{"total":52,"rows":[{"id":"2102.05858","title":"Achieving Near Instance-Optimality and Minimax-Optimality in Stochastic and Adversarial Linear Bandits Simultaneously","created":"2021-02-11","cited_by_count":48,"area":null},{"id":"2110.03580","title":"A Model Selection Approach for Corruption Robust Reinforcement Learning","created":"2021-10-07","cited_by_count":45,"area":null},{"id":"2102.11050","title":"Online Learning via Offline Greedy Algorithms: Applications in Market Design and Optimization","created":"2021-02-18","cited_by_count":40,"area":null},{"id":"2106.04117","title":"The best of both worlds: stochastic and adversarial episodic MDPs with unknown transition","created":"2021-06-08","cited_by_count":40,"area":null},{"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":"2109.10963","title":"On Optimal Robustness to Adversarial Corruption in Online Decision Problems","created":"2021-09-22","cited_by_count":21,"area":null},{"id":"2102.09864","title":"An Algorithm for Stochastic and Adversarial Bandits with Switching Costs","created":"2021-02-19","cited_by_count":19,"area":null},{"id":"2207.14550","title":"Best-of-Both-Worlds Algorithms for Partial Monitoring","created":"2022-07-29","cited_by_count":19,"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":"2305.08629","title":"A Unified Analysis of Nonstochastic Delayed Feedback for Combinatorial Semi-Bandits, Linear Bandits, and MDPs","created":"2023-05-15","cited_by_count":15,"area":null},{"id":"2302.12370","title":"Best-of-Three-Worlds Linear Bandit Algorithm with Variance-Adaptive Regret Bounds","created":"2023-02-24","cited_by_count":14,"area":null},{"id":"2206.06810","title":"Adversarially Robust Multi-Armed Bandit Algorithm with Variance-Dependent Regret Bounds","created":"2022-06-14","cited_by_count":12,"area":null},{"id":"2303.06825","title":"Best-of-three-worlds Analysis for Linear Bandits with Follow-the-regularized-leader Algorithm","created":"2023-03-13","cited_by_count":12,"area":null},{"id":"2312.15433","title":"Best-of-Both-Worlds Algorithms for Linear Contextual Bandits","created":"2023-12-24","cited_by_count":11,"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":"2301.10500","title":"Banker Online Mirror Descent: A Universal Approach for Delayed Online Bandit Learning","created":"2023-01-25","cited_by_count":7,"area":null},{"id":"2410.07533","title":"Corruption-Robust Linear Bandits: Minimax Optimality and Gap-Dependent Misspecification","created":"2024-10-10","cited_by_count":7,"area":null},{"id":"2102.12722","title":"Combinatorial Bandits under Strategic Manipulations","created":"2021-02-25","cited_by_count":6,"area":null},{"id":"2504.00461","title":"Efficient Near-Optimal Algorithm for Online Shortest Paths in Directed Acyclic Graphs with Bandit Feedback Against Adaptive Adversaries","created":"2025-04-01","cited_by_count":6,"area":null},{"id":"2305.17402","title":"Learning and Collusion in Multi-unit Auctions","created":"2023-05-27","cited_by_count":5,"area":null},{"id":"2501.19300","title":"Offline Learning for Combinatorial Multi-armed Bandits","created":"2025-01-31","cited_by_count":5,"area":null},{"id":"2106.06712","title":"Simple Combinatorial Algorithms for Combinatorial Bandits: Corruptions and Approximations","created":"2021-06-12","cited_by_count":4,"area":null},{"id":"2107.09572","title":"Best-of-All-Worlds Bounds for Online Learning with Feedback Graphs","created":"2021-07-20","cited_by_count":4,"area":null},{"id":"2212.12923","title":"Linear Combinatorial Semi-Bandit with Causally Related Rewards","created":"2022-12-25","cited_by_count":4,"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":"2403.03219","title":"LC-Tsallis-INF: Generalized Best-of-Both-Worlds Linear Contextual Bandits","created":"2024-03-05","cited_by_count":4,"area":null},{"id":"2608.04149","title":"Sublogarithmic