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 1001–1050 of 1262 papers

TitleStatusHype
Upper Counterfactual Confidence Bounds: a New Optimism Principle for Contextual Bandits—0
Value Directed Exploration in Multi-Armed Bandits with Structured Priors—0
Variance-Dependent Regret Bounds for Linear Bandits and Reinforcement Learning: Adaptivity and Computational Efficiency—0
Variance-Dependent Regret Lower Bounds for Contextual Bandits—0
Variance-Optimal Augmentation Logging for Counterfactual Evaluation in Contextual Bandits—0
Variational Inference for Model-Free and Model-Based Reinforcement Learning—0
Versatile Dueling Bandits: Best-of-both-World Analyses for Online Learning from Preferences—0
Vertical Federated Linear Contextual Bandits—0
Wasserstein Distributionally Robust Policy Evaluation and Learning for Contextual Bandits—0
Bandit algorithms to emulate human decision making using probabilistic distortions—0
What Doubling Tricks Can and Can't Do for Multi-Armed Bandits—0
Bad Values but Good Behavior: Learning Highly Misspecified Bandits and MDPs—0
When Privacy Meets Partial Information: A Refined Analysis of Differentially Private Bandits—0
Whittle Index Learning Algorithms for Restless Bandits with Constant Stepsizes—0
Why so gloomy? A Bayesian explanation of human pessimism bias in the multi-armed bandit task—0
Worst-case Performance of Greedy Policies in Bandits with Imperfect Context Observations—0
You Can Trade Your Experience in Distributed Multi-Agent Multi-Armed Bandits—0
A Survey on Practical Applications of Multi-Armed and Contextual Bandits—0
Zero-Inflated Bandits—0
Functional multi-armed bandit and the best function identification problems—0
A Bandit Approach to Sequential Experimental Design with False Discovery Control—0
A Batch Sequential Halving Algorithm without Performance Degradation—0
Context in Public Health for Underserved Communities: A Bayesian Approach to Online Restless Bandits—0
A Blackbox Approach to Best of Both Worlds in Bandits and Beyond—0
Access Probability Optimization in RACH: A Multi-Armed Bandits Approach—0
Accurate and Fast Federated Learning via Combinatorial Multi-Armed Bandits—0
A Central Limit Theorem, Loss Aversion and Multi-Armed Bandits—0
Achieving adaptivity and optimality for multi-armed bandits using Exponential-Kullback Leibler Maillard Sampling—0
Achieving User-Side Fairness in Contextual Bandits—0
A Classification View on Meta Learning Bandits—0
A Closer Look at Small-loss Bounds for Bandits with Graph Feedback—0
A Contextual Combinatorial Bandit Approach to Negotiation—0
A Contextual Combinatorial Semi-Bandit Approach to Network Bottleneck Identification—0
A Correction of Pseudo Log-Likelihood Method—0
Active Inference for Autonomous Decision-Making with Contextual Multi-Armed Bandits—0
Active Reinforcement Learning: Observing Rewards at a Cost—0
Active Search for High Recall: a Non-Stationary Extension of Thompson Sampling—0
Active Search for Sparse Signals with Region Sensing—0
Active Velocity Estimation using Light Curtains via Self-Supervised Multi-Armed Bandits—0
AdaLinUCB: Opportunistic Learning for Contextual Bandits—0
AdaptEx: A Self-Service Contextual Bandit Platform—0
Adapting Bandit Algorithms for Settings with Sequentially Available Arms—0
Adapting to Delays and Data in Adversarial Multi-Armed Bandits—0
Adapting to Misspecification in Contextual Bandits with Offline Regression Oracles—0
Adapting to Misspecification in Contextual Bandits—0
Adaptive Best-of-Both-Worlds Algorithm for Heavy-Tailed Multi-Armed Bandits—0
Adaptive Budgeted Multi-Armed Bandits for IoT with Dynamic Resource Constraints—0
Adaptive Contract Design for Crowdsourcing Markets: Bandit Algorithms for Repeated Principal-Agent Problems—0
Adaptive Data Augmentation for Thompson Sampling—0
Adaptive Discretization against an Adversary: Lipschitz bandits, Dynamic Pricing, and Auction Tuning—0
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Benchmark Results

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