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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 251–300 of 1262 papers

TitleStatusHype
Stealthy Adversarial Attacks on Stochastic Multi-Armed Bandits—0
Incentivized Exploration via Filtered Posterior Sampling—0
Thompson Sampling in Partially Observable Contextual Bandits—0
Efficient Prompt Optimization Through the Lens of Best Arm Identification—0
Diffusion Models Meet Contextual Bandits with Large Action Spaces—0
FLASH: Federated Learning Across Simultaneous Heterogeneities—0
Thresholding Data Shapley for Data Cleansing Using Multi-Armed Bandits—0
Stochastic contextual bandits with graph feedback: from independence number to MAS number—0
Contextual Multinomial Logit Bandits with General Value Functions—0
Efficient Contextual Bandits with Uninformed Feedback Graphs—0
Replicability is Asymptotically Free in Multi-armed Bandits—0
More Benefits of Being Distributional: Second-Order Bounds for Reinforcement Learning—0
Fast UCB-type algorithms for stochastic bandits with heavy and super heavy symmetric noise—0
Tree Ensembles for Contextual Bandits—0
Fairness of Exposure in Online Restless Multi-armed BanditsCode0
Simultaneously Achieving Group Exposure Fairness and Within-Group Meritocracy in Stochastic BanditsCode0
Context in Public Health for Underserved Communities: A Bayesian Approach to Online Restless Bandits—0
Fairness and Privacy Guarantees in Federated Contextual Bandits—0
Off-Policy Evaluation of Slate Bandit Policies via Optimizing AbstractionCode0
Multi-Armed Bandits with Interference—0
Query-Efficient Correlation Clustering with Noisy Oracle—0
Falcon: Fair Active Learning using Multi-armed BanditsCode0
Distributionally Robust Policy Evaluation under General Covariate Shift in Contextual BanditsCode0
Distributed Multi-Task Learning for Stochastic Bandits with Context Distribution and Stage-wise Constraints—0
Adaptive Regret for Bandits Made Possible: Two Queries Suffice—0
On Quantum Natural Policy Gradients—0
Contextual Bandits with Stage-wise Constraints—0
Let's Get It Started: Fostering the Discoverability of New Releases on DeezerCode0
Reliability-Optimized User Admission Control for URLLC Traffic: A Neural Contextual Bandit Approach—0
Optimal cross-learning for contextual bandits with unknown context distributions—0
Foundations of Reinforcement Learning and Interactive Decision Making—0
Best-of-Both-Worlds Linear Contextual Bandits—0
Harnessing the Power of Federated Learning in Federated Contextual BanditsCode0
Diversity-Based Recruitment in Crowdsensing By Combinatorial Multi-Armed Bandits—0
Zero-Inflated Bandits—0
Best-of-Both-Worlds Algorithms for Linear Contextual Bandits—0
Neural Contextual Bandits for Personalized Recommendation—0
In-Context Reinforcement Learning for Variable Action SpacesCode1
Bayesian Analysis of Combinatorial Gaussian Process Bandits—0
Distribution-Dependent Rates for Multi-Distribution Learning—0
Observation-Augmented Contextual Multi-Armed Bandits for Robotic Search and Exploration—0
Best Arm Identification with Fixed Budget: A Large Deviation PerspectiveCode0
Online Restless Multi-Armed Bandits with Long-Term Fairness Constraints—0
Risk-Aware Continuous Control with Neural Contextual BanditsCode0
A Hierarchical Nearest Neighbour Approach to Contextual Bandits—0
Robust and Performance Incentivizing Algorithms for Multi-Armed Bandits with Strategic Agents—0
Contextual Bandits with Online Neural Regression—0
RoME: A Robust Mixed-Effects Bandit Algorithm for Optimizing Mobile Health InterventionsCode0
Distributed Optimization via Kernelized Multi-armed Bandits—0
Marginal Density Ratio for Off-Policy Evaluation in Contextual BanditsCode0
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Benchmark Results

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