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Multi-Armed Bandits

Multi-armed bandits refer to a task where a fixed amount of resources must be allocated between competing resources that maximizes expected gain. Typically these problems involve an exploration/exploitation trade-off.

( Image credit: Microsoft Research )

Papers

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Showing 701–750 of 1262 papers

TitleStatusHype
High-dimensional Nonparametric Contextual Bandit Problem—0
High Probability Bound for Cross-Learning Contextual Bandits with Unknown Context Distributions—0
Encrypted Linear Contextual Bandit—0
Honor Among Bandits: No-Regret Learning for Online Fair Division—0
Horde of Bandits using Gaussian Markov Random Fields—0
How Does Variance Shape the Regret in Contextual Bandits?—0
Human-AI Learning Performance in Multi-Armed Bandits—0
Hypothesis Transfer in Bandits by Weighted Models—0
Identifiable latent bandits: Combining observational data and exploration for personalized healthcare—0
Imitation-Regularized Offline Learning—0
Imprecise Multi-Armed Bandits—0
Improved Algorithms for Adversarial Bandits with Unbounded Losses—0
Improved Algorithms for Misspecified Linear Markov Decision Processes—0
Improved Algorithms for Multi-period Multi-class Packing Problems with Bandit Feedback—0
Improved Best-of-Both-Worlds Guarantees for Multi-Armed Bandits: FTRL with General Regularizers and Multiple Optimal Arms—0
Improved High-Probability Regret for Adversarial Bandits with Time-Varying Feedback Graphs—0
Improved Offline Contextual Bandits with Second-Order Bounds: Betting and Freezing—0
A Tractable Online Learning Algorithm for the Multinomial Logit Contextual Bandit—0
Improved Regret Bounds for Linear Bandits with Heavy-Tailed Rewards—0
Improved Regret Bounds for Oracle-Based Adversarial Contextual Bandits—0
Improving Fairness in Adaptive Social Exergames via Shapley Bandits—0
Improving Offline Contextual Bandits with Distributional Robustness—0
Improving Reward-Conditioned Policies for Multi-Armed Bandits using Normalized Weight Functions—0
Improving Thompson Sampling via Information Relaxation for Budgeted Multi-armed Bandits—0
Incentivising Exploration and Recommendations for Contextual Bandits with Payments—0
Incentivized Exploration for Multi-Armed Bandits under Reward Drift—0
Incentivized Exploration via Filtered Posterior Sampling—0
Increasing Students' Engagement to Reminder Emails Through Multi-Armed Bandits—0
Indexability and Rollout Policy for Multi-State Partially Observable Restless Bandits—0
Indexed Minimum Empirical Divergence-Based Algorithms for Linear Bandits—0
Individual Regret in Cooperative Nonstochastic Multi-Armed Bandits—0
Individual Regret in Cooperative Stochastic Multi-Armed Bandits—0
In-Domain African Languages Translation Using LLMs and Multi-armed Bandits—0
Inference for Batched Bandits—0
Instance-Dependent Complexity of Contextual Bandits and Reinforcement Learning: A Disagreement-Based Perspective—0
Instance-optimal PAC Algorithms for Contextual Bandits—0
Concentrated Differential Privacy for Bandits—0
Investigating Gender Fairness in Machine Learning-driven Personalized Care for Chronic Pain—0
Is Offline Decision Making Possible with Only Few Samples? Reliable Decisions in Data-Starved Bandits via Trust Region Enhancement—0
Is Prior-Free Black-Box Non-Stationary Reinforcement Learning Feasible?—0
Is Reinforcement Learning More Difficult Than Bandits? A Near-optimal Algorithm Escaping the Curse of Horizon—0
Joint Representation Training in Sequential Tasks with Shared Structure—0
Kernel-based Multi-Task Contextual Bandits in Cellular Network Configuration—0
Kernel ε-Greedy for Multi-Armed Bandits with Covariates—0
Kernel Methods for Cooperative Multi-Agent Contextual Bandits—0
KL-regularization Itself is Differentially Private in Bandits and RLHF—0
Knowledge Infused Policy Gradients with Upper Confidence Bound for Relational Bandits—0
Lagrangian Index Policy for Restless Bandits with Average Reward—0
Langevin Thompson Sampling with Logarithmic Communication: Bandits and Reinforcement Learning—0
Latent Contextual Bandits and their Application to Personalized Recommendations for New Users—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1NeuralLinear FullPosterior-MRCumulative regret1.92—Unverified
2Linear FullPosterior-MRCumulative regret1.82—Unverified