Bin Li

According to our database, Bin Li authored at least 13 papers between 2022 and 2024.

Collaborative distances:

• Dijkstra number of five.
• Erdős number of five.

2024

Unsupervised temporal anomaly detection: Time series, data stream, and interpretability.

Bin Li

PhD thesis, 2024

State-transition-aware anomaly detection under concept drifts.

Bin Li, Shubham Gupta, Emmanuel Müller

Data Knowl. Eng., 2024

Cohesive Explanation for Time Series Prediction.

Bin Li, Emmanuel Müller

Proceedings of the International Joint Conference on Neural Networks, 2024

2023

On the Consistency and Robustness of Saliency Explanations for Time Series Classification.

Chiara Balestra, Bin Li, Emmanuel Müller

CoRR, 2023

Prototypes as Explanation for Time Series Anomaly Detection.

Bin Li, Carsten Jentsch, Emmanuel Müller

CoRR, 2023

The Good, The Bad, and The Average: Benchmarking of Reconstruction Based Multivariate Time Series Anomaly Detection.

Arn Baudzus, Bin Li, Adnane Jadid, Emmanuel Müller

Proceedings of the Machine Learning and Knowledge Discovery in Databases: Applied Data Science and Demo Track, 2023

Contrastive Time Series Anomaly Detection by Temporal Transformations.

Bin Li, Emmanuel Müller

Proceedings of the International Joint Conference on Neural Networks, 2023

slidSHAPs - sliding Shapley Values for correlation-based change detection in time series.

Chiara Balestra, Bin Li, Emmanuel Müller

Proceedings of the 10th IEEE International Conference on Data Science and Advanced Analytics, 2023

State-Transition-Aware Anomaly Detection Under Concept Drifts.

Bin Li, Emmanuel Müller

Proceedings of the Big Data Analytics and Knowledge Discovery, 2023

On Model Performance Estimation in Time Series Anomaly Detection.

Arn Baudzus, Bin Li, Adnane Jadid, Emmanuel Müller

Proceedings of the 2023 6th International Conference on Computational Intelligence and Intelligent Systems, 2023

2022

STAD: State-Transition-Aware Anomaly Detection Under Concept Drifts.

Bin Li, Emmanuel Müller

Proceedings of the First Workshop on Online Learning from Uncertain Data Streams (OLUD 2022) co-located with IEEE World Congress on Computational Intelligence (WCCI 2022), 2022

ADEPT: Anomaly Detection, Explanation and Processing for Time Series with a Focus on Energy Consumption Data.

Benedikt Tobias Müller, Marvin Ender, Jan Erik Swiadek, Mengcheng Jin, Simon Winkel, Dominik Niedziela, Bin Li, Jelle Hüntelmann, Emmanuel Müller

Proceedings of the Machine Learning and Knowledge Discovery in Databases, 2022

Reconstruction-based unsupervised drift detection over multivariate streaming data.

Daniil Kaminskyi, Bin Li, Emmanuel Müller

Proceedings of the IEEE International Conference on Data Mining Workshops, 2022