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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 926–950 of 1262 papers

TitleStatusHype
Inference for Batched Bandits—0
Selfish Robustness and Equilibria in Multi-Player Bandits—0
The Price of Incentivizing Exploration: A Characterization via Thompson Sampling and Sample Complexity—0
Safe Exploration for Optimizing Contextual BanditsCode0
A Closer Look at Small-loss Bounds for Bandits with Graph Feedback—0
Efficient and Robust Algorithms for Adversarial Linear Contextual Bandits—0
Bandits with Knapsacks beyond the Worst-Case—0
Ballooning Multi-Armed Bandits—0
Incentivising Exploration and Recommendations for Contextual Bandits with Payments—0
Exploration Through Bias: Revisiting Biased Maximum Likelihood Estimation in Stochastic Multi-Armed Bandits—0
Gradient-free Online Learning in Continuous Games with Delayed Rewards—0
Distributionally Robust Policy Evaluation and Learning in Offline Contextual Bandits—0
A Modern Introduction to Online LearningCode1
Fair Contextual Multi-Armed Bandits: Theory and Experiments—0
Sublinear Optimal Policy Value Estimation in Contextual Bandits—0
Surrogate Objectives for Batch Policy Optimization in One-step Decision Making—0
Offline Contextual Bandits with High Probability Fairness GuaranteesCode0
Learning in Generalized Linear Contextual Bandits with Stochastic Delays—0
Nonparametric Contextual Bandits in Metric Spaces with Unknown Metric—0
Epsilon-Best-Arm Identification in Pay-Per-Reward Multi-Armed Bandits—0
Thompson Sampling for Multinomial Logit Contextual BanditsCode0
Contextual Combinatorial Conservative Bandits—0
Automatic Ensemble Learning for Online Influence Maximization—0
Corruption-robust exploration in episodic reinforcement learning—0
Contextual Bandits Evolving Over Finite Time—0
Show:102550
← PrevPage 38 of 51Next →

Benchmark Results

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