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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 451–475 of 1262 papers

TitleStatusHype
Genetic multi-armed bandits: a reinforcement learning approach for discrete optimization via simulation—0
Adversarial Rewards in Universal Learning for Contextual Bandits—0
Piecewise-Stationary Multi-Objective Multi-Armed Bandit with Application to Joint Communications and SensingCode0
Leveraging User-Triggered Supervision in Contextual Bandits—0
On Private and Robust Bandits—0
Multiplier Bootstrap-based Exploration—0
Randomized Greedy Learning for Non-monotone Stochastic Submodular Maximization Under Full-bandit Feedback—0
Stochastic Contextual Bandits with Long Horizon Rewards—0
Improved Algorithms for Multi-period Multi-class Packing Problems with Bandit Feedback—0
Quantum contextual bandits and recommender systems for quantum data—0
Adversarial Attacks on Adversarial Bandits—0
A Framework for Adapting Offline Algorithms to Solve Combinatorial Multi-Armed Bandit Problems with Bandit Feedback—0
Contextual Causal Bayesian Optimisation—0
Communication-Efficient Collaborative Regret Minimization in Multi-Armed Bandits—0
Banker Online Mirror Descent: A Universal Approach for Delayed Online Bandit Learning—0
Quantum Heavy-tailed Bandits—0
Multi-Armed Bandits and Quantum Channel Oracles—0
Multi-armed Bandit Learning for TDMA Transmission Slot Scheduling and Defragmentation for Improved Bandwidth Usage—0
Best Arm Identification in Stochastic Bandits: Beyond β-optimality—0
Local Differential Privacy for Sequential Decision Making in a Changing Environment—0
Contextual Bandits and Optimistically Universal Learning—0
Online Statistical Inference for Contextual Bandits via Stochastic Gradient Descent—0
On the Complexity of Representation Learning in Contextual Linear Bandits—0
MABSplit: Faster Forest Training Using Multi-Armed BanditsCode0
Faster Maximum Inner Product Search in High Dimensions—0
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Benchmark Results

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