347 citations · 809 across the 25 of their papers we have counts for
6 papers · 1 filter
Understanding Concept Identification as Consistent Data Clustering Across Multiple Feature Spaces
Felix Lanfermann, Sebastian Schmitt, Patricia Wollstadt
Identifying meaningful concepts in large data sets can provide valuable insights into engineering design problems. Concept identification aims at identifying non-overlapping groups…
Recent Advances in Bayesian Optimization
Xilu Wang, Yaochu Jin, Sebastian Schmitt +1
Bayesian optimization has emerged at the forefront of expensive black-box optimization due to its data efficiency. Recent years have witnessed a proliferation of studies on the dev…
Stream-based Active Learning with Verification Latency in Non-stationary Environments
Andrea Castellani, Sebastian Schmitt, Barbara Hammer
Data stream classification is an important problem in the field of machine learning. Due to the non-stationary nature of the data where the underlying distribution changes over tim…
Task-Sensitive Concept Drift Detector with Constraint Embedding
Andrea Castellani, Sebastian Schmitt, Barbara Hammer
Detecting drifts in data is essential for machine learning applications, as changes in the statistics of processed data typically has a profound influence on the performance of tra…
Estimating the electrical power output of industrial devices with end-to-end time-series classification in the presence of label noise
Andrea Castellani, Sebastian Schmitt, Barbara Hammer
In complex industrial settings, it is common practice to monitor the operation of machines in order to detect undesired states, adjust maintenance schedules, optimize system perfor…
Real-World Anomaly Detection by using Digital Twin Systems and Weakly-Supervised Learning
Andrea Castellani, Sebastian Schmitt, Stefano Squartini
The continuously growing amount of monitored data in the Industry 4.0 context requires strong and reliable anomaly detection techniques. The advancement of Digital Twin technologie…