# Robust Stochastic Linear Contextual Bandits Under Adversarial Attacks

arXiv: https://arxiv.org/abs/2106.02978 · HTML: https://mlsanity.com/paper/2106.02978/

## Key facts

- Cited by 33 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 27 references that are in the map.
- First posted on arXiv 5 Jun 2021.

## Builds on (most cited first, 13 of 13)

- [Stochastic bandits robust to adversarial corruptions](https://mlsanity.com/paper/1803.09353/) (2018)
- [Better Algorithms for Stochastic Bandits with Adversarial Corruptions](https://mlsanity.com/paper/1902.08647/) (2019)
- [Learning to Optimize under Non-Stationarity](https://mlsanity.com/paper/1810.03024/) (2018)
- [Stochastic Linear Bandits Robust to Adversarial Attacks](https://mlsanity.com/paper/2007.03285/) (2020)
- [Adversarial Attacks on Stochastic Bandits](https://mlsanity.com/paper/1810.12188/) (2018)
- [Data Poisoning Attacks on Stochastic Bandits](https://mlsanity.com/paper/1905.06494/) (2019)
- [Adversarial Attacks on Linear Contextual Bandits](https://mlsanity.com/paper/2002.03839/) (2020)
- [Corruption-Tolerant Gaussian Process Bandit Optimization](https://mlsanity.com/paper/2003.01971/) (2020)
- [An Efficient Algorithm For Generalized Linear Bandit: Online Stochastic Gradient Descent and Thompson Sampling](https://mlsanity.com/paper/2006.04012/) (2020)
- [Stochastic Linear Optimization with Adversarial Corruption](https://mlsanity.com/paper/1909.02109/) (2019)
- [Data Poisoning Attacks in Contextual Bandits](https://mlsanity.com/paper/1808.05760/) (2018)
- [Stochastic Linear Contextual Bandits with Diverse Contexts](https://mlsanity.com/paper/2003.02681/) (2020)
- [Robust Bandit Learning with Imperfect Context](https://mlsanity.com/paper/2102.05018/) (2021)

## Built on by (most cited first, 30 of 33)

- [Nearly Optimal Algorithms for Linear Contextual Bandits with Adversarial Corruptions](https://mlsanity.com/paper/2205.06811/) (2022)
- [Corruption-Robust Algorithms with Uncertainty Weighting for Nonlinear Contextual Bandits and Markov Decision Processes](https://mlsanity.com/paper/2212.05949/) (2022)
- [Corruption-Robust Offline Reinforcement Learning with General Function Approximation](https://mlsanity.com/paper/2310.14550/) (2023)
- [Linear Contextual Bandits with Adversarial Corruptions](https://mlsanity.com/paper/2110.12615/) (2021)
- [BanditSpec: Adaptive Speculative Decoding via Bandit Algorithms](https://mlsanity.com/paper/2505.15141/) (2025)
- [Robust Lipschitz Bandits to Adversarial Corruptions](https://mlsanity.com/paper/2305.18543/) (2023)
- [Best-of-three-worlds Analysis for Linear Bandits with Follow-the-regularized-leader Algorithm](https://mlsanity.com/paper/2303.06825/) (2023)
- [Towards Robust Model-Based Reinforcement Learning Against Adversarial Corruption](https://mlsanity.com/paper/2402.08991/) (2024)
- [Best-of-Both-Worlds Algorithms for Linear Contextual Bandits](https://mlsanity.com/paper/2312.15433/) (2023)
- [Can Agents Run Relay Race with Strangers? Generalization of RL to Out-of-Distribution Trajectories](https://mlsanity.com/paper/2304.13424/) (2023)
- [Nearly Optimal Algorithms for Contextual Dueling Bandits from Adversarial Feedback](https://mlsanity.com/paper/2404.10776/) (2024)
- [On the Model-Misspecification in Reinforcement Learning](https://mlsanity.com/paper/2306.10694/) (2023)
- [Adversarial Attacks on Adversarial Bandits](https://mlsanity.com/paper/2301.12595/) (2023)
- [Strategic Linear Contextual Bandits](https://mlsanity.com/paper/2406.00551/) (2024)
- [Robust Contextual Linear Bandits](https://mlsanity.com/paper/2210.14483/) (2022)
- [Online learning in bandits with predicted context](https://mlsanity.com/paper/2307.13916/) (2023)
- [Robust Causal Bandits for Linear Models](https://mlsanity.com/paper/2310.19794/) (2023)
- [LC-Tsallis-INF: Generalized Best-of-Both-Worlds Linear Contextual Bandits](https://mlsanity.com/paper/2403.03219/) (2024)
- [Efficient Action Poisoning Attacks on Linear Contextual Bandits](https://mlsanity.com/paper/2112.05367/) (2021)
- [Online Corrupted User Detection and Regret Minimization](https://mlsanity.com/paper/2310.04768/) (2023)
- [Non-Stationary Latent Auto-Regressive Bandits](https://mlsanity.com/paper/2402.03110/) (2024)
- [Best-of-Both-Worlds Linear Contextual Bandits](https://mlsanity.com/paper/2312.16489/) (2023)
- [Stealthy Adversarial Attacks on Stochastic Multi-Armed Bandits](https://mlsanity.com/paper/2402.13487/) (2024)
- [Learning from Imperfect Human Feedback: a Tale from Corruption-Robust Dueling](https://mlsanity.com/paper/2405.11204/) (2024)
- [Robust Thompson Sampling Algorithms Against Reward Poisoning Attacks](https://mlsanity.com/paper/2410.19705/) (2024)
- [Online Continuous Hyperparameter Optimization for Generalized Linear Contextual Bandits](https://mlsanity.com/paper/2302.09440/) (2023)
- [A Jointly Efficient and Optimal Algorithm for Heteroskedastic Generalized Linear Bandits with Adversarial Corruptions](https://mlsanity.com/paper/2602.10971/) (2026)
- [Learning to Seek: Multi-Agent Online Source Seeking Against Non-Stochastic Disturbances](https://mlsanity.com/paper/2305.00154/) (2023)
- [Robust Best-arm Identification in Linear Bandits](https://mlsanity.com/paper/2311.04731/) (2023)
- [Robust In-Context Reinforcement Learning Under Reward Poisoning Attacks](https://mlsanity.com/paper/2506.06891/) (2025)

## Abstract (arXiv)

Stochastic linear contextual bandit algorithms have substantial applications in practice, such as recommender systems, online advertising, clinical trials, etc. Recent works show that optimal bandit algorithms are vulnerable to adversarial attacks and can fail completely in the presence of attacks. Existing robust bandit algorithms only work for the non-contextual setting under the attack of rewards and cannot improve the robustness in the general and popular contextual bandit environment. In addition, none of the existing methods can defend against attacked context. In this work, we provide the first robust bandit algorithm for stochastic linear contextual bandit setting under a fully adaptive and omniscient attack with sub-linear regret. Our algorithm not only works under the attack of rewards, but also under attacked context. Moreover, it does not need any information about the attack budget or the particular form of the attack. We provide theoretical guarantees for our proposed algorithm and show by experiments that our proposed algorithm improves the robustness against various kinds of popular attacks.