On the combination of locally optimal pairwise classifiers. - Gero Szepannek; Bernd Bischl; Claus Weihs (Eng. Appl. Artif. Intell., 2009)

Selecting Groups of Audio Features by Statistical Tests and the Group Lasso. - Bernd Bischl; Markus Eichhoff; Claus Weihs (Sprachkommunikation, 2010)

Bias-Variance Analysis of Local Classification Methods. - Julia Schiffner; Bernd Bischl; Claus Weihs (GfKl, 2010)

A Case Study on the Use of Statistical Classification Methods in Particle Physics. - Claus Weihs; Olaf Mersmann; Bernd Bischl; Arno Fritsch; Heike Trautmann; Till Moritz Karbach; Bernhard Spaan (GfKl, 2010)

Selecting Small Audio Feature Sets in Music Classification by Means of Asymmetric Mutation. - Bernd Bischl; Igor Vatolkin; Mike Preuss (PPSN (1), 2010)

Exploratory landscape analysis. - Olaf Mersmann; Bernd Bischl; Heike Trautmann; Mike Preuss; Claus Weihs; Günter Rudolph (GECCO, 2011)

Huge Music Archives on Mobile Devices. - Holger Blume; Bernd Bischl; Martin Botteck; Christian Igel; Rainer Martin 001; Günther Rötter; Günter Rudolph; Wolfgang M. Theimer; Igor Vatolkin; Claus Weihs (IEEE Signal Process. Mag., 2011)

Algorithm selection based on exploratory landscape analysis and cost-sensitive learning. - Bernd Bischl; Olaf Mersmann; Heike Trautmann; Mike Preuß (GECCO, 2012)

Benchmarking Classification Algorithms on High-Performance Computing Clusters. - Bernd Bischl; Julia Schiffner; Claus Weihs (GfKl, 2012)

Support Vector Machines on Large Data Sets: Simple Parallel Approaches. - Oliver Meyer 002; Bernd Bischl; Claus Weihs (GfKl, 2012)

Statistical Comparison of Classifiers for Multi-objective Feature Selection in Instrument Recognition. - Igor Vatolkin; Bernd Bischl; Günter Rudolph; Claus Weihs (GfKl, 2012)

Local Search and the Traveling Salesman Problem: A Feature-Based Characterization of Problem Hardness. - Olaf Mersmann; Bernd Bischl; Jakob Bossek; Heike Trautmann; Markus Wagner 007; Frank Neumann 001 (LION, 2012)

A Novel Feature-Based Approach to Characterize Algorithm Performance for the Traveling Salesman Problem - Olaf Mersmann; Bernd Bischl; Heike Trautmann; Markus Wagner 007; Frank Neumann 001 (CoRR, 2012)

Resampling Methods for Meta-Model Validation with Recommendations for Evolutionary Computation. - Bernd Bischl; Olaf Mersmann; Heike Trautmann; Claus Weihs (Evol. Comput., 2012)

Tuning and evolution of support vector kernels. - Patrick Koch; Bernd Bischl; Oliver Flasch; Thomas Bartz-Beielstein; Claus Weihs; Wolfgang Konen (Evol. Intell., 2012)

A feature-based comparison of local search and the christofides algorithm for the travelling salesperson problem. - Samadhi Nallaperuma; Markus Wagner 007; Frank Neumann 001; Bernd Bischl; Olaf Mersmann; Heike Trautmann (FOGA, 2013)

PROGRESS: Progressive Reinforcement-Learning-Based Surrogate Selection. - Stefan Hess; Tobias Wagner 001; Bernd Bischl (LION, 2013)

OpenML: A Collaborative Science Platform. - Jan N. van Rijn; Bernd Bischl; Luís Torgo; Bo Gao 002; Venkatesh Umaashankar; Simon Fischer 001; Patrick Winter; Bernd Wiswedel; Michael R. Berthold; Joaquin Vanschoren (ECML/PKDD (3), 2013)

A novel feature-based approach to characterize algorithm performance for the traveling salesperson problem. - Olaf Mersmann; Bernd Bischl; Heike Trautmann; Markus Wagner 007; Jakob Bossek; Frank Neumann 001 (Ann. Math. Artif. Intell., 2013)

Benchmarking local classification methods. - Bernd Bischl; Julia Schiffner; Claus Weihs (Comput. Stat., 2013)

