Deception by Omission: Using Adversarial Missingness to Poison Causal Structure Learning-Reference-Cited by-同舟云学术

Deception by Omission: Using Adversarial Missingness to Poison Causal Structure Learning

Published:2023-08-04 Issue: Volume: Page:
ISSN:
Container-title:Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining
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Author:

Koyuncu Deniz¹^ORCID,Gittens Alex¹^ORCID,Yener Bülent¹^ORCID,Yung Moti²^ORCID

Affiliation:

1. Rensselaer Polytechnic Institute, Troy, NY, USA

2. Google LLC & Columbia University, New York, NY, USA

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3580305.3599297

Reference30 articles.

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3. Adversarial data poisoning attacks against the PC learning algorithm

4. Rohit Bhattacharya Razieh Nabi Ilya Shpitser and James M Robins. 2020. Identification in missing data models represented by directed acyclic graphs. In Uncertainty in Artificial Intelligence. PMLR 1149--1158. Rohit Bhattacharya Razieh Nabi Ilya Shpitser and James M Robins. 2020. Identification in missing data models represented by directed acyclic graphs. In Uncertainty in Artificial Intelligence. PMLR 1149--1158.

5. A Backdoor Attack Against LSTM-Based Text Classification Systems

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1. Deception by Omission: Using Adversarial Missingness to Poison Causal Structure Learning;Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining;2023-08-04