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 1051–1100 of 1262 papers

TitleStatusHype
Balanced Linear Contextual Bandits—0
ADARES: Adaptive Resource Management for Virtual Machines—0
Contextual Combinatorial Multi-armed Bandits with Volatile Arms and Submodular Reward—0
Why so gloomy? A Bayesian explanation of human pessimism bias in the multi-armed bandit task—0
A Bandit Approach to Sequential Experimental Design with False Discovery Control—0
Stochastic Top-K Subset Bandits with Linear Space and Non-Linear Feedback—0
Adversarial Bandits with Knapsacks—0
Kernel-based Multi-Task Contextual Bandits in Cellular Network Configuration—0
Rotting bandits are not harder than stochastic ones—0
Bandits with Temporal Stochastic Constraints—0
Decentralized Exploration in Multi-Armed Bandits -- Extended version—0
Best Arm Identification in Linked Bandits—0
Sample complexity of partition identification using multi-armed bandits—0
Garbage In, Reward Out: Bootstrapping Exploration in Multi-Armed Bandits—0
Adapting multi-armed bandits policies to contextual bandits scenariosCode0
Practical Bayesian Learning of Neural Networks via Adaptive Optimisation MethodsCode0
Multi-armed Bandits with Compensation—0
Stay With Me: Lifetime Maximization Through Heteroscedastic Linear Bandits With RenegingCode0
Online learning with feedback graphs and switching costs—0
Simple Regret Minimization for Contextual Bandits—0
Regularized Contextual Bandits—0
Fighting Contextual Bandits with Stochastic Smoothing—0
Decentralized Cooperative Stochastic BanditsCode0
Thompson Sampling Algorithms for Cascading Bandits—0
Contextual Multi-Armed Bandits for Causal Marketing—0
Contextual Bandits with Cross-learning—0
SIC-MMAB: Synchronisation Involves Communication in Multiplayer Multi-Armed BanditsCode0
Multi-Player Bandits: A Trekking Approach—0
Machine Teaching of Active Sequential LearnersCode0
Diversity-Driven Selection of Exploration Strategies in Multi-Armed Bandits—0
Correlated Multi-armed Bandits with a Latent Random SourceCode0
Data Poisoning Attacks in Contextual Bandits—0
Nonparametric Gaussian Mixture Models for the Multi-Armed BanditCode0
On-line Adaptative Curriculum Learning for GANsCode0
Preference-based Online Learning with Dueling Bandits: A Survey—0
Deep Contextual Multi-armed Bandits—0
Tsallis-INF: An Optimal Algorithm for Stochastic and Adversarial Bandits—0
Linear Bandits with Stochastic Delayed Feedback—0
Multi-User Multi-Armed Bandits for Uncoordinated Spectrum Access—0
Learning to Coordinate with Coordination Graphs in Repeated Single-Stage Multi-Agent Decision Problems—0
Contextual bandits with surrogate losses: Margin bounds and efficient algorithms—0
Greybox fuzzing as a contextual bandits problem—0
Finding the bandit in a graph: Sequential search-and-stop—0
Mitigating Bias in Adaptive Data Gathering via Differential Privacy—0
A General Framework for Bandit Problems Beyond Cumulative Objectives—0
The Externalities of Exploration and How Data Diversity Helps Exploitation—0
Myopic Bayesian Design of Experiments via Posterior Sampling and Probabilistic ProgrammingCode0
Learning Contextual Bandits in a Non-stationary EnvironmentCode0
Multi-Statistic Approximate Bayesian Computation with Multi-Armed Bandits—0
Bandit-Based Monte Carlo Optimization for Nearest NeighborsCode0
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Benchmark Results

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