36 citations · 173 across the 61 of their papers we have counts for
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Automated Benchmark-Driven Design and Explanation of Hyperparameter Optimizers
Julia Moosbauer, Martin Binder, Lennart Schneider +5
Automated hyperparameter optimization (HPO) has gained great popularity and is an important ingredient of most automated machine learning frameworks. The process of designing HPO a…
Towards modelling hazard factors in unstructured data spaces using gradient-based latent interpolation
Tobias Weber, Michael Ingrisch, Bernd Bischl +1
The application of deep learning in survival analysis (SA) allows utilizing unstructured and high-dimensional data types uncommon in traditional survival methods. This allows to ad…
Survival-oriented embeddings for improving accessibility to complex data structures
Tobias Weber, Michael Ingrisch, Matthias Fabritius +2
Deep learning excels in the analysis of unstructured data and recent advancements allow to extend these techniques to survival analysis. In the context of clinical radiology, this…
Explaining Hyperparameter Optimization via Partial Dependence Plots
Julia Moosbauer, Julia Herbinger, Giuseppe Casalicchio +2
Automated hyperparameter optimization (HPO) can support practitioners to obtain peak performance in machine learning models. However, there is often a lack of valuable insights int…
Joint Debiased Representation Learning and Imbalanced Data Clustering
Mina Rezaei, Emilio Dorigatti, David Ruegamer +1
One of the most promising approaches for unsupervised learning is combining deep representation learning and deep clustering. Some recent works propose to simultaneously learn repr…