OpenML: networked science in machine learning. - Joaquin Vanschoren; Jan N. van Rijn; Bernd Bischl; Luís Torgo (SIGKDD Explor., 2013)

Fast Model Based Optimization of Tone Onset Detection by Instance Sampling. - Nadja Bauer; Klaus Friedrichs; Bernd Bischl; Claus Weihs (ECDA, 2014)

Big Data Classification : Aspects on Many Features and Many Observations. - Claus Weihs; Daniel Horn; Bernd Bischl (ECDA, 2014)

MOI-MBO: Multiobjective Infill for Parallel Model-Based Optimization. - Bernd Bischl; Simon Wessing; Nadja Bauer; Klaus Friedrichs; Claus Weihs (LION, 2014)

On Class Imbalance Correction for Classification Algorithms in Credit Scoring. - Bernd Bischl; Tobias Kühn; Gero Szepannek (OR, 2014)

Model and Algorithm Selection in Statistical Learning and Optimization. - Bernd Bischl (2014)

Model-Based Multi-objective Optimization: Taxonomy, Multi-Point Proposal, Toolbox and Benchmark. - Daniel Horn; Tobias Wagner 001; Dirk Biermann; Claus Weihs; Bernd Bischl (EMO (1), 2015)

Learning Feature-Parameter Mappings for Parameter Tuning via the Profile Expected Improvement. - Jakob Bossek; Bernd Bischl; Tobias Wagner 001; Günter Rudolph (GECCO, 2015)

The Impact of Initial Designs on the Performance of MATSuMoTo on the Noiseless BBOB-2015 Testbed: A Preliminary Study. - Dimo Brockhoff; Bernd Bischl; Tobias Wagner 001 (GECCO (Companion), 2015)

To tune or not to tune: Recommending when to adjust SVM hyper-parameters via meta-learning. - Rafael Gomes Mantovani; André Luis Debiaso Rossi; Joaquin Vanschoren; Bernd Bischl; André C. P. L. F. de Carvalho (IJCNN, 2015)

Effectiveness of Random Search in SVM hyper-parameter tuning. - Rafael Gomes Mantovani; André Luis Debiaso Rossi; Joaquin Vanschoren; Bernd Bischl; André C. P. L. F. de Carvalho (IJCNN, 2015)

Taking machine learning research online with OpenML. - Joaquin Vanschoren; Jan N. van Rijn; Bernd Bischl (BigMine, 2015)

Applying Model-Based Optimization to Hyperparameter Optimization in Machine Learning. - Bernd Bischl (MetaSel@PKDD/ECML, 2015)

Analyzing the BBOB Results by Means of Benchmarking Concepts. - Olaf Mersmann; Mike Preuss; Heike Trautmann; Bernd Bischl; Claus Weihs (Evol. Comput., 2015)

Reinforcement Learning for Automatic Online Algorithm Selection - an Empirical Study. - Hans Degroote; Bernd Bischl; Lars Kotthoff; Patrick De Causmaecker (ITAT, 2016)

Faster Model-Based Optimization Through Resource-Aware Scheduling Strategies. - Jakob Richter; Helena Kotthaus; Bernd Bischl; Peter Marwedel; Jörg Rahnenführer; Michel Lang (LION, 2016)

Multi-objective parameter configuration of machine learning algorithms using model-based optimization. - Daniel Horn; Bernd Bischl (SSCI, 2016)

ASlib: A benchmark library for algorithm selection. - Bernd Bischl; Pascal Kerschke; Lars Kotthoff; Marius Lindauer; Yuri Malitsky; Alexandre Fréchette; Holger H. Hoos; Frank Hutter; Kevin Leyton-Brown; Kevin Tierney; Joaquin Vanschoren (Artif. Intell., 2016)

Fast model selection by limiting SVM training times. - Aydin Demircioglu; Daniel Horn; Tobias Glasmachers; Bernd Bischl; Claus Weihs (CoRR, 2016)

mlr Tutorial. - Julia Schiffner; Bernd Bischl; Michel Lang; Jakob Richter; Zachary M. Jones; Philipp Probst; Florian Pfisterer; Mason Gallo; Dominik Kirchhoff; Tobias Kühn; Janek Thomas; Lars Kotthoff (CoRR, 2016)

mlr: Machine Learning in R. - Bernd Bischl; Michel Lang; Lars Kotthoff; Julia Schiffner; Jakob Richter; Erich Studerus; Giuseppe Casalicchio; Zachary M. Jones (J. Mach. Learn. Res., 2016)

