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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 551–600 of 1262 papers

TitleStatusHype
Information-Directed Selection for Top-Two AlgorithmsCode0
Neural Contextual Bandits Based Dynamic Sensor Selection for Low-Power Body-Area Networks—0
Computationally Efficient Horizon-Free Reinforcement Learning for Linear Mixture MDPs—0
Falsification of Multiple Requirements for Cyber-Physical Systems Using Online Generative Adversarial Networks and Multi-Armed Bandits—0
Contextual Information-Directed Sampling—0
Pessimism for Offline Linear Contextual Bandits using _p Confidence Sets—0
SplitPlace: AI Augmented Splitting and Placement of Large-Scale Neural Networks in Mobile Edge EnvironmentsCode1
Stability Enforced Bandit Algorithms for Channel Selection in Remote State Estimation of Gauss-Markov Processes—0
Breaking the T Barrier: Instance-Independent Logarithmic Regret in Stochastic Contextual Linear Bandits—0
Multi-Armed Bandits in Brain-Computer InterfacesCode0
Slowly Changing Adversarial Bandit Algorithms are Efficient for Discounted MDPs—0
Semi-Parametric Contextual Bandits with Graph-Laplacian Regularization—0
From Dirichlet to Rubin: Optimistic Exploration in RL without Bonuses—0
Nearly Optimal Algorithms for Linear Contextual Bandits with Adversarial Corruptions—0
A Survey of Risk-Aware Multi-Armed Bandits—0
Selectively Contextual Bandits—0
Federated Multi-Armed Bandits Under Byzantine Attacks—0
Pervasive Machine Learning for Smart Radio Environments Enabled by Reconfigurable Intelligent SurfacesCode1
Multi-Player Multi-Armed Bandits with Finite Shareable Resources Arms: Learning Algorithms & Applications—0
Evolutionary Multi-Armed Bandits with Genetic Thompson SamplingCode0
Thompson Sampling for Bandit Learning in Matching MarketsCode0
Rate-Constrained Remote Contextual Bandits—0
Worst-case Performance of Greedy Policies in Bandits with Imperfect Context Observations—0
Stochastic Multi-armed Bandits with Non-stationary Rewards Generated by a Linear Dynamical System—0
Strategies for Safe Multi-Armed Bandits with Logarithmic Regret and Risk—0
Flexible and Efficient Contextual Bandits with Heterogeneous Treatment Effect Oracles—0
Best Arm Identification in Restless Markov Multi-Armed Bandits—0
On Kernelized Multi-Armed Bandits with Constraints—0
Modeling Attrition in Recommender Systems with Departing Bandits—0
Multi-armed bandits for resource efficient, online optimization of language model pre-training: the use case of dynamic maskingCode0
Efficient Algorithms for Extreme BanditsCode0
Approximate Function Evaluation via Multi-Armed Bandits—0
Reinforced Meta Active Learning—0
Reward-Biased Maximum Likelihood Estimation for Neural Contextual Bandits—0
PAC-Bayesian Lifelong Learning For Multi-Armed Bandits—0
Restless Multi-Armed Bandits under Exogenous Global Markov Process—0
Federated Online Sparse Decision Making—0
Truncated LinUCB for Stochastic Linear BanditsCode0
The Pareto Frontier of Instance-Dependent Guarantees in Multi-Player Multi-Armed Bandits with no Communication—0
Cost-Efficient Distributed Learning via Combinatorial Multi-Armed Bandits—0
Versatile Dueling Bandits: Best-of-both-World Analyses for Online Learning from Preferences—0
Off-Policy Evaluation for Large Action Spaces via EmbeddingsCode2
Shuffle Private Linear Contextual Bandits—0
Efficient Kernel UCB for Contextual BanditsCode0
Remote Contextual Bandits—0
Settling the Communication Complexity for Distributed Offline Reinforcement Learning—0
Smoothed Online Learning is as Easy as Statistical Learning—0
Budgeted Combinatorial Multi-Armed Bandits—0
Variance-Optimal Augmentation Logging for Counterfactual Evaluation in Contextual Bandits—0
Doubly Robust Off-Policy Evaluation for Ranking Policies under the Cascade Behavior ModelCode2
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Benchmark Results

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