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 401–450 of 1262 papers

TitleStatusHype
On the Interplay Between Misspecification and Sub-optimality Gap in Linear Contextual Bandits—0
Only Pay for What Is Uncertain: Variance-Adaptive Thompson Sampling—0
Data Dependent Regret Guarantees Against General Comparators for Full or Bandit Feedback—0
Flooding with Absorption: An Efficient Protocol for Heterogeneous Bandits over Complex NetworksCode0
Queue Scheduling with Adversarial Bandit Learning—0
Efficient Explorative Key-term Selection Strategies for Conversational Contextual BanditsCode0
Fairness for Workers Who Pull the Arms: An Index Based Policy for Allocation of Restless Bandit Tasks—0
Multi-Armed Bandits with Generalized Temporally-Partitioned Rewards—0
Approximately Stationary Bandits with Knapsacks—0
The Choice of Noninformative Priors for Thompson Sampling in Multiparameter Bandit Models—0
Improved Best-of-Both-Worlds Guarantees for Multi-Armed Bandits: FTRL with General Regularizers and Multiple Optimal Arms—0
On Differentially Private Federated Linear Contextual Bandits—0
Kernel Conditional Moment Constraints for Confounding Robust InferenceCode0
Active Velocity Estimation using Light Curtains via Self-Supervised Multi-Armed Bandits—0
Asymptotically Unbiased Off-Policy Policy Evaluation when Reusing Old Data in Nonstationary Environments—0
Variance-Dependent Regret Bounds for Linear Bandits and Reinforcement Learning: Adaptivity and Computational Efficiency—0
A Blackbox Approach to Best of Both Worlds in Bandits and Beyond—0
Estimating Optimal Policy Value in General Linear Contextual Bandits—0
Stochastic Approximation Approaches to Group Distributionally Robust Optimization and Beyond—0
Online Continuous Hyperparameter Optimization for Generalized Linear Contextual Bandits—0
Improving Fairness in Adaptive Social Exergames via Shapley Bandits—0
Practical Contextual Bandits with Feedback Graphs—0
Infinite Action Contextual Bandits with Reusable Data ExhaustCode0
Genetic multi-armed bandits: a reinforcement learning approach for discrete optimization via simulation—0
Bandit Social Learning: Exploration under Myopic Behavior—0
Adversarial Rewards in Universal Learning for Contextual Bandits—0
Piecewise-Stationary Multi-Objective Multi-Armed Bandit with Application to Joint Communications and SensingCode0
Leveraging User-Triggered Supervision in Contextual Bandits—0
On Private and Robust Bandits—0
Multiplier Bootstrap-based Exploration—0
Randomized Greedy Learning for Non-monotone Stochastic Submodular Maximization Under Full-bandit Feedback—0
Stochastic Contextual Bandits with Long Horizon Rewards—0
Quantum contextual bandits and recommender systems for quantum data—0
Improved Algorithms for Multi-period Multi-class Packing Problems with Bandit Feedback—0
Adversarial Attacks on Adversarial Bandits—0
A Framework for Adapting Offline Algorithms to Solve Combinatorial Multi-Armed Bandit Problems with Bandit Feedback—0
Contextual Causal Bayesian Optimisation—0
Communication-Efficient Collaborative Regret Minimization in Multi-Armed Bandits—0
Banker Online Mirror Descent: A Universal Approach for Delayed Online Bandit Learning—0
Quantum Heavy-tailed Bandits—0
Multi-Armed Bandits and Quantum Channel Oracles—0
Multi-armed Bandit Learning for TDMA Transmission Slot Scheduling and Defragmentation for Improved Bandwidth Usage—0
Best Arm Identification in Stochastic Bandits: Beyond β-optimality—0
Local Differential Privacy for Sequential Decision Making in a Changing Environment—0
Contextual Bandits and Optimistically Universal Learning—0
Online Statistical Inference for Contextual Bandits via Stochastic Gradient Descent—0
On the Complexity of Representation Learning in Contextual Linear Bandits—0
MABSplit: Faster Forest Training Using Multi-Armed BanditsCode0
Faster Maximum Inner Product Search in High Dimensions—0
Corruption-Robust Algorithms with Uncertainty Weighting for Nonlinear Contextual Bandits and Markov Decision Processes—0
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Benchmark Results

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