The Fourth Workshop on Adversarial Learning Methods for Machine Learning and Data Mining (AdvML 2022)

Pin Yu Chen, Cho Jui Hsieh, Bo Li, Sijia Liu

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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.

Original languageEnglish (US)
Title of host publicationKDD 2022 - Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining
PublisherAssociation for Computing Machinery
Pages4858-4859
Number of pages2
ISBN (Electronic)9781450393850
DOIs
StatePublished - Aug 14 2022
Event28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, KDD 2022 - Washington, United States
Duration: Aug 14 2022Aug 18 2022

Publication series

NameProceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining

Conference

Conference28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, KDD 2022
Country/TerritoryUnited States
CityWashington
Period8/14/228/18/22

Keywords

  • adversarial machine learning
  • adversarial robustness

ASJC Scopus subject areas

  • Software
  • Information Systems

Fingerprint

Dive into the research topics of 'The Fourth Workshop on Adversarial Learning Methods for Machine Learning and Data Mining (AdvML 2022)'. Together they form a unique fingerprint.

Cite this