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 101–125 of 1262 papers

TitleStatusHype
Model selection for behavioral learning data and applications to contextual bandits—0
Near-Optimal Private Learning in Linear Contextual Bandits—0
Contextual Linear Bandits with Delay as Payoff—0
Improved Offline Contextual Bandits with Second-Order Bounds: Betting and Freezing—0
Contextual bandits with entropy-based human feedbackCode0
Provably Efficient RLHF Pipeline: A Unified View from Contextual Bandits—0
Heterogeneous Multi-agent Multi-armed Bandits on Stochastic Block Models—0
Quantile Multi-Armed Bandits with 1-bit Feedback—0
Towards a Sharp Analysis of Offline Policy Learning for f-Divergence-Regularized Contextual Bandits—0
Nearly Tight Bounds for Cross-Learning Contextual Bandits with Graphical Feedback—0
From Restless to Contextual: A Thresholding Bandit Approach to Improve Finite-horizon PerformanceCode0
Early Stopping in Contextual Bandits and Inferences—0
Catoni Contextual Bandits are Robust to Heavy-tailed Rewards—0
Optimizing Online Advertising with Multi-Armed Bandits: Mitigating the Cold Start Problem under Auction Dynamics—0
Nearly Tight Bounds for Exploration in Streaming Multi-armed Bandits with Known Optimality Gap—0
Meta-Prompt Optimization for LLM-Based Sequential Decision Making—0
Multi-agent Multi-armed Bandit with Fully Heavy-tailed Dynamics—0
Offline Learning for Combinatorial Multi-armed Bandits—0
Solving Inverse Problem for Multi-armed Bandits via Convex OptimizationCode0
Nearly-Optimal Bandit Learning in Stackelberg Games with Side Information—0
Contextual Online Decision Making with Infinite-Dimensional Functional Regression—0
Breaking the (1/Δ_2) Barrier: Better Batched Best Arm Identification with Adaptive Grids—0
Sequential Learning of the Pareto Front for Multi-objective BanditsCode0
HD-CB: The First Exploration of Hyperdimensional Computing for Contextual Bandits Problems—0
Restless Multi-armed Bandits under Frequency and Window Constraints for Public Service Inspections—0
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Benchmark Results

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