Submission Instructions

We welcome full paper submissions. The papers must be no longer than 9 pages in total: 7 pages for the body of the paper and 2 pages for references. Please submit at the following CMT website:
ADFM 2024 CMT submission website. 

The paper submissions must be in pdf format and use the IJCAI 2024 official templates. All submissions must be anonymous and conform to the IJCAI 2024 standards for double-blind review. The accepted papers will be posted on the workshop website and will not appear in the IJCAI 2024 proceedings. IJCAI 2024 workshop chairs enforce that at least one author of each accepted paper must travel to the IJCAI venue in person and present the paper, and that multiple submissions of the same paper to more IJCAI workshops are forbidden. Failure to comply with the aforesaid rules may cause the paper to be removed from the workshop program.  

Submission deadline: April 26 May 10, 2024 AOE Time  
Notification to authors: May 31, 2024
Camera ready deadline:  June 7, 2024 AOE Time

We invite the submission of original and high-quality research papers in the topics related to anomaly detection with foundation models. Each accepted paper will be presented as either an oral, spotlight, or poster presentation. 

Topics

The topics for ADFM 2024 include, but are not limited to:

• Foundations and principles of foundation models for anomaly detection.
• Innovative anomaly detection algorithms and frameworks leveraging foundation models.
• Application-specific anomaly detection using foundation models, including but not limited to finance, healthcare, cybersecurity, and industrial systems.
• Evaluation metrics and benchmarks for assessing anomaly detection in foundation models.
• Techniques for enhancing the explainability and interpretability of foundation models in anomaly detection tasks.
• Strategies for ensuring fairness and reducing bias in anomaly detection with foundation models.
• Privacy-preserving methods in anomaly detection using foundation models.
• Trustworthiness and reliability of foundation models in critical anomaly detection applications.
• Cross-disciplinary approaches for improving anomaly detection, incorporating insights from other fields such as psychology and sociology.
• Incremental learning and adaptation mechanisms for foundation models in dynamic environments.
• Integration of domain knowledge and expert systems with foundation models for improved anomaly detection.
• Exploring the limitations and challenges of current foundation models in detecting anomalies in complex and noisy data.
• Forward-looking perspectives on the evolution of anomaly detection methodologies with the advancement of foundation models.