Data poisoning attacks in contextual bandits
WebFeb 10, 2024 · Adversarial Attacks on Linear Contextual Bandits. Contextual bandit algorithms are applied in a wide range of domains, from advertising to recommender systems, from clinical trials to education. In many of these domains, malicious agents may have incentives to attack the bandit algorithm to induce it to perform a desired behavior. WebFigure 3: Histograms of poisoning effort ratio in the offline attacks. - "Data Poisoning Attacks on Stochastic Bandits"
Data poisoning attacks in contextual bandits
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WebData poisoning attacks in contextual bandits. In Conference on Decision and Game Theory for Security (GameSec), 2024. Google Scholar Cross Ref; Ng, Andrew Y., Harada, Daishi, and Russell, Stuart J. Policy invariance under reward transformations: Theory and application to reward shaping. WebSep 26, 2024 · Data Poisoning Attacks in Contextual Bandits: 9th International Conference, GameSec 2024, Seattle, WA, USA, October 29–31, 2024, Proceedings …
WebWe study offline data poisoning attacks in contextual bandits, a class of reinforcement learning problems with important applications in online recommendation and adaptive medical treatment, among others. We provide a general attack framework … WebApr 20, 2024 · Data poisoning attacks in contextual bandits. In International Conference on Decision and Game Theory for Security. Springer, 186–204. Google Scholar Cross Ref; Yao Ma, Suhang Wang, Charu C Aggarwal, and Jiliang Tang. 2024. Graph convolutional networks with eigenpooling. In Proceedings of the 25th ACM SIGKDD International …
WebIn addition to disclosing the security hole, we design a novel countermeasure for poisoning attacks on FedRecs. Specifically, we propose a hierarchical gradient clipping with sparsified updating to defend against existing poisoning attacks. The empirical results demonstrate that the proposed defending mechanism improves the robustness of ... WebAug 17, 2024 · We study offline data poisoning attacks in contextual bandits, a class of reinforcement learning problems with important applications in online recommendation …
WebY. Ma, K.-S. Jun, L. Li, and J. Zhu: Data poisoning attacks in contextual bandits. In the 9th Conference on Decision and Game Theory for Security (GameSec), ... L. Li, W. Chu, J. Langford, and R.E. Schapire: A contextual-bandit approach to personalized news article recommendation. In the 19th International Conference on World Wide Web ...
WebFeb 10, 2024 · In this paper, we study several attack scenarios and show that a malicious agent can force a linear contextual bandit algorithm to pull any desired arm T - o (T) times over a horizon of T steps, while applying adversarial modifications to either rewards or contexts that only grow logarithmically as O (log T). We also investigate the case when a ... how does the price cap affect meWebMar 30, 2024 · 攻击方法：. 1）Functional Adversarial Attacks 2）Improving Black-box Adversarial Attacks with a Transfer-based Prior 3）Cross-Domain Transferability of Adversarial Perturbations 4）Subspace Attack: Exploiting Promising Subspaces for Query-Efficient Black-box Attacks 5）A Unified Framework for Data Poisoning Attack to … how does the precision march exhibit seishinWebDec 1, 2024 · By using a novel contextual multi-armed bandit-based algorithm to balance the accuracy and efficiency, we propose a context-based online big-data-oriented … how does the pre trial system work in franceWebMay 16, 2024 · Stochastic multi-armed bandits form a class of online learning problems that have important applications in online recommendation systems, adaptive medical treatment, and many others. Even though potential attacks against these learning algorithms may hijack their behavior, causing catastrophic loss in real-world applications, little is known ... how does the prelude present natureWebDec 11, 2024 · X-armed bandits have achieved the state-of-the-art performance in optimizing unknown stochastic continuous functions, which can model many machine … photofeeler scam redditWebTrojAI Literature Review. The list below contains curated papers and arXiv articles that are related to Trojan attacks, backdoor attacks, and data poisoning on neural networks and machine learning systems. They are ordered "approximately" from most to least recent and articles denoted with a "*" mention the TrojAI program directly. photoferrotrophy 意味WebDec 10, 2024 · In order to develop trustworthy contextual bandit systems, understanding the impacts of various adversarial attacks on contextual bandit algorithms is essential. … photoferrotrophy什么意思