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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 901–925 of 1262 papers

TitleStatusHype
Self-Supervised Contextual Bandits in Computer Vision—0
Learning and Fairness in Energy Harvesting: A Maximin Multi-Armed Bandits Approach—0
Delay-Adaptive Learning in Generalized Linear Contextual Bandits—0
Convex Hull Monte-Carlo Tree Search—0
Online Residential Demand Response via Contextual Multi-Armed Bandits—0
A Farewell to Arms: Sequential Reward Maximization on a Budget with a Giving Up Option—0
Stochastic Linear Contextual Bandits with Diverse Contexts—0
Robustness Guarantees for Mode Estimation with an Application to Bandits—0
Generalized Policy Elimination: an efficient algorithm for Nonparametric Contextual Bandits—0
Taking a hint: How to leverage loss predictors in contextual bandits?—0
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
Residual Bootstrap Exploration for Bandit Algorithms—0
On conditional versus marginal bias in multi-armed bandits—0
Adaptive Estimator Selection for Off-Policy EvaluationCode0
Coordination without communication: optimal regret in two players multi-armed bandits—0
Tight Lower Bounds for Combinatorial Multi-Armed Bandits—0
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Benchmark Results

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