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 801–825 of 1262 papers

TitleStatusHype
Queue Scheduling with Adversarial Bandit Learning—0
Quick-Draw Bandits: Quickly Optimizing in Nonstationary Environments with Extremely Many Arms—0
Raising Student Completion Rates with Adaptive Curriculum and Contextual Bandits—0
Random Effect Bandits—0
Randomized Allocation with Nonparametric Estimation for Contextual Multi-Armed Bandits with Delayed Rewards—0
Randomized Greedy Learning for Non-monotone Stochastic Submodular Maximization Under Full-bandit Feedback—0
Towards Fundamental Limits of Multi-armed Bandits with Random Walk Feedback—0
Rarely-switching linear bandits: optimization of causal effects for the real world—0
Rate-Constrained Remote Contextual Bandits—0
Reciprocal Learning—0
Recommenadation aided Caching using Combinatorial Multi-armed Bandits—0
Recurrent Submodular Welfare and Matroid Blocking Bandits—0
Recurrent Submodular Welfare and Matroid Blocking Semi-Bandits—0
Reducing Dueling Bandits to Cardinal Bandits—0
Regional Multi-Armed Bandits—0
Regression Oracles and Exploration Strategies for Short-Horizon Multi-Armed Bandits—0
Regret Analysis of Learning-Based MPC with Partially-Unknown Cost Function—0
Regret Analysis of the Finite-Horizon Gittins Index Strategy for Multi-Armed Bandits—0
Regret Bounds and Reinforcement Learning Exploration of EXP-based Algorithms—0
Stochastic Top-K Subset Bandits with Linear Space and Non-Linear Feedback—0
Regret Minimisation in Multi-Armed Bandits Using Bounded Arm Memory—0
Regret vs. Communication: Distributed Stochastic Multi-Armed Bandits and Beyond—0
Regularized Contextual Bandits—0
Regularized-OFU: an efficient algorithm for general contextual bandit with optimization oracles—0
Regularized OFU: an Efficient UCB Estimator forNon-linear Contextual Bandit—0
Show:102550
← PrevPage 33 of 51Next →

Benchmark Results

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