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 1051–1075 of 1262 papers

TitleStatusHype
Towards Fundamental Limits of Multi-armed Bandits with Random Walk Feedback—0
Rarely-switching linear bandits: optimization of causal effects for the real world—0
Rate-Constrained Remote Contextual Bandits—0
Reciprocal Learning—0
Recommenadation aided Caching using Combinatorial Multi-armed Bandits—0
Budgeted Multi-Armed Bandits with Asymmetric Confidence IntervalsCode0
Cascading Bandits for Large-Scale Recommendation ProblemsCode0
Incorporating Multi-armed Bandit with Local Search for MaxSATCode0
VITS : Variational Inference Thompson Sampling for contextual banditsCode0
Causal Contextual Bandits with Adaptive ContextCode0
Efficient Explorative Key-term Selection Strategies for Conversational Contextual BanditsCode0
Adaptive Action Duration with Contextual Bandits for Deep Reinforcement Learning in Dynamic EnvironmentsCode0
Causally Abstracted Multi-armed BanditsCode0
Censored Semi-Bandits: A Framework for Resource Allocation with Censored FeedbackCode0
Online Learning for Function Placement in Serverless ComputingCode0
Safe and Adaptive Decision-Making for Optimization of Safety-Critical Systems: The ARTEO AlgorithmCode0
Optimal Contextual Bandits with Knapsacks under Realizability via Regression OraclesCode0
Efficient Kernel UCB for Contextual BanditsCode0
Multi-armed bandits for resource efficient, online optimization of language model pre-training: the use case of dynamic maskingCode0
Multi-Armed Bandits in Brain-Computer InterfacesCode0
Off-Policy Evaluation of Slate Bandit Policies via Optimizing AbstractionCode0
Off-Policy Evaluation Using Information Borrowing and Context-Based SwitchingCode0
Infinite Action Contextual Bandits with Reusable Data ExhaustCode0
Combinatorial Bandits under Strategic ManipulationsCode0
Adapting multi-armed bandits policies to contextual bandits scenariosCode0
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Benchmark Results

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