6 papers · 1 filter
Multi-Task Optimization over Networks of Tasks
Julian Hatzky, Thomas Bartz-Beielstein, A. E. Eiben +1
Multi-task optimization is a powerful approach for solving a large number of tasks in parallel. However, existing algorithms face distinct limitations: Population-based methods sca…
Optimization with SpotOptim
Thomas Bartz-Beielstein
The spotoptim package implements surrogate-model-based optimization of expensive black-box functions in Python. Building on two decades of Sequential Parameter Optimization (SPO) m…
Tuning for Trustworthiness -- Balancing Performance and Explanation Consistency in Neural Network Optimization
Alexander Hinterleitner, Thomas Bartz-Beielstein
Despite the growing interest in Explainable Artificial Intelligence (XAI), explainability is rarely considered during hyperparameter tuning or neural architecture optimization, whe…
Bed-Attached Vibration Sensor System: A Machine Learning Approach for Fall Detection in Nursing Homes
Thomas Bartz-Beielstein, Axel Wellendorf, Noah Pütz +6
The increasing shortage of nursing staff and the acute risk of falls in nursing homes pose significant challenges for the healthcare system. This study presents the development of…
Enhancing Feature Selection and Interpretability in AI Regression Tasks Through Feature Attribution
Alexander Hinterleitner, Thomas Bartz-Beielstein, Richard Schulz +3
Research in Explainable Artificial Intelligence (XAI) is increasing, aiming to make deep learning models more transparent. Most XAI methods focus on justifying the decisions made b…
Simplifying Hyperparameter Tuning in Online Machine Learning -- The spotRiverGUI
Thomas Bartz-Beielstein
Batch Machine Learning (BML) reaches its limits when dealing with very large amounts of streaming data. This is especially true for available memory, handling drift in data streams…