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 351–360 of 1262 papers

TitleStatusHype
Provable Benefits of Policy Learning from Human Preferences in Contextual Bandit Problems—0
Contextual Bandits and Imitation Learning via Preference-Based Active Queries—0
Preferences Evolve And So Should Your Bandits: Bandits with Evolving States for Online Platforms—0
Decentralized Smart Charging of Large-Scale EVs using Adaptive Multi-Agent Multi-Armed Bandits—0
VITS : Variational Inference Thompson Sampling for contextual banditsCode0
Adaptive Linear Estimating EquationsCode0
On Interpolating Experts and Multi-Armed Bandits—0
Tracking Most Significant Shifts in Nonparametric Contextual Bandits—0
SHAP@k:Efficient and Probably Approximately Correct (PAC) Identification of Top-k Features—0
BOF-UCB: A Bayesian-Optimistic Frequentist Algorithm for Non-Stationary Contextual Bandits—0
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Benchmark Results

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