123 citations · 419 across the 31 of their papers we have counts for
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Hyperparameter Tuning Cookbook: A guide for scikit-learn, PyTorch, river, and spotPython
Thomas Bartz-Beielstein
This document provides a comprehensive guide to hyperparameter tuning using spotPython for scikit-learn, PyTorch, and river. The first part introduces spotPython's surrogate model-…
PyTorch Hyperparameter Tuning - A Tutorial for spotPython
Thomas Bartz-Beielstein
The goal of hyperparameter tuning (or hyperparameter optimization) is to optimize the hyperparameters to improve the performance of the machine or deep learning model. spotPython (…
Experimental Investigation and Evaluation of Model-based Hyperparameter Optimization
Eva Bartz, Martin Zaefferer, Olaf Mersmann +1
Machine learning algorithms such as random forests or xgboost are gaining more importance and are increasingly incorporated into production processes in order to enable comprehensi…
Surrogate Model Based Hyperparameter Tuning for Deep Learning with SPOT
Thomas Bartz-Beielstein, Frederik Rehbach, Amrita Sen +1
A surrogate model based hyperparameter tuning approach for deep learning is presented. This article demonstrates how the architecture-level parameters (hyperparameters) of deep lea…
An Empirical Approach For Probing the Definiteness of Kernels
Martin Zaefferer, Thomas Bartz-Beielstein, Günter Rudolph
Models like support vector machines or Gaussian process regression often require positive semi-definite kernels. These kernels may be based on distance functions. While definitenes…