Wierman, Adam

57 publications

AISTATS 2025 Approximate Global Convergence of Independent Learning in Multi-Agent Systems Ruiyang Jin, Zaiwei Chen, Yiheng Lin, Jie Song, Adam Wierman

ICML 2025 Breaking the Curse of Multiagency in Robust Multi-Agent Reinforcement Learning Laixi Shi, Jingchu Gai, Eric Mazumdar, Yuejie Chi, Adam Wierman

NeurIPS 2025 Conformal Risk Training: End-to-End Optimization of Conformal Risk Control Christopher Yeh, Nicolas Christianson, Adam Wierman, Yisong Yue

NeurIPS 2025 Efficient Policy Optimization in Robust Constrained MDPs with Iteration Complexity Guarantees Sourav Ganguly, Kishan Panaganti, Arnob Ghosh, Adam Wierman

TMLR 2025 End-to-End Conformal Calibration for Optimization Under Uncertainty Christopher Yeh, Nicolas Christianson, Alan Wu, Adam Wierman, Yisong Yue

NeurIPS 2025 Fairness-Regularized Online Optimization with Switching Costs Pengfei Li, Yuelin Han, Adam Wierman, Shaolei Ren

ICML 2025 Fusing Reward and Dueling Feedback in Stochastic Bandits Xuchuang Wang, Qirun Zeng, Jinhang Zuo, Xutong Liu, Mohammad Hajiesmaili, John C.S. Lui, Adam Wierman

AISTATS 2025 Hybrid Transfer Reinforcement Learning: Provable Sample Efficiency from Shifted-Dynamics Data Chengrui Qu, Laixi Shi, Kishan Panaganti, Pengcheng You, Adam Wierman

NeurIPS 2025 Maximizing the Value of Predictions in Control: Accuracy Is Not Enough Yiheng Lin, Christopher Yeh, Zaiwei Chen, Adam Wierman

ICML 2025 Online Robust Reinforcement Learning Through Monte-Carlo Planning Tuan Quang Dam, Kishan Panaganti, Brahim Driss, Adam Wierman

ICML 2025 Overcoming the Curse of Dimensionality in Reinforcement Learning Through Approximate Factorization Chenbei Lu, Laixi Shi, Zaiwei Chen, Chenye Wu, Adam Wierman

NeurIPS 2025 Reinforcement Learning with Imperfect Transition Predictions: A Bellman-Jensen Approach Chenbei Lu, Zaiwei Chen, Tongxin Li, Chenye Wu, Adam Wierman

ICLR 2025 Robust Gymnasium: A Unified Modular Benchmark for Robust Reinforcement Learning Shangding Gu, Laixi Shi, Muning Wen, Ming Jin, Eric Mazumdar, Yuejie Chi, Adam Wierman, Costas Spanos

NeurIPS 2025 SPiDR: A Simple Approach for Zero-Shot Safety in Sim-to-Real Transfer Yarden As, Chengrui Qu, Benjamin Unger, Dongho Kang, Max van der Hart, Laixi Shi, Stelian Coros, Adam Wierman, Andreas Krause

ICML 2024 Best of Both Worlds Guarantees for Smoothed Online Quadratic Optimization Neelkamal Bhuyan, Debankur Mukherjee, Adam Wierman

ICML 2024 Chasing Convex Functions with Long-Term Constraints Adam Lechowicz, Nicolas Christianson, Bo Sun, Noman Bashir, Mohammad Hajiesmaili, Adam Wierman, Prashant Shenoy

L4DC 2024 Combining Model-Based Controller and ML Advice via Convex Reparameterization Junxuan Shen, Adam Wierman, Guannan Qu

NeurIPS 2024 Enhancing Efficiency of Safe Reinforcement Learning via Sample Manipulation Shangding Gu, Laixi Shi, Yuhao Ding, Alois Knoll, Costas Spanos, Adam Wierman, Ming Jin

ICML 2024 Learning the Uncertainty Sets of Linear Control Systems via Set Membership: A Non-Asymptotic Analysis Yingying Li, Jing Yu, Lauren Conger, Taylan Kargin, Adam Wierman

ICML 2024 Model-Free Robust $φ$-Divergence Reinforcement Learning Using Both Offline and Online Data Kishan Panaganti, Adam Wierman, Eric Mazumdar

NeurIPS 2024 Near-Optimal Distributionally Robust Reinforcement Learning with General $L_p$ Norms Pierre Clavier, Laixi Shi, Erwan Le Pennec, Eric Mazumdar, Adam Wierman, Matthieu Geist

ICML 2024 Online Algorithms with Uncertainty-Quantified Predictions Bo Sun, Jerry Huang, Nicolas Christianson, Mohammad Hajiesmaili, Adam Wierman, Raouf Boutaba

NeurIPS 2024 Online Budgeted Matching with General Bids Jianyi Yang, Pengfei Li, Adam Wierman, Shaolei Ren

COLT 2024 Online Policy Optimization in Unknown Nonlinear Systems Yiheng Lin, James A. Preiss, Fengze Xie, Emile Anand, Soon-Jo Chung, Yisong Yue, Adam Wierman

COLT 2024 Risk-Sensitive Online Algorithms (Extended Abstract) Nicolas Christianson, Bo Sun, Steven Low, Adam Wierman

NeurIPS 2024 Safe Exploitative Play with Untrusted Type Beliefs Tongxin Li, Tinashe Handina, Shaolei Ren, Adam Wierman

ICML 2024 Sample-Efficient Robust Multi-Agent Reinforcement Learning in the Face of Environmental Uncertainty Laixi Shi, Eric Mazumdar, Yuejie Chi, Adam Wierman

