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08/31/2023
Everything, Everywhere All in One Evaluation: Using Multiverse Analysis to Evaluate the Influence of Model Design Decisions on Algorithmic Fairness
A vast number of systems across the world use algorithmic decision makin...
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03/15/2023
Can Fairness be Automated? Guidelines and Opportunities for Fairness-aware AutoML
The field of automated machine learning (AutoML) introduces techniques t...
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12/08/2022
Mind the Gap: Measuring Generalization Performance Across Multiple Objectives
Modern machine learning models are often constructed taking into account...
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11/17/2022
Mixture of Experts Distributional Regression: Implementation Using Robust Estimation with Adaptive First-order Methods
In this work, we propose an efficient implementation of mixtures of expe...
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07/30/2022
Tackling Neural Architecture Search With Quality Diversity Optimization
Neural architecture search (NAS) has been studied extensively and has gr...
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07/01/2022
A geometric framework for outlier detection in high-dimensional data
Outlier or anomaly detection is an important task in data analysis. We d...
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06/15/2022
Multi-Objective Hyperparameter Optimization – An Overview
Hyperparameter optimization constitutes a large part of typical modern m...
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05/26/2022
Flexible Group Fairness Metrics for Survival Analysis
Algorithmic fairness is an increasingly important field concerned with d...
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04/28/2022
A Collection of Quality Diversity Optimization Problems Derived from Hyperparameter Optimization of Machine Learning Models
The goal of Quality Diversity Optimization is to generate a collection o...
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11/29/2021
Automated Benchmark-Driven Design and Explanation of Hyperparameter Optimizers
Automated hyperparameter optimization (HPO) has gained great popularity ...
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09/08/2021
YAHPO Gym – Design Criteria and a new Multifidelity Benchmark for Hyperparameter Optimization
When developing and analyzing new hyperparameter optimization (HPO) meth...
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07/04/2021
Mutation is all you need
Neural architecture search (NAS) promises to make deep learning accessib...
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06/10/2021
Meta-Learning for Symbolic Hyperparameter Defaults
Hyperparameter optimization in machine learning (ML) deals with the prob...
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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...
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04/01/2021
Regularized target encoding outperforms traditional methods in supervised machine learning with high cardinality features
Because most machine learning (ML) algorithms are designed for numerical...
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11/04/2020
Debiasing classifiers: is reality at variance with expectation?
Many methods for debiasing classifiers have been proposed, but their eff...
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10/14/2020
Neural Mixture Distributional Regression
We present neural mixture distributional regression (NMDR), a holistic f...
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11/18/2019
Benchmarking time series classification – Functional data vs machine learning approaches
Time series classification problems have drawn increasing attention in t...
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11/06/2019
Towards Human Centered AutoML
Building models from data is an integral part of the majority of data sc...
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08/28/2019
Multi-Objective Automatic Machine Learning with AutoxgboostMC
AutoML systems are currently rising in popularity, as they can build pow...
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02/24/2019
High Dimensional Restrictive Federated Model Selection with multi-objective Bayesian Optimization over shifted distributions
A novel machine learning optimization process coined Restrictive Federat...
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11/23/2018
