4th Workshop on Adversarial Learning Methods for Machine Learning and Data Mining @ KDD 2022

Workshop Agenda: Half-day event co-located with KDD 2022 (Aug 15th, 1-5 pm, Room 201)

  • We are happy to announce that Bon View Press will sponsor our best paper award with a 500 USD cash prize!

  • Accepted workshop papers now have the option to publish in the Journal of Computational and Cognitive Engineering with a fast-track review process!

  • Co-located conference: KDD 2022

  • Workshop Date and time: Aug. 15th (1-5 pm)

  • Organizers: Pin-Yu Chen (IBM Research), Cho-Jui Hsieh (UCLA), Bo Li (UIUC), SIjia Liu (Michigan State University)

  • Paper submission Deadline: May 26th, 2022 (anywhere on earth) June 2nd, 2022 (anywhere on earth; final)

  • Notification Date: June 20th, 2022

  • Submission Site: CMT

  • Paper submission format: ACM template (sample-sigconf), 4 pages excluding references and supporting materials in one single pdf file. The authors can choose to anonymize the author information during submission (but are not required to do so)

  • Call for AdvML Rising Star Award Nominations! (Due June 24th)

Accepted Papers:

New York University

Neuro-inspired Mechanisms for Adversarial Robustness

Topics of interest include but are not limited to:

  • Adversarial attacks and defenses in machine learning and data mining

  • Provably robust machine learning methods and systems

  • Robustness certification and property verification techniques

  • Representation learning, knowledge discovery and model generalizability

  • Generative models and their applications (e.g., generative adversarial nets)

  • Robust optimization methods and (computational) game theory

  • Explainable and fair machine learning models via adversarial learning techniques

  • Transfer learning, multi-agent adaptation, self-paced learning

  • Privacy and security in machine learning systems

  • Adversarial machine learning for (social) good

  • Novel applications and innovations using adversarial machine learning and data mining


Pin-Yu Chen (IBM Research), Cho-Jui Hsieh (UCLA), Bo Li (UIUC), Sijia Liu (Michigan State University)