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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
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
LLMs-augmented Contextual Bandit—0
Adversarial Attacks on Cooperative Multi-agent Bandits—0
Efficient Generalized Low-Rank Tensor Contextual Bandits—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
Contextual Bandits for Evaluating and Improving Inventory Control Policies—0
Off-Policy Evaluation for Large Action Spaces via Policy Convolution—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
Non-Stationary Contextual Bandit Learning via Neural Predictive Ensemble Sampling—0
Byzantine-Resilient Decentralized Multi-Armed Bandits—0
Ensemble Active Learning by Contextual Bandits for AI Incubation in Manufacturing—0
Adversarial Attacks on Combinatorial Multi-Armed BanditsCode0
Improved Algorithms for Adversarial Bandits with Unbounded Losses—0
Finite-Time Analysis of Whittle Index based Q-Learning for Restless Multi-Armed Bandits with Neural Network Function Approximation—0
Adversarial Contextual Bandits Go Kernelized—0
Bayesian Design Principles for Frequentist Sequential LearningCode0
Discrete Choice Multi-Armed Bandits—0
Follow-ups Also Matter: Improving Contextual Bandits via Post-serving Contexts—0
Diversify and Conquer: Bandits and Diversity for an Enhanced E-commerce Homepage Experience—0
A Convex Framework for Confounding Robust InferenceCode0
Task Selection and Assignment for Multi-modal Multi-task Dialogue Act Classification with Non-stationary Multi-armed Bandits—0
Wasserstein Distributionally Robust Policy Evaluation and Learning for Contextual Bandits—0
Doubly High-Dimensional Contextual Bandits: An Interpretable Model for Joint Assortment-Pricing—0
The Best Arm Evades: Near-optimal Multi-pass Streaming Lower Bounds for Pure Exploration in Multi-armed Bandits—0
Bypassing the Simulator: Near-Optimal Adversarial Linear Contextual Bandits—0
Concentrated Differential Privacy for Bandits—0
Pure Exploration under Mediators' Feedback—0
Stochastic Graph Bandit Learning with Side-Observations—0
Learning How to Price Charging in Electric Ride-Hailing Markets—0
Master-slave Deep Architecture for Top-K Multi-armed Bandits with Non-linear Bandit Feedback and Diversity ConstraintsCode0
On Universally Optimal Algorithms for A/B Testing—0
Clustered Linear Contextual Bandits with Knapsacks—0
Graph Neural Bandits—0
Cost-Efficient Online Decision Making: A Combinatorial Multi-Armed Bandit ApproachCode0
Equitable Restless Multi-Armed Bandits: A General Framework Inspired By Digital HealthCode1
AdaptEx: A Self-Service Contextual Bandit Platform—0
Cooperative Multi-agent Bandits: Distributed Algorithms with Optimal Individual Regret and Constant Communication Costs—0
Transfer Learning with Partially Observable Offline Data via Causal Bounds—0
Online Matching: A Real-time Bandit System for Large-scale RecommendationsCode0
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Benchmark Results

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