4 citations · 4 across the 11 of their papers we have counts for
12 papers
Exposing Blind Spots in Deep Imbalanced Regression Evaluation
Noah C. Puetz, Jens U. Brandt, Marc Hilbert +3
Deep Imbalanced Regression (DIR) addresses a common failure mode of regression models: target distributions are highly non-uniform, causing models to perform best in densely popula…
LLMs taking shortcuts in test generation: A study with SAP HANA and LevelDB
Vekil Bekmyradov, Noah C. Pütz, Thomas Bartz-Beielstein
Large Language Models (LLMs) have achieved impressive results on public benchmarks, often leading to claims of advanced reasoning and understanding. However, recent research in cog…
Physics-based Approximation and Prediction of Speedlines in Compressor Performance Maps
Abdul-Malik Akiev, Danyal Ergür, Alexander Schirger +3
Speedlines in compressor performance maps (CPMs) are critical for understanding and predicting compressor behavior under various operating conditions. We investigate a physics-base…
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…
MuViS: Multimodal Virtual Sensing Benchmark
Jens U. Brandt, Noah C. Puetz, Jobel Jose George +5
Virtual sensing aims to infer hard-to-measure quantities from accessible measurements and is central to perception and control in physical systems. Despite rapid progress from firs…
Multi-Objective Optimization with Desirability and Morris-Mitchell Criterion
Thomas Bartz-Beielstein, Eva Bartz, Alexander Hinterleitner +2
Industrial experimental designs frequently lack optimal space-filling properties, rendering them unrepresentative. This study presents a comprehensive methodology to refine existin…
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…
Multi-Objective Optimization and Hyperparameter Tuning With Desirability Functions
Thomas Bartz-Beielstein
The desirability-function approach is a widely adopted method for optimizing multiple-response processes. Kuhn (2016) implemented the packages desirability and desirability2 in the…
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…