{"total":124,"rows":[{"id":"2402.01139","title":"Online conformal prediction with decaying step sizes","created":"2024-02-02","cited_by_count":56,"area":null},{"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":"2107.01509","title":"Bayesian decision-making under misspecified priors with applications to meta-learning","created":"2021-07-03","cited_by_count":44,"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":"2210.10768","title":"Anytime-valid off-policy inference for contextual bandits","created":"2022-10-19","cited_by_count":37,"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":"2208.09407","title":"Learning in Stackelberg Games with Non-myopic Agents","created":"2022-08-19","cited_by_count":29,"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":"2110.13282","title":"The Pareto Frontier of model selection for general Contextual Bandits","created":"2021-10-25","cited_by_count":24,"area":null},{"id":"2206.14906","title":"A Best-of-Both-Worlds Algorithm for Bandits with Delayed Feedback","created":"2022-06-29","cited_by_count":23,"area":null},{"id":"2302.04552","title":"Optimistic Online Mirror Descent for Bridging Stochastic and Adversarial Online Convex Optimization","created":"2023-02-09","cited_by_count":22,"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":"2212.13669","title":"Near-Optimal Algorithms for Group Distributionally Robust Optimization and Beyond","created":"2022-12-28","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":"2305.17301","title":"Stability-penalty-adaptive follow-the-regularized-leader: Sparsity, game-dependency, and best-of-both-worlds","created":"2023-05-26","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":"2110.08627","title":"Achieving the Pareto Frontier of Regret Minimization and Best Arm Identification in Multi-Armed Bandits","created":"2021-10-16","cited_by_count":13,"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":"2212.00884","title":"Pareto Regret Analyses in Multi-objective Multi-armed Bandit","created":"2022-12-01","cited_by_count":13,"area":null},{"id":"2106.04700","title":"Scale Free Adversarial Multi Armed Bandits","created":"2021-06-08","cited_by_count":12,"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":"2206.14912","title":"Best of Both Worlds Model Selection","created":"2022-06-29","cited_by_count":11,"area":null},{"id":"2212.12567","title":"Adapting to game trees in zero-sum imperfect information games","created":"2022-12-23","cited_by_count":11,"area":null},{"id":"2308.10675","title":"A Best-of-both-worlds Algorithm for Bandits with Delayed Feedback with Robustness to Excessive Delays","created":"2023-08-21","cited_by_count":11,"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":"2110.09008","title":"When Are Linear Stochastic Bandits Attackable?","created":"2021-10-18","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":"2501.14349","title":"Online Inverse Linear Optimization: Efficient Logarithmic-Regret Algorithm, Robustness to Suboptimality, and Lower Bound","created":"2025-01-24","cited_by_count":9,"area":null},{"id":"2110.14804","title":"Minimax Optimal Quantile and Semi-Adversarial Regret via Root-Logarithmic Regularizers","created":"2021-10-27","cited_by_count":8,"area":null},{"id":"2305.15383","title":"On the Minimax Regret for Online Learning with Feedback Graphs","created":"2023-05-24","cited_by_count":8,"area":null},{"id":"2305.19036","title":"Delayed Bandits: When Do Intermediate Observations Help?","created":"2023-05-30","cited_by_count":8,"area":null},{"id":"2501.13648","title":"Revisiting Online Learning Approach to Inverse Linear Optimization: A Fenchel$-$Young Loss Perspective and Gap-Dependent Regret Analysis","created":"2025-01-23","cited_by_count":8,"area":null},{"id":"2107.11419","title":"Finite-time Analysis of Globally Nonstationary Multi-Armed Bandits","created":"2021-07-23","cited_by_count":7,"area":null},{"id":"2206.02656","title":"A Regret-Variance