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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 101–125 of 1262 papers

TitleStatusHype
A Novel Approach to Balance Convenience and Nutrition in Meals With Long-Term Group Recommendations and Reasoning on Multimodal Recipes and its Implementation in BEACON—0
Balans: Multi-Armed Bandits-based Adaptive Large Neighborhood Search for Mixed-Integer Programming ProblemCode1
Lagrangian Index Policy for Restless Bandits with Average Reward—0
MaxInfoRL: Boosting exploration in reinforcement learning through information gain maximization—0
An Optimistic Algorithm for Online Convex Optimization with Adversarial Constraints—0
IRL for Restless Multi-Armed Bandits with Applications in Maternal and Child HealthCode0
UCB algorithms for multi-armed bandits: Precise regret and adaptive inference—0
Conservative Contextual Bandits: Beyond Linear Representations—0
Data Acquisition for Improving Model Fairness using Reinforcement Learning—0
Coordinated Multi-Armed Bandits for Improved Spatial Reuse in Wi-Fi—0
Selective Reviews of Bandit Problems in AI via a Statistical View—0
Contextual Bandits in Payment Processing: Non-uniform Exploration and Supervised Learning at Adyen—0
Achieving PAC Guarantees in Mechanism Design through Multi-Armed Bandits—0
Off-policy estimation with adaptively collected data: the power of online learning—0
A unifying framework for generalised Bayesian online learning in non-stationary environmentsCode1
Multi-Agent Stochastic Bandits Robust to Adversarial Corruptions—0
Individual Regret in Cooperative Stochastic Multi-Armed Bandits—0
Variance-Aware Linear UCB with Deep Representation for Neural Contextual BanditsCode0
Multi-armed Bandits with Missing OutcomeCode0
Structure Matters: Dynamic Policy Gradient—0
Sharp Analysis for KL-Regularized Contextual Bandits and RLHF—0
Rising Rested Bandits: Lower Bounds and Efficient Algorithms—0
Non-Stationary Learning of Neural Networks with Automatic Soft Parameter Reset—0
PageRank Bandits for Link PredictionCode0
MBExplainer: Multilevel bandit-based explanations for downstream models with augmented graph embeddings—0
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Benchmark Results

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