A5102770343- Machine Learning and Data Classification
- Forecasting Techniques and Applications
- Stock Market Forecasting Methods
- Advanced Multi-Objective Optimization Algorithms
- Metaheuristic Optimization Algorithms Research
- Fault Detection and Control Systems
- Anomaly Detection Techniques and Applications
Fraunhofer Institute for Integrated Circuits
2023-2024
Ludwig-Maximilians-Universität München
2023
Hyperparameter optimization constitutes a large part of typical modern machine learning (ML) workflows. This arises from the fact that ML methods and corresponding preprocessing steps often only yield optimal performance when hyperparameters are properly tuned. But in many applications, we not interested optimizing pipelines solely for predictive accuracy; additional metrics or constraints must be considered determining an configuration, resulting multi-objective problem. is neglected...
Hyperparameter optimization constitutes a large part of typical modern machine learning workflows. This arises from the fact that methods and corresponding preprocessing steps often only yield optimal performance when hyperparameters are properly tuned. But in many applications, we not interested optimizing ML pipelines solely for predictive accuracy; additional metrics or constraints must be considered determining an configuration, resulting multi-objective problem. is neglected practice,...
Automated machine learning (AutoML) was formed around the fundamental objectives of automatically and efficiently configuring (ML) workflows, aiding research new ML algorithms, contributing to democratization by making it accessible a broader audience. Over past decade, commendable achievements in AutoML have primarily focused on optimizing predictive performance. This progress, while substantial, raises questions about how well has met its broader, original goals. In this position paper, we...
Publications proposing novel machine learning methods are often primarily rated by exhibited predictive performance on selected problems. In this position paper we argue that alone is not a good indicator for the worth of publication. Using it as such even fosters problems like inefficiencies research community whole and setting wrong incentives researchers. We therefore put out call publication "negative" results, which can help alleviate some these improve scientific output community. To...
Recent years have witnessed tremendously improved efficiency of Automated Machine Learning (AutoML), especially Deep (AutoDL) systems, but recent work focuses on tabular, image, or NLP tasks. So far, little attention has been paid to general AutoDL frameworks for time series forecasting, despite the enormous success in applying different novel architectures such In this paper, we propose an efficient approach joint optimization neural architecture and hyperparameters entire data processing...