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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
Communication Efficient Distributed Learning for Kernelized Contextual Bandits—0
Conformal Off-Policy Prediction in Contextual Bandits—0
Efficient Resource Allocation with Fairness Constraints in Restless Multi-Armed Bandits—0
Neural Bandit with Arm Group Graph—0
Finite-Time Regret of Thompson Sampling Algorithms for Exponential Family Multi-Armed Bandits—0
A Simple and Optimal Policy Design with Safety against Heavy-Tailed Risk for Stochastic Bandits—0
Group Meritocratic Fairness in Linear Contextual BanditsCode0
Robust Pareto Set Identification with Contaminated Bandit Feedback—0
Asymptotic Instance-Optimal Algorithms for Interactive Decision Making—0
Contextual Bandits with Knapsacks for a Conversion Model—0
Provable General Function Class Representation Learning in Multitask Bandits and MDPs—0
Online Meta-Learning in Adversarial Multi-Armed Bandits—0
Provably and Practically Efficient Neural Contextual Bandits—0
Optimistic Whittle Index Policy: Online Learning for Restless BanditsCode0
Quantum Multi-Armed Bandits and Stochastic Linear Bandits Enjoy Logarithmic Regrets—0
Federated Neural BanditsCode0
Fairness and Welfare Quantification for Regret in Multi-Armed Bandits—0
Lifting the Information Ratio: An Information-Theoretic Analysis of Thompson Sampling for Contextual Bandits—0
Meta-Learning Adversarial Bandits—0
Exploration, Exploitation, and Engagement in Multi-Armed Bandits with Abandonment—0
Contextual Pandora's Box—0
Neural Contextual Bandits Based Dynamic Sensor Selection for Low-Power Body-Area Networks—0
Information-Directed Selection for Top-Two AlgorithmsCode0
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
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
Federated Multi-Armed Bandits Under Byzantine Attacks—0
Selectively Contextual Bandits—0
Multi-Player Multi-Armed Bandits with Finite Shareable Resources Arms: Learning Algorithms & Applications—0
Evolutionary Multi-Armed Bandits with Genetic Thompson SamplingCode0
Rate-Constrained Remote Contextual Bandits—0
Thompson Sampling for Bandit Learning in Matching MarketsCode0
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
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Benchmark Results

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