2 citations · 3 across the 5 of their papers we have counts for
4 papers · 1 filter
Multi-Objective Optimization of Performance and Interpretability of Tabular Supervised Machine Learning Models
Lennart Schneider, Bernd Bischl, Janek Thomas
We present a model-agnostic framework for jointly optimizing the predictive performance and interpretability of supervised machine learning models for tabular data. Interpretabilit…
MO-DEHB: Evolutionary-based Hyperband for Multi-Objective Optimization
Noor Awad, Ayushi Sharma, Philipp Muller +2
Hyperparameter optimization (HPO) is a powerful technique for automating the tuning of machine learning (ML) models. However, in many real-world applications, accuracy is only one…
Tackling Neural Architecture Search With Quality Diversity Optimization
Lennart Schneider, Florian Pfisterer, Paul Kent +3
Neural architecture search (NAS) has been studied extensively and has grown to become a research field with substantial impact. While classical single-objective NAS searches for th…
mlr Tutorial
Julia Schiffner, Bernd Bischl, Michel Lang +9
This document provides and in-depth introduction to the mlr framework for machine learning experiments in R.