{"total":25,"rows":[{"id":"2310.19647","title":"Fast swap regret minimization and applications to approximate correlated equilibria","created":"2023-10-30","cited_by_count":32,"area":null},{"id":"2305.14709","title":"Regret Matching+: (In)Stability and Fast Convergence in Games","created":"2023-05-24","cited_by_count":14,"area":null},{"id":"2307.09470","title":"Multi-Player Zero-Sum Markov Games with Networked Separable Interactions","created":"2023-07-13","cited_by_count":10,"area":null},{"id":"2502.17625","title":"Instance-Dependent Regret Bounds for Learning Two-Player Zero-Sum Games with Bandit Feedback","created":"2025-02-24","cited_by_count":10,"area":null},{"id":"2503.24340","title":"Faster Rates for No-Regret Learning in General Games via Cautious Optimism","created":"2025-03-31","cited_by_count":10,"area":null},{"id":"2407.04889","title":"Maximizing utility in multi-agent environments by anticipating the behavior of other learners","created":"2024-07-05","cited_by_count":8,"area":null},{"id":"2502.20229","title":"Swap Regret and Correlated Equilibria Beyond Normal-Form Games","created":"2025-02-27","cited_by_count":8,"area":null},{"id":"2412.07120","title":"Corrupted Learning Dynamics in Games","created":"2024-12-10","cited_by_count":4,"area":null},{"id":"2412.20203","title":"No-regret learning in harmonic games: Extrapolation in the face of conflicting interests","created":"2024-12-28","cited_by_count":4,"area":null},{"id":"2602.06348","title":"Adversarial Learning in Games with Bandit Feedback: Logarithmic Pure-Strategy Maximin Regret","created":"2026-02-06","cited_by_count":4,"area":null},{"id":"2208.09747","title":"Near-Optimal $\\Phi$-Regret Learning in Extensive-Form Games","created":"2022-08-20","cited_by_count":3,"area":null},{"id":"2511.01852","title":"Proximal Regret and Proximal Correlated Equilibria: A New Tractable Solution Concept for Online Learning and Games","created":"2025-11-03","cited_by_count":3,"area":null},{"id":"2608.31166","title":"Constant Individual Regret in General Games","created":"2026-08-31","cited_by_count":3,"area":null},{"id":"2304.12768","title":"Towards Characterizing the First-order Query Complexity of Learning (Approximate) Nash Equilibria in Zero-sum Matrix Games","created":"2023-04-25","cited_by_count":2,"area":null},{"id":"2410.23398","title":"On the Optimality of Dilated Entropy and Lower Bounds for Online Learning in Extensive-Form Games","created":"2024-10-30","cited_by_count":2,"area":null},{"id":"2503.04202","title":"Computational Intractability of Strategizing against Online Learners","created":"2025-03-06","cited_by_count":2,"area":null},{"id":"2505.17277","title":"Comparator-Adaptive $\\Phi$-Regret: Improved Bounds, Simpler Algorithms, and Applications to Games","created":"2025-05-22","cited_by_count":2,"area":null},{"id":"2506.16736","title":"Optimism Without Regularization: Constant Regret in Zero-Sum Games","created":"2025-06-20","cited_by_count":2,"area":null},{"id":"2608.24731","title":"Optimal Alternating Regret for Online Learning and Games","created":"2026-08-25","cited_by_count":2,"area":null},{"id":"2609.04113","title":"Constant regret in general games via higher-order optimism","created":"2026-09-03","cited_by_count":2,"area":null},{"id":"2504.03592","title":"Optimistic Online Learning in Symmetric Cone Games","created":"2025-04-04","cited_by_count":0,"area":null},{"id":"2512.22552","title":"Computing Pure-Strategy Nash Equilibria in a Two-Party Policy Competition: Existence and Algorithmic Approaches","created":"2025-12-27","cited_by_count":0,"area":null},{"id":"2608.25182","title":"Minimax Alternating Regret for the Experts Problem and Online Convex Optimization","created":"2026-08-25","cited_by_count":0,"area":null},{"id":"2609.16751","title":"Constant Swap Regret in General-Sum Games via Two-Scale Higher-Order Optimism","created":"2026-09-15","cited_by_count":0,"area":null},{"id":"2609.22839","title":"A Horizon-Independent Regret Bound for Optimistic Hedge in General-Sum Games","created":"2026-09-19","cited_by_count":0,"area":null}],"next_cursor":null}