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

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Showing 51–75 of 1262 papers

TitleStatusHype
Offline Clustering of Linear Bandits: Unlocking the Power of Clusters in Data-Limited Environments—0
Test-Time Scaling of Diffusion Models via Noise Trajectory SearchCode0
KL-regularization Itself is Differentially Private in Bandits and RLHF—0
Scalable and Interpretable Contextual Bandits: A Literature Review and Retail Offer Prototype—0
In-Domain African Languages Translation Using LLMs and Multi-armed Bandits—0
Human in the Loop Adaptive Optimization for Improved Time Series ForecastingCode0
Optimal Best-Arm Identification under Fixed Confidence with Multiple Optima—0
High-dimensional Nonparametric Contextual Bandit Problem—0
Augmenting Online RL with Offline Data is All You Need: A Unified Hybrid RL Algorithm Design and Analysis—0
Multi-Armed Bandits Meet Large Language Models—0
Near Optimal Best Arm Identification for Clustered Bandits—0
Batched Nonparametric Bandits via k-Nearest Neighbor UCB—0
Adaptive, Robust and Scalable Bayesian Filtering for Online Learning—0
Navigating the Rashomon Effect: How Personalization Can Help Adjust Interpretable Machine Learning Models to Individual Users—0
Adaptive Budgeted Multi-Armed Bandits for IoT with Dynamic Resource Constraints—0
Preference-centric Bandits: Optimality of Mixtures and Regret-efficient Algorithms—0
Access Probability Optimization in RACH: A Multi-Armed Bandits Approach—0
Neural Contextual Bandits Under Delayed Feedback Constraints—0
On the Problem of Best Arm Retention—0
Learning-Based User Association for MmWave Vehicular Networks With Kernelized Contextual Bandits—0
Towards More Efficient, Robust, Instance-adaptive, and Generalizable Sequential Decision making—0
A Classification View on Meta Learning Bandits—0
An Exploration-free Method for a Linear Stochastic Bandit Driven by a Linear Gaussian Dynamical System—0
Antithetic Sampling for Top-k Shapley IdentificationCode0
Efficient Near-Optimal Algorithm for Online Shortest Paths in Directed Acyclic Graphs with Bandit Feedback Against Adaptive Adversaries—0
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Benchmark Results

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