Call-for-papers: KDD 2021 Workshop on Anomaly and Novelty Detection, Explanation, and Accommodation (ANDEA 2021)

The 1st Workshop on Anomaly and Novelty Detection, Explanation, and Accommodation (ANDEA 2021) will be co-located with KDD 2021 and held online in August 2021.

Anomalies are referred to as observations or events that are rare or significantly different from the majority of observations we have in hand, while novelties are observations from novel classes that were unseen during learning. Detection, explanation, and accommodation to anomalies and novelties are active research areas in multiple communities, including data mining, machine learning, and computer vision. Some of the most relevant well-established research areas include anomaly detection, out-of-distribution example detection, adversarial example recognition and detection, curiosity-driven reinforcement learning, open-set recognition and adaptation, all of which are of great interest to the SIGKDD community. The successful early detection and accommodation of anomalies and novelties is of great significance across many domains. For example, it may prevent the loss of billions of dollars by its application to fraud detection and anti-money laundering in fintech, save millions of lives through early disease detection, safeguard large-scale computer networks and data centers from malicious attacks by its use in intrusion detection, defend AI systems from adversarial attacks, and equip AI systems with the ability to work safely in open worlds. 

This workshop 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.

This workshop will feature the most recent artificial intelligence advances for detection, explanation and accommodation of anomalies and novelties. It targets both academic researchers and industrial practitioners from data mining, machine learning and computer vision communities, and solicits original research on but not limited to the following topics.

* Detection/accommodation of anomalies/novelties in different types of data
  - Anomaly/novelty detection
  - Out-of-distribution example detection
  - Adversarial example recognition and detection
  - Curiosity learning
  - Open-set recognition and adaption

* Deep learning for anomaly/novelty detection/explanation/accommodation
  - Feature learning specifically designed for anomalies/novelties
  - End-to-end anomaly/novelty recognition/detection/adaption

* Anomaly/novelty-related theories/foundation
  - Mathematical formalization
  - Optimization
  - Generalization bounds and learnability
  - Anomaly/novelty explanation

* Relevant applications
  - Fraud and risk analysis in finance and insurance
  - Disease detection and diagnosis in healthcare
  - Intrusion/malware detection in cybersecurity
  - Malicious activity detection in social networks
  - Misinformation and fake information detection
  - Event detection in video surveillance
  - Safety analysis in AI systems 

#Submission Instructions
We invite three types of submissions, including original research paper (9 pages plus references), demo/short paper (6 pages plus references), visionary papers (6 pages plus references). Submissions must be in PDF format, written in English, and formatted according to the new Standard ACM Conference Proceedings Template available at . All papers will be peer reviewed in single-blind and assessed based on their novelty, technical quality, potential impact, clarity, and reproducibility. All the papers are required to be submitted via EasyChair system at . 

We are committed to a non-archival workshop, and dual submission is allowed. All accepted papers will be made available on the workshop official website. 

#Important Dates
  - Submission Due: May 20, 2021 (23:59 UTC-12)
  - Notification Due: June 10, 2021 (23:59 UTC-12)
  - Camera-ready Due: June 20, 2021 (23:59 UTC-12)

  - Guansong Pang (University of Adelaide, Australia)
  - Jundong Li (University of Virginia, United States)
  - Anton van den Hengel (University of Adelaide, Australia) 
  - Longbing Cao (University of Technology Sydney, Australia ) 
  - Thomas G. Dietterich (Oregon State University, United States)

All questions about submissions can be emailed to