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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 776–800 of 1262 papers

TitleStatusHype
Personalization Paradox in Behavior Change Apps: Lessons from a Social Comparison-Based Personalized App for Physical Activity—0
Online and Scalable Model Selection with Multi-Armed Bandits—0
Efficient Pure Exploration for Combinatorial Bandits with Semi-Bandit Feedback—0
Minimax Off-Policy Evaluation for Multi-Armed Bandits—0
Resource Allocation in NOMA-based Self-Organizing Networks using Stochastic Multi-Armed Bandits—0
Survival of the strictest: Stable and unstable equilibria under regularized learning with partial information—0
Be Greedy in Multi-Armed Bandits—0
Online Learning under Adversarial Corruptions—0
Online Limited Memory Neural-Linear Bandits—0
Combinatorial Pure Exploration with Full-bandit Feedback and Beyond: Solving Combinatorial Optimization under Uncertainty with Limited Observation—0
Learning to Optimize Energy Efficiency in Energy Harvesting Wireless Sensor Networks—0
Lifelong Learning in Multi-Armed Bandits—0
A Regret bound for Non-stationary Multi-Armed Bandits with Fairness Constraints—0
Expanding on Repeated Consumer Search Using Multi-Armed Bandits and Secretaries—0
Relational Boosted BanditsCode0
A One-Size-Fits-All Solution to Conservative Bandit Problems—0
Active Feature Selection for the Mutual Information CriterionCode0
Adversarial Linear Contextual Bandits with Graph-Structured Side Observations—0
Streaming Algorithms for Stochastic Multi-armed Bandits—0
Efficient Automatic CASH via Rising Bandits—0
Accurate and Fast Federated Learning via Combinatorial Multi-Armed Bandits—0
Neural Contextual Bandits with Deep Representation and Shallow Exploration—0
Distributed Thompson Sampling—0
Batched Coarse Ranking in Multi-Armed Bandits—0
Unreasonable Effectiveness of Greedy Algorithms in Multi-Armed Bandit with Many ArmsCode0
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Benchmark Results

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