The 1st Workshop on
Anomaly and Novelty Detection, Explanation and Accommodation (ANDEA)
in Conjunction with KDD 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.
August 11, 2021: The full program schedule is available at Schedule
July 5, 2021: ANDEA will have two sessions, including 1:00 - 5:00pm on Aug. 15 (GMT+8) and 4:00 - 8:00am on Aug. 16 (GMT+8)
May 19, 2021: ANDEA will include a fascinating panel discussion on "Anomaly and novelty detection: Challenges ahead" given by six well-known leaders in the areas!
May 10, 2021: ANDEA will be featured with a line-up of six exciting keynote talks on diverse techniques and applications!
April 1, 2021: ANDEA 2021 workshop website is up
Submission Due: May 20th, 2021 (23:59 UTC-12)
Notification Due: June 20th, 2021 (23:59 UTC-12)
Camera-ready Due: July 4th, 2021 (23:59 UTC-12)
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. Recognition, detection 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. The successful early detection 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. Specialized techniques have been studied in some of these areas for decades, but recent developments are raising a wide variety of new research questions. First, anomaly detection in deep learning is challenging, because the learned latent representations—while they are sufficient for accurate performance on nominal inputs—often fail to represent anomalies and novelties in a way that allows them to be detected. Second, there is a need to create fundamental theories of novelty that can articulate what anomalies can be detected and how much data and computation is required. Third, detection is only the first step in enabling an AI system to adapt successfully to novelties. AI systems need to be able to characterize the nature of the novelties and then develop both short-term and long-term responses to accommodating them.
This workshop will gather researchers and practitioners from diverse communities and knowledge background to promote the development of fundamental theories, effective algorithms, and novel applications of anomaly and novelty detection, characterization, and adaptation.