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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 301–350 of 1262 papers

TitleStatusHype
Query-Efficient Correlation Clustering with Noisy Oracle—0
Multi-Armed Bandits with Interference—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
Best-of-Both-Worlds Linear Contextual Bandits—0
Foundations of Reinforcement Learning and Interactive Decision Making—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
Bayesian Analysis of Combinatorial Gaussian Process Bandits—0
Distribution-Dependent Rates for Multi-Distribution Learning—0
Best Arm Identification with Fixed Budget: A Large Deviation PerspectiveCode0
Observation-Augmented Contextual Multi-Armed Bandits for Robotic Search and Exploration—0
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
Thompson sampling for zero-inflated count outcomes with an application to the Drink Less mobile health study—0
When is Off-Policy Evaluation (Reward Modeling) Useful in Contextual Bandits? A Data-Centric PerspectiveCode0
Provably Efficient High-Dimensional Bandit Learning with Batched Feedbacks—0
An Experimental Design for Anytime-Valid Causal Inference on Multi-Armed BanditsCode0
Adversarial Attacks on Cooperative Multi-agent Bandits—0
Efficient Generalized Low-Rank Tensor Contextual Bandits—0
LLMs-augmented Contextual Bandit—0
High-dimensional Linear Bandits with Knapsacks—0
Federated Linear Bandits with Finite Adversarial Actions—0
An Improved Relaxation for Oracle-Efficient Adversarial Contextual Bandits—0
Near-Optimal Pure Exploration in Matrix Games: A Generalization of Stochastic Bandits & Dueling BanditsCode0
A Risk-Averse Framework for Non-Stationary Stochastic Multi-Armed Bandits—0
Off-Policy Evaluation for Large Action Spaces via Policy Convolution—0
Contextual Bandits for Evaluating and Improving Inventory Control Policies—0
Towards a Pretrained Model for Restless Bandits via Multi-arm Generalization—0
α-Fair Contextual Bandits—0
Pure Exploration in Asynchronous Federated Bandits—0
Leveraging heterogeneous spillover in maximizing contextual bandit rewards—0
Bad Values but Good Behavior: Learning Highly Misspecified Bandits and MDPs—0
Byzantine-Resilient Decentralized Multi-Armed Bandits—0
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Benchmark Results

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