Research

Accurate or Interpretable? Both! Joining Accuracy and Interpretability Evaluation Measures for Time Series Anomaly Detection

Doan, T. T. H., Sylligardos, E., et al.

Breaking the Model Selection Barrier? Three Learning Paradigms for Supervised Ensembling in Time Series Anomaly Detection

Rozzio, A., Sylligardos, E., Boniol, P.

Toward a Scalable and Adaptive Evaluation Measure for Time Series Anomaly Detection

Sylligardos, E., Huang, M., Paparrizos, J., Senellart, P., Boniol, P.

Pyxida: What Do Time Series Anomaly Detection Benchmarks Really Say?

Sylligardos, E., Stanzione, R., Paparrizos, J., Senellart, P., Boniol, P.

AutoVUS

Sylligardos, E., Huang, M., Paparrizos, J., Senellart, P., Boniol, P.

MSAD: A Deep Dive into Model Selection for Time series Anomaly Detection

Sylligardos, E., Paparrizos, J., Palpanas, T., Senellart, P., Boniol, P.

Interpretable Multivariate Anomaly Detector Selection for Automatic Marine Data Quality Control

Nguyen, N.-T., Skålvik, A. M., Sylligardos, E., et al.

Fast or Accurate? Rethinking Time Series Anomaly Detection

Sylligardos, E.

ADecimo: Model Selection for Time Series Anomaly Detection

Boniol, P., Sylligardos, E., et al.

Choose Wisely: An Extensive Evaluation of Model Selection for Anomaly Detection in Time Series

Sylligardos, E., et al.

Neural Network-Enabled Identification of Weak Inspiratory Efforts during Pressure Support Ventilation Using Ventilator Waveforms

Soundoulounaki, S., Sylligardos, E., et al.

A Deep Learning Approach to Detect Ventilatory Over-Assistance

Sylligardos, E., et al.

Social robots and Digital humans as Job Interviewers: A study of human reactions towards a more natural interaction

Baka, E., Mishra, N., Sylligardos, E., et al.