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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 201–225 of 1262 papers

TitleStatusHype
Boundary Crossing Probabilities for General Exponential Families—0
Bounded Regret for Finitely Parameterized Multi-Armed Bandits—0
Breaking the (1/Δ_2) Barrier: Better Batched Best Arm Identification with Adaptive Grids—0
Breaking the T Barrier: Instance-Independent Logarithmic Regret in Stochastic Contextual Linear Bandits—0
Bridging Offline Reinforcement Learning and Imitation Learning: A Tale of Pessimism—0
Budget-Constrained Multi-Armed Bandits with Multiple Plays—0
Budgeted Combinatorial Multi-Armed Bandits—0
An Optimal Algorithm for Adversarial Bandits with Arbitrary Delays—0
Budgeted Recommendation with Delayed Feedback—0
Building Bridges: Viewing Active Learning from the Multi-Armed Bandit Lens—0
Bypassing the Monster: A Faster and Simpler Optimal Algorithm for Contextual Bandits under Realizability—0
Bypassing the Simulator: Near-Optimal Adversarial Linear Contextual Bandits—0
Byzantine-Resilient Decentralized Multi-Armed Bandits—0
A Gang of Bandits—0
An Optimistic Algorithm for Online Convex Optimization with Adversarial Constraints—0
Catoni Contextual Bandits are Robust to Heavy-tailed Rewards—0
Causal Bandits: Online Decision-Making in Endogenous Settings—0
A General Reduction for High-Probability Analysis with General Light-Tailed Distributions—0
Balanced off-policy evaluation in general action spaces—0
Causal Feature Selection Method for Contextual Multi-Armed Bandits in Recommender System—0
AdaLinUCB: Opportunistic Learning for Contextual Bandits—0
Competing Bandits in Matching Markets—0
Balanced Linear Contextual Bandits—0
Classical Bandit Algorithms for Entanglement Detection in Parameterized Qubit States—0
A framework for optimizing COVID-19 testing policy using a Multi Armed Bandit approach—0
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Benchmark Results

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