NeurIPS 2023 A Finite-Sample Analysis of Payoff-Based Independent Learning in Zero-Sum Stochastic Games Zaiwei Chen, Kaiqing Zhang, Eric Mazumdar, Asuman Ozdaglar, Adam Wierman

NeurIPS 2023 Adversarial Attacks on Online Learning to Rank with Click Feedback Jinhang Zuo, Zhiyao Zhang, Zhiyong Wang, Shuai Li, Mohammad Hajiesmaili, Adam Wierman

NeurIPS 2023 Anytime-Competitive Reinforcement Learning with Policy Prior Jianyi Yang, Pengfei Li, Tongxin Li, Adam Wierman, Shaolei Ren

NeurIPS 2023 Beyond Black-Box Advice: Learning-Augmented Algorithms for MDPs with Q-Value Predictions Tongxin Li, Yiheng Lin, Shaolei Ren, Adam Wierman

ICML 2023 Contextual Combinatorial Bandits with Probabilistically Triggered Arms Xutong Liu, Jinhang Zuo, Siwei Wang, John C.S. Lui, Mohammad Hajiesmaili, Adam Wierman, Wei Chen

UAI 2023 Convergence Rates for Localized Actor-Critic in Networked Markov Potential Games Zhaoyi Zhou, Zaiwei Chen, Yiheng Lin, Adam Wierman

NeurIPS 2023 Online Adaptive Policy Selection in Time-Varying Systems: No-Regret via Contractive Perturbations Yiheng Lin, James A. Preiss, Emile Anand, Yingying Li, Yisong Yue, Adam Wierman

L4DC 2023 Online Switching Control with Stability and Regret Guarantees Yingying Li, James A Preiss, Na Li, Yiheng Lin, Adam Wierman, Jeff S Shamma

AISTATS 2023 Optimal Robustness-Consistency Tradeoffs for Learning-Augmented Metrical Task Systems Nicolas Christianson, Junxuan Shen, Adam Wierman

NeurIPS 2023 Robust Learning for Smoothed Online Convex Optimization with Feedback Delay Pengfei Li, Jianyi Yang, Adam Wierman, Shaolei Ren

NeurIPS 2023 SustainGym: Reinforcement Learning Environments for Sustainable Energy Systems 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

NeurIPS 2022 Bounded-Regret MPC via Perturbation Analysis: Prediction Error, Constraints, and Nonlinearity Yiheng Lin, Yang Hu, Guannan Qu, Tongxin Li, Adam Wierman

COLT 2022 Chasing Convex Bodies and Functions with Black-Box Advice Nicolas Christianson, Tinashe Handina, Adam Wierman

ICML 2022 Decentralized Online Convex Optimization in Networked Systems Yiheng Lin, Judy Gan, Guannan Qu, Yash Kanoria, Adam Wierman

NeurIPS 2022 On the Sample Complexity of Stabilizing LTI Systems on a Single Trajectory Yang Hu, Adam Wierman, Guannan Qu

AAAI 2021 Data-Driven Competitive Algorithms for Online Knapsack and Set Cover Ali Zeynali, Bo Sun, Mohammad Hassan Hajiesmaili, Adam Wierman

NeurIPS 2021 Multi-Agent Reinforcement Learning in Stochastic Networked Systems Yiheng Lin, Guannan Qu, Longbo Huang, Adam Wierman

NeurIPS 2021 Pareto-Optimal Learning-Augmented Algorithms for Online Conversion Problems Bo Sun, Russell Lee, Mohammad Hajiesmaili, Adam Wierman, Danny Tsang

NeurIPS 2021 Perturbation-Based Regret Analysis of Predictive Control in Linear Time Varying Systems Yiheng Lin, Yang Hu, Guanya Shi, Haoyuan Sun, Guannan Qu, Adam Wierman

L4DC 2021 Stable Online Control of Linear Time-Varying Systems Guannan Qu, Yuanyuan Shi, Sahin Lale, Anima Anandkumar, Adam Wierman

COLT 2020 Finite-Time Analysis of Asynchronous Stochastic Approximation and $q$-Learning Guannan Qu, Adam Wierman

NeurIPS 2020 Online Optimization with Memory and Competitive Control Guanya Shi, Yiheng Lin, Soon-Jo Chung, Yisong Yue, Adam Wierman

NeurIPS 2020 Scalable Multi-Agent Reinforcement Learning for Networked Systems with Average Reward Guannan Qu, Yiheng Lin, Adam Wierman, Na Li

L4DC 2020 Scalable Reinforcement Learning of Localized Policies for Multi-Agent Networked Systems Guannan Qu, Adam Wierman, Na Li

NeurIPS 2020 The Power of Predictions in Online Control Chenkai Yu, Guanya Shi, Soon-Jo Chung, Yisong Yue, Adam Wierman

AISTATS 2019 An Online Algorithm for Smoothed Regression and LQR Control Gautam Goel, Adam Wierman

NeurIPS 2019 Beyond Online Balanced Descent: An Optimal Algorithm for Smoothed Online Optimization Gautam Goel, Yiheng Lin, Haoyuan Sun, Adam Wierman

AAAI 2018 A Parallelizable Acceleration Framework for Packing Linear Programs Palma London, Shai Vardi, Adam Wierman, Hanling Yi

COLT 2018 Smoothed Online Convex Optimization in High Dimensions via Online Balanced Descent Niangjun Chen, Gautam Goel, Adam Wierman

COLT 2013 A Tale of Two Metrics: Simultaneous Bounds on Competitiveness and Regret Lachlan L. H. Andrew, Siddharth Barman, Katrina Ligett, Minghong Lin, Adam Meyerson, Alan Roytman, Adam Wierman