Publication Type

Conference Proceeding Article

Version

publishedVersion

Publication Date

8-2022

Abstract

The detection of, explanation of, and accommodation to anomalies and novelties are active research areas in multiple communities, including data mining, machine learning, and computer vision. They are applied in various guises including anomaly detection, out-of-distribution example detection, adversarial example recognition and detection, curiosity-driven reinforcement learning, and open-set recognition and adaptation, all of which are of great interest to the SIGKDD community. The techniques developed have been applied in a wide range of domains including fraud detection and anti-money laundering in fintech, early disease detection, intrusion detection in large-scale computer networks and data centers, defending AI systems from adversarial attacks, and in improving the practicality of agents through overcoming the closed-world assumption.This workshop is focused on Anomaly and Novelty Detection, Explanation, and Accommodation (ANDEA). It will gather researchers and practitioners from data mining, machine learning, and computer vision communities and diverse knowledge background to promote the development of fundamental theories, effective algorithms, and novel applications of anomaly and novelty detection, characterization, and adaptation. All materials of keynote talks and accepted papers of the workshop are made available at https://sites.google.com/view/andea2022/.

Keywords

Anomaly detection, Anomaly explanation, Novelty accommodation, Novelty detection, Novelty explanation, Outlier detection

Discipline

Artificial Intelligence and Robotics

Research Areas

Data Science and Engineering

Publication

Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, Washington DC, USA, 2022 August 14 -18

First Page

4892

Last Page

4893

ISBN

9781450393850

Identifier

10.1145/3534678.3542910

Publisher

Association for Computing Machinery

City or Country

Washington

Additional URL

https://doi.org/10.1145/3534678.3542910

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