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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 826–850 of 1262 papers

TitleStatusHype
Online Semi-Supervised Learning in Contextual Bandits with Episodic RewardCode0
Thompson Sampling for Unsupervised Sequential Selection—0
Partial Bandit and Semi-Bandit: Making the Most Out of Scarce Users' Feedback—0
Deep Contextual Bandits for Fast Initial Access in mmWave Based User-Centric Ultra-Dense Networks—0
Dual-Mandate Patrols: Multi-Armed Bandits for Green SecurityCode0
Carousel Personalization in Music Streaming Apps with Contextual BanditsCode1
VacSIM: Learning Effective Strategies for COVID-19 Vaccine Distribution using Reinforcement LearningCode0
Unifying Clustered and Non-stationary Bandits—0
Statistically Robust, Risk-Averse Best Arm Identification in Multi-Armed Bandits—0
Dynamic Batch Learning in High-Dimensional Sparse Linear Contextual Bandits—0
A Sleeping, Recovering Bandit Algorithm for Optimizing Recurring Notifications—0
Contextual Bandits for Advertising Budget Allocation—0
Offline Contextual Multi-armed Bandits for Mobile Health Interventions: A Case Study on Emotion Regulation—0
Using Subjective Logic to Estimate Uncertainty in Multi-Armed Bandit ProblemsCode0
Kernel Methods for Cooperative Multi-Agent Contextual Bandits—0
Lenient Regret for Multi-Armed Bandits—0
A framework for optimizing COVID-19 testing policy using a Multi Armed Bandit approach—0
Greedy Bandits with Sampled Context—0
Multi-Armed Bandits for Minesweeper: Profiting from Exploration-Exploitation Synergy—0
Minimax Policy for Heavy-tailed Bandits—0
Competing Bandits: The Perils of Exploration Under Competition—0
Self-Tuning Bandits over Unknown Covariate-Shifts—0
Upper Counterfactual Confidence Bounds: a New Optimism Principle for Contextual Bandits—0
Optimal Learning for Structured BanditsCode0
Quantum exploration algorithms for multi-armed banditsCode0
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Benchmark Results

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