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 1141–1150 of 1262 papers

TitleStatusHype
Federated Multi-armed Bandits with PersonalizationCode0
Federated Neural BanditsCode0
Online Learning in Iterated Prisoner's Dilemma to Mimic Human BehaviorCode0
Thompson Sampling for Bandit Learning in Matching MarketsCode0
Variational inference for the multi-armed contextual banditCode0
AC-Band: A Combinatorial Bandit-Based Approach to Algorithm ConfigurationCode0
PageRank Bandits for Link PredictionCode0
Stochastic Rising BanditsCode0
Contextual Linear Bandits under Noisy Features: Towards Bayesian OraclesCode0
Variance-Aware Linear UCB with Deep Representation for Neural Contextual BanditsCode0
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Benchmark Results

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