5 papers
Formally Exploring Time-Series Anomaly Detection Evaluation Metrics
Dennis Wagner, Arjun Nair, Billy Joe Franks +24
Undetected anomalies in time series can trigger catastrophic failures in safety-critical systems, such as chemical plant explosions or power grid outages. Although many detection m…
On the Challenges and Opportunities in Generative AI
Laura Manduchi, Clara Meister, Kushagra Pandey +23
The field of deep generative modeling has grown rapidly in the last few years. With the availability of massive amounts of training data coupled with advances in scalable unsupervi…
Model Selection of Anomaly Detectors in the Absence of Labeled Validation Data
Clement Fung, Chen Qiu, Aodong Li +1
Anomaly detection is the task of identifying abnormal samples in large unlabeled datasets. While the advent of foundation models has produced powerful zero-shot anomaly detection m…
Anomaly Detection of Tabular Data Using LLMs
Aodong Li, Yunhan Zhao, Chen Qiu +4
Large language models (LLMs) have shown their potential in long-context understanding and mathematical reasoning. In this paper, we study the problem of using LLMs to detect tabula…
Uncertainty-aware Evaluation of Auxiliary Anomalies with the Expected Anomaly Posterior
Lorenzo Perini, Maja Rudolph, Sabrina Schmedding +1
Anomaly detection is the task of identifying examples that do not behave as expected. Because anomalies are rare and unexpected events, collecting real anomalous examples is often…