Trade-Off in Online Learning","created":"2022-06-06","cited_by_count":7,"area":null},{"id":"2303.03272","title":"Accelerated Rates between Stochastic and Adversarial Online Convex Optimization","created":"2023-03-06","cited_by_count":7,"area":null},{"id":"2310.16252","title":"Near-Optimal Pure Exploration in Matrix Games: A Generalization of Stochastic Bandits & Dueling Bandits","created":"2023-10-25","cited_by_count":7,"area":null},{"id":"2501.02926","title":"Offline-to-online hyperparameter transfer for stochastic bandits","created":"2025-01-06","cited_by_count":7,"area":"offlinerl"},{"id":"2111.07458","title":"Mean-based Best Arm Identification in Stochastic Bandits under Reward Contamination","created":"2021-11-14","cited_by_count":6,"area":null},{"id":"2206.07908","title":"Simultaneously Learning Stochastic and Adversarial Bandits with General Graph Feedback","created":"2022-06-16","cited_by_count":6,"area":null},{"id":"2406.01793","title":"Towards the Transferability of Rewards Recovered via Regularized Inverse Reinforcement Learning","created":"2024-06-03","cited_by_count":6,"area":"offlinerl"},{"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":"2106.04207","title":"Cooperative Stochastic Multi-agent Multi-armed Bandits Robust to Adversarial Corruptions","created":"2021-06-08","cited_by_count":5,"area":null},{"id":"2202.06694","title":"Versatile Dueling Bandits: Best-of-both-World Analyses for Online Learning from Preferences","created":"2022-02-14","cited_by_count":5,"area":null},{"id":"2202.11219","title":"No-Regret Learning with Unbounded Losses: The Case of Logarithmic Pooling","created":"2022-02-22","cited_by_count":5,"area":null},{"id":"2407.12163","title":"Bellman Diffusion Models","created":"2024-07-16","cited_by_count":5,"area":"diffusion"},{"id":"2410.00690","title":"Beyond Minimax Rates in Group Distributionally Robust Optimization via a Novel Notion of Sparsity","created":"2024-10-01","cited_by_count":5,"area":null},{"id":"2602.23116","title":"Provably Efficient Regularized Online RLHF with Generalized Bilinear Preferences","created":"2026-02-26","cited_by_count":5,"area":"alignment"},{"id":"2608.26288","title":"Muon with Finite Newton-Schulz: The Smoothing Benefit in Nonsmooth Nonconvex Optimization","created":"2026-08-26","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":"2206.07528","title":"Density-Based Algorithms for Corruption-Robust Contextual Search and Convex Optimization","created":"2022-06-15","cited_by_count":4,"area":null},{"id":"2301.12755","title":"Efficient Node Selection in Private Personalized Decentralized Learning","created":"2023-01-30","cited_by_count":4,"area":null},{"id":"2302.02333","title":"Learning in quantum games","created":"2023-02-05","cited_by_count":4,"area":null},{"id":"2311.02407","title":"The equivalence of dynamic and strategic stability under regularized learning in games","created":"2023-11-04","cited_by_count":4,"area":null},{"id":"2402.10282","title":"Information Capacity Regret Bounds for Bandits with Mediator Feedback","created":"2024-02-15","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":"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":"2605.09363","title":"Near-Optimal Last-Iterate Convergence for Zero-Sum Games with Bandit Feedback and Opponent Actions","created":"2026-05-10","cited_by_count":4,"area":"alignment"},{"id":"2211.17154","title":"On Regret-optimal Cooperative Nonstochastic Multi-armed Bandits","created":"2022-11-30","cited_by_count":3,"area":null},{"id":"2310.01756","title":"Improved Algorithms for Adversarial Bandits with Unbounded Losses","created":"2023-10-03","cited_by_count":3,"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":"2405.15911","title":"Learning accurate and interpretable tree-based models","created":"2024-05-24","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":"2602.07472","title":"Bandit Allocational