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 1176–1200 of 1262 papers

TitleStatusHype
Risk-Aware Algorithms for Adversarial Contextual Bandits—0
Exploration Potential—0
On Sequential Elimination Algorithms for Best-Arm Identification in Multi-Armed Bandits—0
On the Identification and Mitigation of Weaknesses in the Knowledge Gradient Policy for Multi-Armed Bandits—0
An optimal learning method for developing personalized treatment regimes—0
Making Contextual Decisions with Low Technical Debt—0
Improved Regret Bounds for Oracle-Based Adversarial Contextual Bandits—0
Contextual Bandits with Latent Confounders: An NMF Approach—0
Open Problem: Best Arm Identification: Almost Instance-Wise Optimality and the Gap Entropy Conjecture—0
Fairness in Learning: Classic and Contextual Bandits—0
Graph Clustering Bandits for Recommendation—0
Stochastic Contextual Bandits with Known Reward Functions—0
Latent Contextual Bandits and their Application to Personalized Recommendations for New Users—0
Cascading Bandits for Large-Scale Recommendation ProblemsCode0
PAC Reinforcement Learning with Rich Observations—0
BISTRO: An Efficient Relaxation-Based Method for Contextual Bandits—0
Bandits meet Computer Architecture: Designing a Smartly-allocated Cache—0
Personalized Course Sequence Recommendations—0
On Top-k Selection in Multi-Armed Bandits and Hidden Bipartite Graphs—0
Algorithms for Differentially Private Multi-Armed Bandits—0
Regret Analysis of the Finite-Horizon Gittins Index Strategy for Multi-Armed Bandits—0
Context-Aware Bandits—0
A Survey of Online Experiment Design with the Stochastic Multi-Armed BanditCode0
Multi-armed Bandits with Application to 5G Small Cells—0
Sequential Design for Ranking Response Surfaces—0
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Benchmark Results

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