@inproceedings{6d4441b78b8a435ca6fb0f750e63a461,
title = "The Fourth Workshop on Adversarial Learning Methods for Machine Learning and Data Mining (AdvML 2022)",
abstract = "Adversarial learning methods and their applications such as generative adversarial network, adversarial robustness, and security and privacy, have prevailed and revolutionized the research in machine learning and data mining. Their importance has not only been emphasized by the research community but also been widely recognized by the industry and the general public. Continuing the synergies in previous years, this third annual workshop aims to advance this research field. The AdvML'22 workshop consists of four tracks: (i) open-call paper submissions; (ii) invited speakers; (iii) rising star awards and presentations; and (iv) panel discussion on AdvML. The full details about the workshop can be found at https://sites.google.com/view/advml.",
keywords = "adversarial machine learning, adversarial robustness",
author = "Chen, {Pin Yu} and Hsieh, {Cho Jui} and Bo Li and Sijia Liu",
note = "Publisher Copyright: {\textcopyright} 2022 Owner/Author.; 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, KDD 2022 ; Conference date: 14-08-2022 Through 18-08-2022",
year = "2022",
month = aug,
day = "14",
doi = "10.1145/3534678.3542897",
language = "English (US)",
series = "Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining",
publisher = "Association for Computing Machinery",
pages = "4858--4859",
booktitle = "KDD 2022 - Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining",
address = "United States",
}