# Deep Neural Linear Bandits: Overcoming Catastrophic Forgetting through Likelihood Matching

arXiv: https://arxiv.org/abs/1901.08612 · HTML: https://mlsanity.com/paper/1901.08612/

## Key facts

- Cited by 17 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 24 Jan 2019.

## Built on by (most cited first, 17 of 17)

- [Batched Neural Bandits](https://mlsanity.com/paper/2102.13028/) (2021)
- [Multi-facet Contextual Bandits: A Neural Network Perspective](https://mlsanity.com/paper/2106.03039/) (2021)
- [Learning Neural Contextual Bandits Through Perturbed Rewards](https://mlsanity.com/paper/2201.09910/) (2022)
- [A Map of Bandits for E-commerce](https://mlsanity.com/paper/2107.00680/) (2021)
- [Anti-Concentrated Confidence Bonuses for Scalable Exploration](https://mlsanity.com/paper/2110.11202/) (2021)
- [Fast online inference for nonlinear contextual bandit based on Generative Adversarial Network](https://mlsanity.com/paper/2202.08867/) (2022)
- [Meta Clustering of Neural Bandits](https://mlsanity.com/paper/2408.05586/) (2024)
- [Neural Logistic Bandits](https://mlsanity.com/paper/2505.02069/) (2025)
- [Influence Maximization via Graph Neural Bandits](https://mlsanity.com/paper/2406.12835/) (2024)
- [Deep Upper Confidence Bound Algorithm for Contextual Bandit Ranking of Information Selection](https://mlsanity.com/paper/2110.04127/) (2021)
- [Reward-Biased Maximum Likelihood Estimation for Neural Contextual Bandits](https://mlsanity.com/paper/2203.04192/) (2022)
- [Top-K Ranking Deep Contextual Bandits for Information Selection Systems](https://mlsanity.com/paper/2201.13287/) (2022)
- [Graph Neural Thompson Sampling](https://mlsanity.com/paper/2406.10686/) (2024)
- [CAE: Repurposing the Critic as an Explorer in Deep Reinforcement Learning](https://mlsanity.com/paper/2503.18980/) (2025)
- [Quantum-Enhanced Neural Contextual Bandit Algorithms](https://mlsanity.com/paper/2601.02870/) (2026)
- [CASCADE: Case-Based Continual Adaptation for Large Language Models During Deployment](https://mlsanity.com/paper/2605.06702/) (2026)
- [Regularized OFU: an Efficient UCB Estimator forNon-linear Contextual Bandit](https://mlsanity.com/paper/2106.15128/) (2021)

## Abstract (arXiv)

We study the neural-linear bandit model for solving sequential decision-making problems with high dimensional side information. Neural-linear bandits leverage the representation power of deep neural networks and combine it with efficient exploration mechanisms, designed for linear contextual bandits, on top of the last hidden layer. Since the representation is being optimized during learning, information regarding exploration with "old" features is lost. Here, we propose the first limited memory neural-linear bandit that is resilient to this phenomenon, which we term catastrophic forgetting. We evaluate our method on a variety of real-world data sets, including regression, classification, and sentiment analysis, and observe that our algorithm is resilient to catastrophic forgetting and achieves superior performance.