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

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Showing 576–600 of 1262 papers

TitleStatusHype
Contextual Linear Bandits with Delay as Payoff—0
Contextual Information-Directed Sampling—0
Bandit Regret Scaling with the Effective Loss Range—0
A Hybrid Meta-Learning and Multi-Armed Bandit Approach for Context-Specific Multi-Objective Recommendation Optimization—0
Adaptive Data Augmentation for Thompson Sampling—0
A conversion theorem and minimax optimality for continuum contextual bandits—0
Contextual Combinatorial Multi-armed Bandits with Volatile Arms and Submodular Reward—0
BanditRank: Learning to Rank Using Contextual Bandits—0
Contextual Combinatorial Conservative Bandits—0
Contextual Causal Bayesian Optimisation—0
BanditQ: Fair Bandits with Guaranteed Rewards—0
A Hierarchical Nearest Neighbour Approach to Contextual Bandits—0
Contextual Bandit with Herding Effects: Algorithms and Recommendation Applications—0
Individual Regret in Cooperative Stochastic Multi-Armed Bandits—0
Individual Regret in Cooperative Nonstochastic Multi-Armed Bandits—0
Contextual bandits with surrogate losses: Margin bounds and efficient algorithms—0
Indexed Minimum Empirical Divergence-Based Algorithms for Linear Bandits—0
Indexability and Rollout Policy for Multi-State Partially Observable Restless Bandits—0
Increasing Students' Engagement to Reminder Emails Through Multi-Armed Bandits—0
Contextual Bandits with Stage-wise Constraints—0
A General Theory of the Stochastic Linear Bandit and Its Applications—0
In-Domain African Languages Translation Using LLMs and Multi-armed Bandits—0
Inference for Batched Bandits—0
Adaptive Contract Design for Crowdsourcing Markets: Bandit Algorithms for Repeated Principal-Agent Problems—0
Contextual Bandits with Sparse Data in Web setting—0
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Benchmark Results

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