Get our free extension to see links to code for papers anywhere online!

 Add to Chrome

 Add to Firefox

CatalyzeX Code Finder - Browser extension linking code for ML papers across the web! | Product Hunt Embed
Semi-Structured Deep Piecewise Exponential Models

Nov 11, 2020
Philipp Kopper, Sebastian P√∂lsterl, Christian Wachinger, Bernd Bischl, Andreas Bender, David R√ľgamer

* 8 pages, 3 figures 

  Access Paper or Ask Questions

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 

  Access Paper or Ask Questions

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

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


  Access Paper or Ask Questions

Neural Mixture Distributional Regression

Oct 14, 2020
David R√ľgamer, Florian Pfisterer, Bernd Bischl


  Access Paper or Ask Questions

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 

  Access Paper or Ask Questions

mlr3proba: Machine Learning Survival Analysis in R

Aug 18, 2020
Raphael Sonabend, Franz J. Kir√°ly, Andreas Bender, Bernd Bischl, Michel Lang

* Submitted to JMLR 

  Access Paper or Ask Questions

Relative Feature Importance

Jul 16, 2020
Gunnar König, Christoph Molnar, Bernd Bischl, Moritz Grosse-Wentrup


  Access Paper or Ask Questions

Pitfalls to Avoid when Interpreting Machine Learning Models

Jul 08, 2020
Christoph Molnar, Gunnar König, 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/

  Access Paper or Ask Questions

A General Machine Learning Framework for Survival Analysis

Jun 27, 2020
Andreas Bender, David R√ľgamer, Fabian Scheipl, Bernd Bischl

* Accepted at ECML PKDD 2020, Research Track 

  Access Paper or Ask Questions

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

Jun 08, 2020
Christoph Molnar, Gunnar König, Bernd Bischl, Giuseppe Casalicchio


  Access Paper or Ask Questions

Multi-Objective Counterfactual Explanations

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


  Access Paper or Ask Questions

Multi-Objective Hyperparameter Tuning and Feature Selection using Filter Ensembles

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


  Access Paper or Ask Questions

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

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


  Access Paper or Ask Questions

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

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


  Access Paper or Ask Questions

Towards Human Centered AutoML

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

* 4 pages 

  Access Paper or Ask Questions

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

Oct 04, 2019
Xudong Sun, Bernd Bischl


  Access Paper or Ask Questions

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 

  Access Paper or Ask Questions

An Open Source AutoML Benchmark

Jul 01, 2019
Pieter Gijsbers, Erin LeDell, Janek Thomas, Sébastien 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/ 

  Access Paper or Ask Questions

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

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


  Access Paper or Ask Questions

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


  Access Paper or Ask Questions

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

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


  Access Paper or Ask Questions

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


  Access Paper or Ask Questions

Component-Wise Boosting of Targets for Multi-Output Prediction

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


  Access Paper or Ask Questions

Quantifying Interpretability of Arbitrary Machine Learning Models Through Functional Decomposition

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


  Access Paper or Ask Questions

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

Feb 24, 2019
Xudong Sun, Andrea Bommert, Florian Pfisterer, J√∂rg Rahnenf√ľhrer, Michel Lang, Bernd Bischl


  Access Paper or Ask Questions

Robust Anomaly Detection in Images using Adversarial Autoencoders

Jan 18, 2019
Laura Beggel, Michael Pfeiffer, Bernd Bischl


  Access Paper or Ask Questions

Learning Multiple Defaults for Machine Learning Algorithms

Nov 23, 2018
Florian Pfisterer, Jan N. van Rijn, Philipp Probst, Andreas M√ľller, Bernd Bischl


  Access Paper or Ask Questions

Tunability: Importance of Hyperparameters of Machine Learning Algorithms

Oct 22, 2018
Philipp Probst, Bernd Bischl, Anne-Laure Boulesteix

* 22 pages, 10 tables, 8 figures 

  Access Paper or Ask Questions

Automatic Exploration of Machine Learning Experiments on OpenML

Oct 19, 2018
Daniel K√ľhn, Philipp Probst, Janek Thomas, Bernd Bischl

* 6 pages, 0 figures 

  Access Paper or Ask Questions