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 801–810 of 1262 papers

TitleStatusHype
Meta-Learning surrogate models for sequential decision making—0
Meta-Prompt Optimization for LLM-Based Sequential Decision Making—0
Meta-Reasoner: Dynamic Guidance for Optimized Inference-time Reasoning in Large Language Models—0
Meta-Thompson Sampling—0
Metric-Free Individual Fairness with Cooperative Contextual Bandits—0
Minimax Off-Policy Evaluation for Multi-Armed Bandits—0
Minimax-optimal trust-aware multi-armed bandits—0
Minimax Policy for Heavy-tailed Bandits—0
Mitigating Bias in Adaptive Data Gathering via Differential Privacy—0
Modeling Attrition in Recommender Systems with Departing Bandits—0
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Benchmark Results

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