{"total":18,"rows":[{"id":"2302.09739","title":"A Blackbox Approach to Best of Both Worlds in Bandits and Beyond","created":"2023-02-20","cited_by_count":30,"area":null},{"id":"2206.00873","title":"Nearly Optimal Best-of-Both-Worlds Algorithms for Online Learning with Feedback Graphs","created":"2022-06-02","cited_by_count":26,"area":null},{"id":"2302.13534","title":"Improved Best-of-Both-Worlds Guarantees for Multi-Armed Bandits: FTRL with General Regularizers and Multiple Optimal Arms","created":"2023-02-27","cited_by_count":18,"area":null},{"id":"2206.00557","title":"A Near-Optimal Best-of-Both-Worlds Algorithm for Online Learning with Feedback Graphs","created":"2022-06-01","cited_by_count":17,"area":null},{"id":"2302.09408","title":"Best of Both Worlds Policy Optimization","created":"2023-02-18","cited_by_count":17,"area":null},{"id":"2103.12487","title":"Improved Analysis of the Tsallis-INF Algorithm in Stochastically Constrained Adversarial Bandits and Stochastic Bandits with Adversarial Corruptions","created":"2021-03-23","cited_by_count":15,"area":null},{"id":"2206.03098","title":"Better Best of Both Worlds Bounds for Bandits with Switching Costs","created":"2022-06-07","cited_by_count":13,"area":null},{"id":"2403.00715","title":"Adaptive Learning Rate for Follow-the-Regularized-Leader: Competitive Analysis and Best-of-Both-Worlds","created":"2024-03-01","cited_by_count":11,"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":"2306.09588","title":"Understanding the Role of Feedback in Online Learning with Switching Costs","created":"2023-06-16","cited_by_count":4,"area":null},{"id":"2303.01709","title":"Streaming Algorithms for Learning with Experts: Deterministic Versus Robust","created":"2023-03-03","cited_by_count":3,"area":null},{"id":"2410.01857","title":"Learning the Optimal Path and DNN Partition for Collaborative Edge Inference","created":"2024-10-02","cited_by_count":3,"area":null},{"id":"2506.05479","title":"Learning-Augmented Algorithms for MTS with Bandit Access to Multiple Predictors","created":"2025-06-05","cited_by_count":2,"area":null},{"id":"2211.01798","title":"Phase Transitions in Learning and Earning under Price Protection Guarantee","created":"2022-11-03","cited_by_count":0,"area":null},{"id":"2404.01883","title":"Adversarial Combinatorial Bandits with Switching Costs","created":"2024-04-02","cited_by_count":0,"area":null},{"id":"2410.14533","title":"The Traveling Bandit: A Framework for Bayesian Optimization with Movement Costs","created":"2024-10-18","cited_by_count":0,"area":null},{"id":"2606.27448","title":"Learning in Markovian bandits with non-observable states and constrained decision epochs","created":"2026-06-25","cited_by_count":0,"area":null},{"id":"2610.14790","title":"Counterfactual Regret for Reflective Agents with Lean-Verified Guarantees","created":"2026-10-10","cited_by_count":0,"area":null}],"next_cursor":null}