Instability","created":"2026-02-07","cited_by_count":3,"area":null},{"id":"2205.15921","title":"Online Meta-Learning in Adversarial Multi-Armed Bandits","created":"2022-05-31","cited_by_count":2,"area":null},{"id":"2304.14989","title":"Kullback-Leibler Maillard Sampling for Multi-armed Bandits with Bounded Rewards","created":"2023-04-28","cited_by_count":2,"area":null},{"id":"2309.16563","title":"CRIMED: Lower and Upper Bounds on Regret for Bandits with Unbounded Stochastic Corruption","created":"2023-09-28","cited_by_count":2,"area":null},{"id":"2311.02423","title":"Payoff-based learning with matrix multiplicative weights in quantum games","created":"2023-11-04","cited_by_count":2,"area":null},{"id":"2312.08008","title":"Learning in Zero-Sum Markov Games: Relaxing Strong Reachability and Mixing Time Assumptions","created":"2023-12-13","cited_by_count":2,"area":null},{"id":"2312.16489","title":"Best-of-Both-Worlds Linear Contextual Bandits","created":"2023-12-27","cited_by_count":2,"area":null},{"id":"2401.03756","title":"Adaptive Experimental Design for Policy Learning","created":"2024-01-08","cited_by_count":2,"area":null},{"id":"2503.00509","title":"Functional multi-armed bandit and the best function identification problems","created":"2025-03-01","cited_by_count":2,"area":null},{"id":"2505.23124","title":"Learning to Incentivize in Repeated Principal-Agent Problems with Adversarial Agent Arrivals","created":"2025-05-29","cited_by_count":2,"area":null},{"id":"2506.01722","title":"When Lower-Order Terms Dominate: Adaptive Expert Algorithms for Heavy-Tailed Losses","created":"2025-06-02","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":"2510.17103","title":"Adapting to Stochastic and Adversarial Losses in Episodic MDPs with Aggregate Bandit Feedback","created":"2025-10-20","cited_by_count":2,"area":null},{"id":"2512.20368","title":"Avoiding the Price of Adaptivity: Inference in Linear Contextual Bandits via Stability","created":"2025-12-23","cited_by_count":2,"area":null},{"id":"2208.11747","title":"Entropy Regularization for Population Estimation","created":"2022-08-24","cited_by_count":1,"area":null},{"id":"2303.05018","title":"Improved Regret Bounds for Online Kernel Selection under Bandit Feedback","created":"2023-03-09","cited_by_count":1,"area":null},{"id":"2408.08859","title":"Stochastic Bandits Robust to Adversarial Attacks","created":"2024-08-16","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":"2509.08765","title":"One-shot acceleration of transient PDE solvers via online-learned preconditioners","created":"2025-09-10","cited_by_count":1,"area":null},{"id":"2510.12152","title":"Follow-the-Perturbed-Leader for Decoupled Bandits: Best-of-Both-Worlds and Practicality","created":"2025-10-14","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":"2511.22283","title":"The Hidden Cost of Approximation in Online Mirror Descent","created":"2025-11-27","cited_by_count":1,"area":null},{"id":"2602.01295","title":"Best-of-Both-Worlds for Heavy-Tailed Markov Decision Processes","created":"2026-02-01","cited_by_count":1,"area":null},{"id":"2602.07418","title":"Achieving Optimal Static and Dynamic Regret Simultaneously in Bandits with Deterministic Losses","created":"2026-02-07","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":"2608.01223","title":"Perspectives on Tsallis Statistics for Artificial Intelligence","created":"2026-08-02","cited_by_count":1,"area":"deeprl"},{"id":"2201.01973","title":"Robust Linear Predictions: Analyses of Uniform Concentration, Fast Rates and Model Misspecification","created":"2022-01-06","cited_by_count":0,"area":null},{"id":"2301.11802","title":"Decentralized Online Bandit Optimization on Directed Graphs with Regret Bounds","created":"2023-01-27","cited_by_count":0,"area":null},{"id":"2304.00999","title":"Bandits for Sponsored Search Auctions under Unknown Valuation Model: Case Study in E-Commerce Advertising","created":"2023-03-31","cited_by_count":0,"area":null}],"next_cursor":"100"}