, Adam Wierman:
Stability Constrained Reinforcement Learning for Real-Time Voltage Control in Distribution Systems. CoRR abs/2209.07669 (2022) - [i60]Bo Sun, Lin Yang, Mohammad H. Hajiesmaili, Adam Wierman, John C. S. Lui, Don Towsley, Danny H. K. Tsang:
The Online Knapsack Problem with Departures. CoRR abs/2209.11934 (2022) - [i59]