5 citations · 6 across the 8 of their papers we have counts for
8 papers
Provably Reduced Sample Cost in Prior-Guided Hyperparameter Optimization
Leona Hennig, Jasmin Brandt, Lukas Fehring +3
Large-scale hyperparameter optimization (HPO) in automated machine learning (AutoML) consumes substantial computational resources, raising growing concerns about scalability and en…
Evolutionary Mapping of Neural Networks to Spatial Accelerators
Alessandro Pierro, Jonathan Timcheck, Jason Yik +3
Spatial accelerators, composed of arrays of compute-memory integrated units, offer an attractive platform for deploying inference workloads with low latency and low energy consumpt…
Dynamic Hyperparameter Importance for Efficient Multi-Objective Optimization
Daphne Theodorakopoulos, Marcel Wever, Marius Lindauer
Choosing a suitable ML model is a complex task that can depend on several objectives, e.g., accuracy, fairness, or energy consumption. In practice, this requires trading off multip…
DeepCAVE: A Visualization and Analysis Tool for Automated Machine Learning
Sarah Segel, Helena Graf, Edward Bergman +5
Hyperparameter optimization (HPO), as a central paradigm of AutoML, is crucial for leveraging the full potential of machine learning (ML) models; yet its complexity poses challenge…
HyperSHAP: Shapley Values and Interactions for Explaining Hyperparameter Optimization
Marcel Wever, Maximilian Muschalik, Fabian Fumagalli +1
Hyperparameter optimization (HPO) is a crucial step in achieving strong predictive performance. Yet, the impact of individual hyperparameters on model generalization is highly cont…
Hyperparameter Importance Analysis for Multi-Objective AutoML
Daphne Theodorakopoulos, Frederic Stahl, Marius Lindauer
Hyperparameter optimization plays a pivotal role in enhancing the predictive performance and generalization capabilities of ML models. However, in many applications, we do not only…
Towards Leveraging AutoML for Sustainable Deep Learning: A Multi-Objective HPO Approach on Deep Shift Neural Networks
Leona Hennig, Tanja Tornede, Marius Lindauer
Deep Learning (DL) has advanced various fields by extracting complex patterns from large datasets. However, the computational demands of DL models pose environmental and resource c…
Interactive Hyperparameter Optimization in Multi-Objective Problems via Preference Learning
Joseph Giovanelli, Alexander Tornede, Tanja Tornede +1
Hyperparameter optimization (HPO) is important to leverage the full potential of machine learning (ML). In practice, users are often interested in multi-objective (MO) problems, i.…