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. 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 ...

Data Poisoning Attacks in Contextual Bandits - arXiv

WebAug 17, 2024 · We study offline data poisoning attacks in contextual bandits, a class of reinforcement learning problems with important applications in online recommendation … WebMay 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 ... inc5000 log in https://emailmit.com

Dataset Security for Machine Learning: Data Poisoning, …

WebSep 26, 2024 · Data Poisoning Attacks in Contextual Bandits: 9th International Conference, GameSec 2024, Seattle, WA, USA, October 29–31, 2024, Proceedings … WebSep 26, 2024 · Data Poisoning Attacks in Contextual Bandits: 9th International Conference, GameSec 2024, Seattle, WA, USA, October 29–31, 2024, Proceedings September 2024 DOI: 10.1007/978-3-030-01554-1_11 WebSyndicated Bandits: A Framework for Auto Tuning Hyper-parameters in Contextual Bandit Algorithms QIN DING, Yue Kang, Yi-Wei Liu, Thomas Chun Man Lee, Cho-Jui Hsieh, James Sharpnack; ... A Powerful Defense against Data Poisoning Attack Tian Yu Liu, Yu Yang, Baharan Mirzasoleiman; inc5937076

Adversarial Attacks on Linear Contextual Bandits DeepAI

Category:Data Poisoning Attacks in Contextual Bandits SpringerLink

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Data poisoning attacks in contextual bandits

[2002.03839] Adversarial Attacks on Linear Contextual …

WebMay 16, 2024 · Stochastic multi-armed bandits form a class of online learning problems that have important applications in online recommendation systems, adaptive medical … WebMay 16, 2024 · Data Poisoning Attacks on Stochastic Bandits. Fang Liu, Ness Shroff. Stochastic multi-armed bandits form a class of online learning problems that have …

Data poisoning attacks in contextual bandits

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WebTrojAI 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. WebDepending on the target of the poisoning attacks, the poisoning attacks against contextual linear bandits can be categorized into four types: reward poisoning attack, action poisoning attack, context poisoning attack and the mix of them. In this paper, we aim to investigate the impact of action poisoning attacks on contextual bandit models.

WebDec 10, 2024 · In this paper, we propose a new class of attacks: action poisoning attacks, where an adversary can change the action signal selected by the agent. We design … 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 ...

WebDec 11, 2024 · X-armed bandits have achieved the state-of-the-art performance in optimizing unknown stochastic continuous functions, which can model many machine … WebIn this paper, we study the action poisoning attack against linear contextual bandit in both white-box and black-box settings. In the white-box setting, we assume that the attacker knows the coefficient vectors associated with arms. Thus, at each round, the attacker knows the mean rewards of all arms. While it is often unrealistic to exactly know

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 …

WebFigure 3: Histograms of poisoning effort ratio in the offline attacks. - "Data Poisoning Attacks on Stochastic Bandits" included on an email brieflyWebWe study offline data poisoning attacks in contextual bandits, a class of reinforcement learning problems with important applications in online recommendation and adaptive … inc56241WebDec 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 … included offers with six flags passWebData Poisoning Attacks in Contextual Bandits 3 Formally, a contextual bandit has a set Xof contexts and a set A= f1;2;:::;Kgof K arms. A contextual bandit algorithm proceeds … inc51Webcontextual bandit. We also investigate the feasibility and the side effects of such attacks, and identify future directions for defense. Experiments on both synthetic and real-world … included offenceWebAug 27, 2024 · For example, you can use a contextual bandit to select which news article to show first on the main page of your website to optimize click through rate. The context is information about the user: where they come from, previously visited pages of the site, device information, geolocation, etc. An action is a choice of what news article to display. included offensesinc5506