20 papers
mlr3mbo: Bayesian Optimization in R
Marc Becker, Lennart Schneider, Martin Binder +2
We present mlr3mbo, a modular toolbox for Bayesian optimization in R. mlr3mbo supports single- and multi-objective optimization, multi-point proposals, batch and asynchronous paral…
mlr3torch: A Deep Learning Framework in R based on mlr3 and torch
Sebastian Fischer, Lukas Burk, Carson Zhang +2
Deep learning (DL) has become a cornerstone of modern machine learning (ML) praxis. We introduce the R package mlr3torch, which is an extensible DL framework for the mlr3 ecosystem…
Smoothing the Edges: Smooth Optimization for Sparse Regularization using Hadamard Overparametrization
Chris Kolb, Christian L. Müller, Bernd Bischl +1
We present a framework for smooth optimization of explicitly regularized objectives for (structured) sparsity. These non-smooth and possibly non-convex problems typically rely on s…
Analyzing Error Sources in Global Feature Effect Estimation
Timo HeiÃ, Coco Bögel, Bernd Bischl +1
Global feature effects such as partial dependence (PD) and accumulated local effects (ALE) plots are widely used to interpret black-box models. However, they are only estimates of…
CASHomon Sets: Efficient Rashomon Sets Across Multiple Model Classes and their Hyperparameters
Fiona Katharina Ewald, Martin Binder, Matthias Feurer +2
Rashomon sets are model sets within one model class that perform nearly as well as a reference model from the same model class. They reveal the existence of alternative well-perfor…
xplainfi: Feature Importance and Statistical Inference for Machine Learning in R
Lukas Burk, Fiona Katharina Ewald, Giuseppe Casalicchio +2
We introduce xplainfi, an R package built on top of the mlr3 ecosystem for global, loss-based feature importance methods for machine learning models. Various feature importance met…