mlsanityData to 7 Oct 2026

A Neural Networks Committee for the Contextual Bandit Problem

arXiv 1409.8191 ↗First posted 29 Sep 2014cs.NE cs.LGcited by 23

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Abstract (arXiv)

This paper presents a new contextual bandit algorithm, NeuralBandit, which does not need hypothesis on stationarity of contexts and rewards. Several neural networks are trained to modelize the value of rewards knowing the context. Two variants, based on multi-experts approach, are proposed to choose online the parameters of multi-layer perceptrons. The proposed algorithms are successfully tested on a large dataset with and without stationarity of rewards.

Key facts. Cited by 23 papers in the map (data through 7 Oct 2026). First posted on arXiv 29 Sep 2014.

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 8; 2022: 5; 2023: 5; 2024: 2; 2025: 2; 2026: 1.

23 papers.

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  1. 2021Multi-facet Contextual Bandits: A Neural Network Perspectivecited by 20
  2. 2021Learning Accurate Decision Trees with Bandit Feedback via Quantized Gradient Descentcited by 14
  3. 2022Tsetlin Machine for Solving Contextual Bandit Problemscited by 10
  4. 2023LLMs-augmented Contextual Banditcited by 8
  5. 2022Neural Collaborative Filtering Bandits via Meta Learningcited by 7
  6. 2022Fast online inference for nonlinear contextual bandit based on Generative Adversarial Networkcited by 7
  7. 2022Stochastic Conservative Contextual Linear Banditscited by 6
  8. 2021Optimal Epidemic Control as a Contextual Combinatorial Bandit with Budgetcited by 5
  9. 2021Adversarial Gradient Driven Exploration for Deep Click-Through Rate Predictioncited by 4
  10. 2025Multiplayer Information Asymmetric Contextual Banditscited by 3
  11. 2021BanditMF: Multi-Armed Bandit Based Matrix Factorization Recommender Systemcited by 2
  12. 2022Universality of parametric Coupling Flows over parametric diffeomorphismscited by 2
11 more
  1. 2023Leveraging heterogeneous spillover in maximizing contextual bandit rewardscited by 2
  2. 2024Thompson Sampling for Stochastic Bandits with Noisy Contexts: An Information-Theoretic Regret Analysiscited by 2
  3. 2021An Efficient Algorithm for Deep Stochastic Contextual Banditscited by 1
  4. 2021Empirical analysis of representation learning and exploration in neural kernel banditscited by 1
  5. 2023Non-Stationary Contextual Bandit Learning via Neural Predictive Ensemble Samplingcited by 1
  6. 2023Adversarial Bandits with Multi-User Delayed Feedback: Theory and Applicationcited by 1
  7. 2025LNUCB-TA: Linear-nonlinear Hybrid Bandit Learning with Temporal Attentioncited by 1
  8. 2026BanditLP: Large-Scale Stochastic Optimization for Personalized Recommendationscited by 1
  9. 2021Etat de l'art sur l'application des bandits multi-brascited by 0
  10. 2023Efficient Learning for Selecting Top-m Context-Dependent Designscited by 0
  11. 2024Reliability-Optimized User Admission Control for URLLC Traffic: A Neural Contextual Bandit Approachcited by 0