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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 101–150 of 1262 papers

TitleStatusHype
Doubly Robust Policy Evaluation and LearningCode0
Efficient Algorithms for Extreme BanditsCode0
Online SuBmodular + SuPermodular (BP) Maximization with Bandit FeedbackCode0
Intrinsically Efficient, Stable, and Bounded Off-Policy Evaluation for Reinforcement LearningCode0
From Complexity to Simplicity: Adaptive ES-Active Subspaces for Blackbox OptimizationCode0
Invariant Policy Learning: A Causal PerspectiveCode0
Distributionally Robust Policy Evaluation under General Covariate Shift in Contextual BanditsCode0
Kernel Conditional Moment Constraints for Confounding Robust InferenceCode0
RoME: A Robust Mixed-Effects Bandit Algorithm for Optimizing Mobile Health InterventionsCode0
Antithetic Sampling for Top-k Shapley IdentificationCode0
AC-Band: A Combinatorial Bandit-Based Approach to Algorithm ConfigurationCode0
Learning Structural Weight Uncertainty for Sequential Decision-MakingCode0
Let's Get It Started: Fostering the Discoverability of New Releases on DeezerCode0
Linear Contextual Bandits with Hybrid Payoff: RevisitedCode0
Approximating a Target Distribution using Weight QueriesCode0
Local Metric Learning for Off-Policy Evaluation in Contextual Bandits with Continuous ActionsCode0
Adversarial Attacks on Combinatorial Multi-Armed BanditsCode0
MABSplit: Faster Forest Training Using Multi-Armed BanditsCode0
Adapting multi-armed bandits policies to contextual bandits scenariosCode0
Meta-in-context learning in large language modelsCode0
Mitigating Exposure Bias in Online Learning to Rank Recommendation: A Novel Reward Model for Cascading BanditsCode0
Model-free Reinforcement Learning in Infinite-horizon Average-reward Markov Decision ProcessesCode0
A Survey of Online Experiment Design with the Stochastic Multi-Armed BanditCode0
Cost-Efficient Online Decision Making: A Combinatorial Multi-Armed Bandit ApproachCode0
A Survey on Contextual Multi-armed BanditsCode0
Multi-armed bandits for resource efficient, online optimization of language model pre-training: the use case of dynamic maskingCode0
Deep Bayesian Bandits Showdown: An Empirical Comparison of Bayesian Deep Networks for Thompson SamplingCode0
Doubly-Robust Lasso BanditCode0
Contextual Bandits with Stochastic ExpertsCode0
Contextual Bandits with Large Action Spaces: Made PracticalCode0
Contextual Linear Bandits under Noisy Features: Towards Bayesian OraclesCode0
Adaptive Action Duration with Contextual Bandits for Deep Reinforcement Learning in Dynamic EnvironmentsCode0
Confidence Intervals for Policy Evaluation in Adaptive ExperimentsCode0
Flooding with Absorption: An Efficient Protocol for Heterogeneous Bandits over Complex NetworksCode0
A Field Test of Bandit Algorithms for Recommendations: Understanding the Validity of Assumptions on Human Preferences in Multi-armed BanditsCode0
Confident Off-Policy Evaluation and Selection through Self-Normalized Importance WeightingCode0
Corralling a Band of Bandit AlgorithmsCode0
Conditionally Risk-Averse Contextual BanditsCode0
Constrained regret minimization for multi-criterion multi-armed banditsCode0
Contextual bandits with entropy-based human feedbackCode0
Contextual Bandits with Smooth Regret: Efficient Learning in Continuous Action SpacesCode0
Censored Semi-Bandits: A Framework for Resource Allocation with Censored FeedbackCode0
Causally Abstracted Multi-armed BanditsCode0
A New Bandit Setting Balancing Information from State Evolution and Corrupted ContextCode0
Combinatorial Bandits under Strategic ManipulationsCode0
Decentralized Cooperative Stochastic BanditsCode0
Cascading Bandits for Large-Scale Recommendation ProblemsCode0
Causal Contextual Bandits with Adaptive ContextCode0
Combinatorial Multi-armed Bandits for Resource AllocationCode0
Best Arm Identification with Fixed Budget: A Large Deviation PerspectiveCode0
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Benchmark Results

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