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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 476–500 of 1262 papers

TitleStatusHype
Fast and Sample Efficient Multi-Task Representation Learning in Stochastic Contextual Bandits—0
Active Inference for Autonomous Decision-Making with Contextual Multi-Armed Bandits—0
Faster Maximum Inner Product Search in High Dimensions—0
Accurate and Fast Federated Learning via Combinatorial Multi-Armed Bandits—0
Fast UCB-type algorithms for stochastic bandits with heavy and super heavy symmetric noise—0
Federated Combinatorial Multi-Agent Multi-Armed Bandits—0
Federated Linear Bandits with Finite Adversarial Actions—0
Federated Linear Contextual Bandits—0
Federated Linear Contextual Bandits with Heterogeneous Clients—0
Federated Linear Contextual Bandits with User-level Differential Privacy—0
Deep Contextual Multi-armed Bandits—0
Federated Multi-Armed Bandits Under Byzantine Attacks—0
Deep Contextual Bandits for Fast Neighbor-Aided Initial Access in mmWave Cell-Free Networks—0
Towards Bayesian Data Selection—0
Federated Online Sparse Decision Making—0
Batch Ensemble for Variance Dependent Regret in Stochastic Bandits—0
FedMABA: Towards Fair Federated Learning through Multi-Armed Bandits Allocation—0
Feel-Good Thompson Sampling for Contextual Bandits and Reinforcement Learning—0
Feel-Good Thompson Sampling for Contextual Dueling Bandits—0
Analysis of Thompson Sampling for Partially Observable Contextual Multi-Armed Bandits—0
Fighting Contextual Bandits with Stochastic Smoothing—0
Decision Making in Changing Environments: Robustness, Query-Based Learning, and Differential Privacy—0
Scalable Decision-Focused Learning in Restless Multi-Armed Bandits with Application to Maternal and Child Health—0
Finding the bandit in a graph: Sequential search-and-stop—0
Batched Thompson Sampling for Multi-Armed Bandits—0
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Benchmark Results

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