Florian Pfisterer | DeepAI

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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...

0 Jan Simson, et al. ∙

∙ 03/15/2023

Can Fairness be Automated? Guidelines and Opportunities for Fairness-aware AutoML

The field of automated machine learning (AutoML) introduces techniques t...

19 Hilde Weerts, et al. ∙

∙ 12/08/2022

Mind the Gap: Measuring Generalization Performance Across Multiple Objectives

Modern machine learning models are often constructed taking into account...

0 Matthias Feurer, et al. ∙

∙ 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...

0 David Rügamer, et al. ∙

∙ 07/30/2022

Tackling Neural Architecture Search With Quality Diversity Optimization

Neural architecture search (NAS) has been studied extensively and has gr...

0 Lennart Schneider, et al. ∙

∙ 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...

0 Moritz Herrmann, et al. ∙

∙ 06/15/2022

Multi-Objective Hyperparameter Optimization – An Overview

Hyperparameter optimization constitutes a large part of typical modern m...

0 Florian Karl, et al. ∙

∙ 05/26/2022

Flexible Group Fairness Metrics for Survival Analysis

Algorithmic fairness is an increasingly important field concerned with d...

0 Raphael Sonabend, et al. ∙

∙ 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...

0 Lennart Schneider, et al. ∙

∙ 11/29/2021

Automated Benchmark-Driven Design and Explanation of Hyperparameter Optimizers

Automated hyperparameter optimization (HPO) has gained great popularity ...

0 Julia Moosbauer, et al. ∙

∙ 09/08/2021

YAHPO Gym – Design Criteria and a new Multifidelity Benchmark for Hyperparameter Optimization

When developing and analyzing new hyperparameter optimization (HPO) meth...

0 Florian Pfisterer, et al. ∙

∙ 07/04/2021

Mutation is all you need

Neural architecture search (NAS) promises to make deep learning accessib...

0 Lennart Schneider, et al. ∙

∙ 06/10/2021

Meta-Learning for Symbolic Hyperparameter Defaults

Hyperparameter optimization in machine learning (ML) deals with the prob...

0 Pieter Gijsbers, et al. ∙

∙ 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...

0 David Rügamer, et al. ∙

∙ 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...

0 Florian Pargent, et al. ∙

∙ 11/04/2020

Debiasing classifiers: is reality at variance with expectation?

Many methods for debiasing classifiers have been proposed, but their eff...

0 Ashrya Agrawal, et al. ∙

∙ 10/14/2020

Neural Mixture Distributional Regression

We present neural mixture distributional regression (NMDR), a holistic f...

8 David Rügamer, et al. ∙

∙ 11/18/2019

Benchmarking time series classification – Functional data vs machine learning approaches

Time series classification problems have drawn increasing attention in t...

0 Florian Pfisterer, et al. ∙

∙ 11/06/2019

Towards Human Centered AutoML

Building models from data is an integral part of the majority of data sc...

0 Florian Pfisterer, et al. ∙

∙ 08/28/2019

Multi-Objective Automatic Machine Learning with AutoxgboostMC

AutoML systems are currently rising in popularity, as they can build pow...

0 Florian Pfisterer, et al. ∙

∙ 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...

0 Xudong Sun, et al. ∙

∙ 11/23/2018

Learning Multiple Defaults for Machine Learning Algorithms

The performance of modern machine learning methods highly depends on the...

0 Florian Pfisterer, et al. ∙

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