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Grouped Feature Importance and Combined Features Effect Plot


Apr 23, 2021
Quay Au, Julia Herbinger, Clemens Stachl, Bernd Bischl, Giuseppe Casalicchio


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


Apr 06, 2021
David R├╝gamer, Ruolin Shen, Christina Bukas, Lisa Barros de Andrade e Sousa, Dominik Thalmeier, Nadja Klein, Chris Kolb, Florian Pfisterer, Philipp Kopper, Bernd Bischl, Christian L. M├╝ller


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


Apr 01, 2021
Florian Pargent, Florian Pfisterer, Janek Thomas, Bernd Bischl


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Deep Semi-Supervised Learning for Time Series Classification


Feb 06, 2021
Jann Goschenhofer, Rasmus Hvingelby, David R├╝gamer, Janek Thomas, Moritz Wagner, Bernd Bischl


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Semi-Structured Deep Piecewise Exponential Models


Nov 11, 2020
Philipp Kopper, Sebastian P├Âlsterl, Christian Wachinger, Bernd Bischl, Andreas Bender, David R├╝gamer

* 8 pages, 3 figures 

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Debiasing classifiers: is reality at variance with expectation?


Nov 04, 2020
Ashrya Agrawal, Florian Pfisterer, Bernd Bischl, Jiahao Chen, Srijan Sood, Sameena Shah, Francois Buet-Golfouse, Bilal A Mateen, Sebastian Vollmer

* 11 pages, under review 

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Interpretable Machine Learning -- A Brief History, State-of-the-Art and Challenges


Oct 19, 2020
Christoph Molnar, Giuseppe Casalicchio, Bernd Bischl


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Neural Mixture Distributional Regression


Oct 14, 2020
David R├╝gamer, Florian Pfisterer, Bernd Bischl


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Symplectic Gaussian Process Regression of Hamiltonian Flow Maps


Sep 11, 2020
Katharina Rath, Christopher G. Albert, Bernd Bischl, Udo von Toussaint

* 24 pages, 9 figures 

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mlr3proba: Machine Learning Survival Analysis in R


Aug 18, 2020
Raphael Sonabend, Franz J. Király, Andreas Bender, Bernd Bischl, Michel Lang

* Submitted to JMLR 

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Relative Feature Importance


Jul 16, 2020
Gunnar K├Ânig, Christoph Molnar, Bernd Bischl, Moritz Grosse-Wentrup


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Pitfalls to Avoid when Interpreting Machine Learning Models


Jul 08, 2020
Christoph Molnar, Gunnar K├Ânig, Julia Herbinger, Timo Freiesleben, Susanne Dandl, Christian A. Scholbeck, Giuseppe Casalicchio, Moritz Grosse-Wentrup, Bernd Bischl

* This article was accepted at the ICML 2020 workshop XXAI: Extending Explainable AI Beyond Deep Models and Classifiers (see http://interpretable-ml.org/icml2020workshop/

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A General Machine Learning Framework for Survival Analysis


Jun 27, 2020
Andreas Bender, David R├╝gamer, Fabian Scheipl, Bernd Bischl

* Accepted at ECML PKDD 2020, Research Track 

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Model-agnostic Feature Importance and Effects with Dependent Features -- A Conditional Subgroup Approach


Jun 08, 2020
Christoph Molnar, Gunnar K├Ânig, Bernd Bischl, Giuseppe Casalicchio


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Multi-Objective Counterfactual Explanations


Apr 23, 2020
Susanne Dandl, Christoph Molnar, Martin Binder, Bernd Bischl


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Multi-Objective Hyperparameter Tuning and Feature Selection using Filter Ensembles


Feb 13, 2020
Martin Binder, Julia Moosbauer, Janek Thomas, Bernd Bischl


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Model-Agnostic Approaches to Multi-Objective Simultaneous Hyperparameter Tuning and Feature Selection


Dec 30, 2019
Martin Binder, Julia Moosbauer, Janek Thomas, Bernd Bischl


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


Nov 18, 2019
Florian Pfisterer, Laura Beggel, Xudong Sun, Fabian Scheipl, Bernd Bischl


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Towards Human Centered AutoML


Nov 06, 2019
Florian Pfisterer, Janek Thomas, Bernd Bischl

* 4 pages 

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Tutorial and Survey on Probabilistic Graphical Model and Variational Inference in Deep Reinforcement Learning


Oct 04, 2019
Xudong Sun, Bernd Bischl


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Multi-Objective Automatic Machine Learning with AutoxgboostMC


Aug 28, 2019
Florian Pfisterer, Stefan Coors, Janek Thomas, Bernd Bischl

* Accepted at ECMLPKDD WORKSHOP ON AUTOMATING DATA SCIENCE 

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An Open Source AutoML Benchmark


Jul 01, 2019
Pieter Gijsbers, Erin LeDell, Janek Thomas, S├ębastien Poirier, Bernd Bischl, Joaquin Vanschoren

* Accepted paper at the AutoML Workshop at ICML 2019. Code: https://github.com/openml/automlbenchmark/ Accompanying website: https://openml.github.io/automlbenchmark/ 

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Resampling-based Assessment of Robustness to Distribution Shift for Deep Neural Networks


Jun 07, 2019
Xudong Sun, Yu Wang, Alexej Gossmann, Bernd Bischl


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Wearable-based Parkinson's Disease Severity Monitoring using Deep Learning


Apr 24, 2019
Jann Goschenhofer, Franz MJ Pfister, Kamer Ali Yuksel, Bernd Bischl, Urban Fietzek, Janek Thomas


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ReinBo: Machine Learning pipeline search and configuration with Bayesian Optimization embedded Reinforcement Learning


Apr 10, 2019
Xudong Sun, Jiali Lin, Bernd Bischl


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Sampling, Intervention, Prediction, Aggregation: A Generalized Framework for Model Agnostic Interpretations


Apr 08, 2019
Christian A. Scholbeck, Christoph Molnar, Christian Heumann, Bernd Bischl, Giuseppe Casalicchio


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Component-Wise Boosting of Targets for Multi-Output Prediction


Apr 08, 2019
Quay Au, Daniel Schalk, Giuseppe Casalicchio, Ramona Schoedel, Clemens Stachl, Bernd Bischl


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Quantifying Interpretability of Arbitrary Machine Learning Models Through Functional Decomposition


Apr 08, 2019
Christoph Molnar, Giuseppe Casalicchio, Bernd Bischl


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


Feb 24, 2019
Xudong Sun, Andrea Bommert, Florian Pfisterer, J├Ârg Rahnenf├╝hrer, Michel Lang, Bernd Bischl


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