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 911–920 of 1262 papers

TitleStatusHype
Model Selection in Contextual Stochastic Bandit Problems—0
Bounded Regret for Finitely Parameterized Multi-Armed Bandits—0
Distributed Cooperative Decision Making in Multi-agent Multi-armed Bandits—0
Decentralized Multi-player Multi-armed Bandits with No Collision Information—0
Designing Truthful Contextual Multi-Armed Bandits based Sponsored Search Auctions—0
Structured Linear Contextual Bandits: A Sharp and Geometric Smoothed Analysis—0
Bandit Learning with Delayed Impact of Actions—0
The Unreasonable Effectiveness of Greedy Algorithms in Multi-Armed Bandit with Many ArmsCode0
Survey Bandits with Regret Guarantees—0
Online Learning in Contextual Bandits using Gated Linear Networks—0
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Benchmark Results

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