research
∙
07/07/2023
Smoothing the Edges: A General Framework for Smooth Optimization in Sparse Regularization using Hadamard Overparametrization
This paper introduces a smooth method for (structured) sparsity in ℓ_q a...
share
research
∙
04/13/2023
counterfactuals: An R Package for Counterfactual Explanation Methods
Counterfactual explanation methods provide information on how feature va...
research
∙
12/08/2022
Mind the Gap: Measuring Generalization Performance Across Multiple Objectives
Modern machine learning models are often constructed taking into account...
research
∙
09/14/2022
Improved proteasomal cleavage prediction with positive-unlabeled learning
Accurate in silico modeling of the antigen processing pathway is crucial...
research
∙
06/15/2022
Multi-Objective Hyperparameter Optimization – An Overview
Hyperparameter optimization constitutes a large part of typical modern m...
research
∙
07/13/2021
Hyperparameter Optimization: Foundations, Algorithms, Best Practices and Open Challenges
Most machine learning algorithms are configured by one or several hyperp...
research
∙
04/06/2021
deepregression: a Flexible Neural Network Framework for Semi-Structured Deep Distributional Regression
This paper describes the implementation of semi-structured deep distribu...
research
∙
10/19/2020
Interpretable Machine Learning – A Brief History, State-of-the-Art and Challenges
We present a brief history of the field of interpretable machine learnin...
research
∙
07/08/2020
Pitfalls to Avoid when Interpreting Machine Learning Models
Modern requirements for machine learning (ML) models include both high p...
research
∙
04/23/2020
Multi-Objective Counterfactual Explanations
Counterfactual explanations are one of the most popular methods to make ...
research
∙
12/30/2019
Model-Agnostic Approaches to Multi-Objective Simultaneous Hyperparameter Tuning and Feature Selection
Highly non-linear machine learning algorithms have the capacity to handl...
research
∙
11/18/2019
Benchmarking time series classification – Functional data vs machine learning approaches
Time series classification problems have drawn increasing attention in t...
research
∙
04/08/2019
Quantifying Interpretability of Arbitrary Machine Learning Models Through Functional Decomposition
To obtain interpretable machine learning models, either interpretable mo...
research
∙
06/08/2015
