Search Results for author: Florian Pfisterer

Found 22 papers, 12 papers with code

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

no code implementations15 Mar 2023 Hilde Weerts, Florian Pfisterer, Matthias Feurer, Katharina Eggensperger, Edward Bergman, Noor Awad, Joaquin Vanschoren, Mykola Pechenizkiy, Bernd Bischl, Frank Hutter

The field of automated machine learning (AutoML) introduces techniques that automate parts of the development of machine learning (ML) systems, accelerating the process and reducing barriers for novices.

AutoML Fairness

Mind the Gap: Measuring Generalization Performance Across Multiple Objectives

1 code implementation8 Dec 2022 Matthias Feurer, Katharina Eggensperger, Edward Bergman, Florian Pfisterer, Bernd Bischl, Frank Hutter

Modern machine learning models are often constructed taking into account multiple objectives, e. g., minimizing inference time while also maximizing accuracy.

Hyperparameter Optimization

Tackling Neural Architecture Search With Quality Diversity Optimization

1 code implementation30 Jul 2022 Lennart Schneider, Florian Pfisterer, Paul Kent, Juergen Branke, Bernd Bischl, Janek Thomas

Although considerable progress has been made in the field of multi-objective NAS, we argue that there is some discrepancy between the actual optimization problem of practical interest and the optimization problem that multi-objective NAS tries to solve.

Neural Architecture Search

Automated Benchmark-Driven Design and Explanation of Hyperparameter Optimizers

1 code implementation29 Nov 2021 Julia Moosbauer, Martin Binder, Lennart Schneider, Florian Pfisterer, Marc Becker, Michel Lang, Lars Kotthoff, Bernd Bischl

Automated hyperparameter optimization (HPO) has gained great popularity and is an important ingredient of most automated machine learning frameworks.

Bayesian Optimization Hyperparameter Optimization

YAHPO Gym -- An Efficient Multi-Objective Multi-Fidelity Benchmark for Hyperparameter Optimization

1 code implementation8 Sep 2021 Florian Pfisterer, Lennart Schneider, Julia Moosbauer, Martin Binder, Bernd Bischl

When developing and analyzing new hyperparameter optimization methods, it is vital to empirically evaluate and compare them on well-curated benchmark suites.

Hyperparameter Optimization

Mutation is all you need

no code implementations ICML Workshop AutoML 2021 Lennart Schneider, Florian Pfisterer, Martin Binder, Bernd Bischl

Neural architecture search (NAS) promises to make deep learning accessible to non-experts by automating architecture engineering of deep neural networks.

Bayesian Optimization Neural Architecture Search

Meta-Learning for Symbolic Hyperparameter Defaults

1 code implementation10 Jun 2021 Pieter Gijsbers, Florian Pfisterer, Jan N. van Rijn, Bernd Bischl, Joaquin Vanschoren

Hyperparameter optimization in machine learning (ML) deals with the problem of empirically learning an optimal algorithm configuration from data, usually formulated as a black-box optimization problem.

Hyperparameter Optimization Meta-Learning

deepregression: a Flexible Neural Network Framework for Semi-Structured Deep Distributional Regression

2 code implementations6 Apr 2021 David Rügamer, Chris Kolb, Cornelius Fritz, Florian Pfisterer, Philipp Kopper, Bernd Bischl, Ruolin Shen, Christina Bukas, Lisa Barros de Andrade e Sousa, Dominik Thalmeier, Philipp Baumann, Lucas Kook, Nadja Klein, Christian L. Müller

In this paper we describe the implementation of semi-structured deep distributional regression, a flexible framework to learn conditional distributions based on the combination of additive regression models and deep networks.

regression

Regularized target encoding outperforms traditional methods in supervised machine learning with high cardinality features

2 code implementations1 Apr 2021 Florian Pargent, Florian Pfisterer, Janek Thomas, Bernd Bischl

Since most machine learning (ML) algorithms are designed for numerical inputs, efficiently encoding categorical variables is a crucial aspect in data analysis.

BIG-bench Machine Learning

Debiasing classifiers: is reality at variance with expectation?

no code implementations4 Nov 2020 Ashrya Agrawal, Florian Pfisterer, Bernd Bischl, Francois Buet-Golfouse, Srijan Sood, Jiahao Chen, Sameena Shah, Sebastian Vollmer

We present an empirical study of debiasing methods for classifiers, showing that debiasers often fail in practice to generalize out-of-sample, and can in fact make fairness worse rather than better.

Fairness

Neural Mixture Distributional Regression

no code implementations14 Oct 2020 David Rügamer, Florian Pfisterer, Bernd Bischl

We present neural mixture distributional regression (NMDR), a holistic framework to estimate complex finite mixtures of distributional regressions defined by flexible additive predictors.

regression

Benchmarking time series classification -- Functional data vs machine learning approaches

1 code implementation18 Nov 2019 Florian Pfisterer, Laura Beggel, Xudong Sun, Fabian Scheipl, Bernd Bischl

In order to assess the methods and implementations, we run a benchmark on a wide variety of representative (time series) data sets, with in-depth analysis of empirical results, and strive to provide a reference ranking for which method(s) to use for non-expert practitioners.

Additive models Benchmarking +6

Towards Human Centered AutoML

no code implementations6 Nov 2019 Florian Pfisterer, Janek Thomas, Bernd Bischl

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

AutoML Position

Multi-Objective Automatic Machine Learning with AutoxgboostMC

no code implementations28 Aug 2019 Florian Pfisterer, Stefan Coors, Janek Thomas, Bernd Bischl

AutoML systems are currently rising in popularity, as they can build powerful models without human oversight.

AutoML BIG-bench Machine Learning +1

High Dimensional Restrictive Federated Model Selection with multi-objective Bayesian Optimization over shifted distributions

1 code implementation24 Feb 2019 Xudong Sun, Andrea Bommert, Florian Pfisterer, Jörg Rahnenführer, Michel Lang, Bernd Bischl

To carry out a clinical research under this scenario, an analyst could train a machine learning model only on local data site, but it is still possible to execute a statistical query at a certain cost in the form of sending a machine learning model to some of the remote data sites and get the performance measures as feedback, maybe due to prediction being usually much cheaper.

Bayesian Optimization BIG-bench Machine Learning +2

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