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 301–325 of 1262 papers

TitleStatusHype
Thompson sampling for zero-inflated count outcomes with an application to the Drink Less mobile health study—0
When is Off-Policy Evaluation (Reward Modeling) Useful in Contextual Bandits? A Data-Centric PerspectiveCode0
Provably Efficient High-Dimensional Bandit Learning with Batched Feedbacks—0
An Experimental Design for Anytime-Valid Causal Inference on Multi-Armed BanditsCode0
LLMs-augmented Contextual Bandit—0
Adversarial Attacks on Cooperative Multi-agent Bandits—0
Efficient Generalized Low-Rank Tensor Contextual Bandits—0
High-dimensional Linear Bandits with Knapsacks—0
Federated Linear Bandits with Finite Adversarial Actions—0
An Improved Relaxation for Oracle-Efficient Adversarial Contextual Bandits—0
Near-Optimal Pure Exploration in Matrix Games: A Generalization of Stochastic Bandits & Dueling BanditsCode0
A Risk-Averse Framework for Non-Stationary Stochastic Multi-Armed Bandits—0
Contextual Bandits for Evaluating and Improving Inventory Control Policies—0
Off-Policy Evaluation for Large Action Spaces via Policy Convolution—0
Towards a Pretrained Model for Restless Bandits via Multi-arm Generalization—0
α-Fair Contextual Bandits—0
Pure Exploration in Asynchronous Federated Bandits—0
Leveraging heterogeneous spillover in maximizing contextual bandit rewards—0
Bad Values but Good Behavior: Learning Highly Misspecified Bandits and MDPs—0
Non-Stationary Contextual Bandit Learning via Neural Predictive Ensemble Sampling—0
Byzantine-Resilient Decentralized Multi-Armed Bandits—0
Ensemble Active Learning by Contextual Bandits for AI Incubation in Manufacturing—0
Adversarial Attacks on Combinatorial Multi-Armed BanditsCode0
Improved Algorithms for Adversarial Bandits with Unbounded Losses—0
Finite-Time Analysis of Whittle Index based Q-Learning for Restless Multi-Armed Bandits with Neural Network Function Approximation—0
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Benchmark Results

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