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 501–525 of 1262 papers

TitleStatusHype
Finite-Horizon Single-Pull Restless Bandits: An Efficient Index Policy For Scarce Resource Allocation—0
Competing Bandits in Matching Markets—0
Finite-Time Analysis of Kernelised Contextual Bandits—0
Finite-Time Analysis of Whittle Index based Q-Learning for Restless Multi-Armed Bandits with Neural Network Function Approximation—0
Conformal Off-Policy Prediction in Contextual Bandits—0
First- and Second-Order Bounds for Adversarial Linear Contextual Bandits—0
Fixed-Budget Best-Arm Identification in Structured Bandits—0
FLASH: Federated Learning Across Simultaneous Heterogeneities—0
Flexible and Efficient Contextual Bandits with Heterogeneous Treatment Effect Oracles—0
Follow-ups Also Matter: Improving Contextual Bandits via Post-serving Contexts—0
α-Fair Contextual Bandits—0
Generalized Translation and Scale Invariant Online Algorithm for Adversarial Multi-Armed Bandits—0
Full Gradient Deep Reinforcement Learning for Average-Reward Criterion—0
Adapting to Misspecification in Contextual Bandits with Offline Regression Oracles—0
Generalized Policy Elimination: an efficient algorithm for Nonparametric Contextual Bandits—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
A Closer Look at Small-loss Bounds for Bandits with Graph Feedback—0
Fully Gap-Dependent Bounds for Multinomial Logit Bandit—0
Fundamental Limits of Online and Distributed Algorithms for Statistical Learning and Estimation—0
Garbage In, Reward Out: Bootstrapping Exploration in Multi-Armed Bandits—0
Conservative Contextual Bandits: Beyond Linear Representations—0
Gaussian Process bandits with adaptive discretization—0
Generalized Risk-Aversion in Stochastic Multi-Armed Bandits—0
From Dirichlet to Rubin: Optimistic Exploration in RL without Bonuses—0
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Benchmark Results

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