SOTAVerified

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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 726–750 of 1262 papers

TitleStatusHype
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