Swap Regret in Multiplayer General-Sum Games via Hybrid Regularization","created":"2026-08-04","cited_by_count":4,"area":null},{"id":"2402.08321","title":"Exploration by Optimization with Hybrid Regularizers: Logarithmic Regret with Adversarial Robustness in Partial Monitoring","created":"2024-02-13","cited_by_count":3,"area":null},{"id":"2405.20028","title":"A Simple and Adaptive Learning Rate for FTRL in Online Learning with Minimax Regret of $\\Theta(T^{2/3})$ and its Application to Best-of-Both-Worlds","created":"2024-05-30","cited_by_count":3,"area":null},{"id":"2409.20440","title":"Optimism in the Face of Ambiguity Principle for Multi-Armed Bandits","created":"2024-09-30","cited_by_count":3,"area":null},{"id":"2508.18604","title":"Revisiting Follow-the-Perturbed-Leader with Unbounded Perturbations in Bandit Problems","created":"2025-08-26","cited_by_count":3,"area":null},{"id":"2207.05437","title":"Simultaneously Learning Stochastic and Adversarial Bandits under the Position-Based Model","created":"2022-07-12","cited_by_count":2,"area":null},{"id":"2504.07307","title":"Follow-the-Perturbed-Leader Approaches Best-of-Both-Worlds for the m-Set Semi-Bandit Problems","created":"2025-04-09","cited_by_count":2,"area":null},{"id":"2508.18768","title":"Efficient Best-of-Both-Worlds Algorithms for Contextual Combinatorial Semi-Bandits","created":"2025-08-26","cited_by_count":2,"area":null},{"id":"2307.09093","title":"Non-stationary Delayed Combinatorial Semi-Bandit with Causally Related Rewards","created":"2023-07-18","cited_by_count":1,"area":null},{"id":"2506.12490","title":"Note on Follow-the-Perturbed-Leader in Combinatorial Semi-Bandit Problems","created":"2025-06-14","cited_by_count":1,"area":null},{"id":"2510.22819","title":"Last-Iterate Analyses of FTRL with the 1/2-Tsallis Entropy in Stochastic Bandits","created":"2025-10-26","cited_by_count":1,"area":null},{"id":"2606.06043","title":"Adaptive Learning Rates with Surrogate Probability for Follow-the-Perturbed-Leader","created":"2026-06-04","cited_by_count":1,"area":null},{"id":"2607.13686","title":"Optimal and Efficient Contextual Combinatorial Semi-bandits with General Function Approximation","created":"2026-07-15","cited_by_count":1,"area":null},{"id":"2104.12827","title":"Learning-based decentralized offloading decision making in an adversarial environment","created":"2021-04-26","cited_by_count":0,"area":null},{"id":"2308.12680","title":"Master-slave Deep Architecture for Top-K Multi-armed Bandits with Non-linear Bandit Feedback and Diversity Constraints","created":"2023-08-24","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":"2502.07514","title":"A Near-optimal, Scalable and Parallelizable Framework for Stochastic Bandits Robust to Adversarial Corruptions and Beyond","created":"2025-02-11","cited_by_count":0,"area":null},{"id":"2508.06247","title":"Near-Optimal Regret for Efficient Stochastic Combinatorial Semi-Bandits","created":"2025-08-08","cited_by_count":0,"area":null},{"id":"2509.09933","title":"Multi-Play Combinatorial Semi-Bandit Problem","created":"2025-09-12","cited_by_count":0,"area":null},{"id":"2603.18972","title":"Best-of-Both-Worlds Multi-Dueling Bandits: Unified Algorithms for Stochastic and Adversarial Preferences under Condorcet and Borda Objectives","created":"2026-03-19","cited_by_count":0,"area":null},{"id":"2605.14953","title":"Efficient Online Conformal Selection with Limited Feedback","created":"2026-05-14","cited_by_count":0,"area":null},{"id":"2605.31070","title":"Learning to Bid in FCR Markets: A Best-of-Both-Worlds Approach","created":"2026-05-29","cited_by_count":0,"area":null},{"id":"2608.15365","title":"Does 1/2-Tsallis-INF Also Work Well for Best-Arm Identification?","created":"2026-08-15","cited_by_count":0,"area":null}],"next_cursor":null}