# A Neural Networks Committee for the Contextual Bandit Problem

arXiv: https://arxiv.org/abs/1409.8191 · HTML: https://mlsanity.com/paper/1409.8191/

## Key facts

- Cited by 23 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 29 Sep 2014.

## Built on by (most cited first, 23 of 23)

- [Multi-facet Contextual Bandits: A Neural Network Perspective](https://mlsanity.com/paper/2106.03039/) (2021)
- [Learning Accurate Decision Trees with Bandit Feedback via Quantized Gradient Descent](https://mlsanity.com/paper/2102.07567/) (2021)
- [Tsetlin Machine for Solving Contextual Bandit Problems](https://mlsanity.com/paper/2202.01914/) (2022)
- [LLMs-augmented Contextual Bandit](https://mlsanity.com/paper/2311.02268/) (2023)
- [Neural Collaborative Filtering Bandits via Meta Learning](https://mlsanity.com/paper/2201.13395/) (2022)
- [Fast online inference for nonlinear contextual bandit based on Generative Adversarial Network](https://mlsanity.com/paper/2202.08867/) (2022)
- [Stochastic Conservative Contextual Linear Bandits](https://mlsanity.com/paper/2203.15629/) (2022)
- [Optimal Epidemic Control as a Contextual Combinatorial Bandit with Budget](https://mlsanity.com/paper/2106.15808/) (2021)
- [Adversarial Gradient Driven Exploration for Deep Click-Through Rate Prediction](https://mlsanity.com/paper/2112.11136/) (2021)
- [Multiplayer Information Asymmetric Contextual Bandits](https://mlsanity.com/paper/2503.08961/) (2025)
- [BanditMF: Multi-Armed Bandit Based Matrix Factorization Recommender System](https://mlsanity.com/paper/2106.10898/) (2021)
- [Universality of parametric Coupling Flows over parametric diffeomorphisms](https://mlsanity.com/paper/2202.02906/) (2022)
- [Leveraging heterogeneous spillover in maximizing contextual bandit rewards](https://mlsanity.com/paper/2310.10259/) (2023)
- [Thompson Sampling for Stochastic Bandits with Noisy Contexts: An Information-Theoretic Regret Analysis](https://mlsanity.com/paper/2401.11565/) (2024)
- [An Efficient Algorithm for Deep Stochastic Contextual Bandits](https://mlsanity.com/paper/2104.05613/) (2021)
- [Empirical analysis of representation learning and exploration in neural kernel bandits](https://mlsanity.com/paper/2111.03543/) (2021)
- [Non-Stationary Contextual Bandit Learning via Neural Predictive Ensemble Sampling](https://mlsanity.com/paper/2310.07786/) (2023)
- [Adversarial Bandits with Multi-User Delayed Feedback: Theory and Application](https://mlsanity.com/paper/2310.11188/) (2023)
- [LNUCB-TA: Linear-nonlinear Hybrid Bandit Learning with Temporal Attention](https://mlsanity.com/paper/2503.00387/) (2025)
- [BanditLP: Large-Scale Stochastic Optimization for Personalized Recommendations](https://mlsanity.com/paper/2601.15552/) (2026)
- [Etat de l'art sur l'application des bandits multi-bras](https://mlsanity.com/paper/2101.00001/) (2021)
- [Efficient Learning for Selecting Top-m Context-Dependent Designs](https://mlsanity.com/paper/2305.04086/) (2023)
- [Reliability-Optimized User Admission Control for URLLC Traffic: A Neural Contextual Bandit Approach](https://mlsanity.com/paper/2401.03059/) (2024)

## 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.