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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 551–600 of 1262 papers

TitleStatusHype
Contextual Bandits for Unbounded Context Distributions—0
Heterogeneous Multi-Player Multi-Armed Bandits Robust To Adversarial Attacks—0
Contextual Bandits in a Survey Experiment on Charitable Giving: Within-Experiment Outcomes versus Policy Learning—0
Full Gradient Deep Reinforcement Learning for Average-Reward Criterion—0
Contextual Bandits in Payment Processing: Non-uniform Exploration and Supervised Learning at Adyen—0
Hierarchical Optimistic Region Selection driven by Curiosity—0
High-dimensional Linear Bandits with Knapsacks—0
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
Contextual Bandits with Continuous Actions: Smoothing, Zooming, and Adapting—0
Adapting to Misspecification in Contextual Bandits with Offline Regression Oracles—0
Instance-Dependent Complexity of Contextual Bandits and Reinforcement Learning: A Disagreement-Based Perspective—0
Identifiable latent bandits: Combining observational data and exploration for personalized healthcare—0
Balancing Act: Prioritization Strategies for LLM-Designed Restless Bandit Rewards—0
Imitation-Regularized Offline Learning—0
The Choice of Noninformative Priors for Thompson Sampling in Multiparameter Bandit Models—0
Survival of the strictest: Stable and unstable equilibria under regularized learning with partial information—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
A Closer Look at Small-loss Bounds for Bandits with Graph Feedback—0
Contextual Bandits with Sparse Data in Web setting—0
Instance-optimal PAC Algorithms for Contextual Bandits—0
Indexability and Rollout Policy for Multi-State Partially Observable Restless Bandits—0
From Dirichlet to Rubin: Optimistic Exploration in RL without Bonuses—0
Indexed Minimum Empirical Divergence-Based Algorithms for Linear Bandits—0
From Bandits to Experts: On the Value of Side-Observations—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
Contextual Causal Bayesian Optimisation—0
Confidence-Budget Matching for Sequential Budgeted Learning—0
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Benchmark Results

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