First Investigations on Noisy Model-Based Multi-objective Optimization. - Daniel Horn; Melanie Dagge; Xudong Sun 002; Bernd Bischl (EMO, 2017)

Evaluating random forest models for irace. - Leslie Pérez Cáceres; Bernd Bischl; Thomas Stützle (GECCO (Companion), 2017)

RAMBO: Resource-Aware Model-Based Optimization with Scheduling for Heterogeneous Runtimes and a Comparison with Asynchronous Model-Based Optimization. - Helena Kotthaus; Jakob Richter; Andreas Lang 002; Janek Thomas; Bernd Bischl; Peter Marwedel; Jörg Rahnenführer; Michel Lang (LION, 2017)

Probing for Sparse and Fast Variable Selection with Model-Based Boosting. - Janek Thomas; Tobias Hepp 001; Andreas Mayr; Bernd Bischl (Comput. Math. Methods Medicine, 2017)

OpenML: An R Package to Connect to the Networked Machine Learning Platform OpenML. - Giuseppe Casalicchio; Jakob Bossek; Michel Lang; Dominik Kirchhoff; Pascal Kerschke; Benjamin Hofner; Heidi Seibold; Joaquin Vanschoren; Bernd Bischl (CoRR, 2017)

OpenML Benchmarking Suites and the OpenML100. - Bernd Bischl; Giuseppe Casalicchio; Matthias Feurer; Frank Hutter; Michel Lang; Rafael Gomes Mantovani; Jan N. van Rijn; Joaquin Vanschoren (CoRR, 2017)

batchtools: Tools for R to work on batch systems. - Michel Lang; Bernd Bischl; Dirk Surmann (J. Open Source Softw., 2017)

Multilabel Classification with R Package mlr. - Philipp Probst; Quay Au; Giuseppe Casalicchio; Clemens Stachl; Bernd Bischl (R J., 2017)

Visualizing the Feature Importance for Black Box Models. - Giuseppe Casalicchio; Christoph Molnar; Bernd Bischl (ECML/PKDD (1), 2018)

A Regression-Based Methodology for Online Algorithm Selection. - Hans Degroote; Patrick De Causmaecker; Bernd Bischl; Lars Kotthoff (SOCS, 2018)

A comparative study on large scale kernelized support vector machines. - Daniel Horn; Aydin Demircioglu; Bernd Bischl; Tobias Glasmachers; Claus Weihs (Adv. Data Anal. Classif., 2018)

Corrigendum to "Probing for Sparse and Fast Variable Selection with Model-Based Boosting". - Janek Thomas; Tobias Hepp 001; Andreas Mayr; Bernd Bischl (Comput. Math. Methods Medicine, 2018)

Automatic Exploration of Machine Learning Experiments on OpenML. - Daniel Kühn; Philipp Probst; Janek Thomas; Bernd Bischl (CoRR, 2018)

Automatic Gradient Boosting. - Janek Thomas; Stefan Coors; Bernd Bischl (CoRR, 2018)

Proceedings of Reisensburg 2014-2015. - Hans A. Kestler; Bernd Bischl; Matthias Schmid (Comput. Stat., 2018)

iml: An R package for Interpretable Machine Learning. - Christoph Molnar; Giuseppe Casalicchio; Bernd Bischl (J. Open Source Softw., 2018)

compboost: Modular Framework for Component-Wise Boosting. - Daniel Schalk; Janek Thomas; Bernd Bischl (J. Open Source Softw., 2018)

Gradient boosting for distributional regression: faster tuning and improved variable selection via noncyclical updates. - Janek Thomas; Andreas Mayr; Bernd Bischl; Matthias Schmid; Adam Smith; Benjamin Hofner (Stat. Comput., 2018)

compboost. - Daniel Schalk; Janek Thomas; Bernd Bischl (Zenodo, 2018)

High Dimensional Restrictive Federated Model Selection with Multi-objective Bayesian Optimization over Shifted Distributions. - Xudong Sun 002; Andrea Bommert; Florian Pfisterer; Jörg Rahnenführer; Michel Lang; Bernd Bischl (IntelliSys (1), 2019)

ReinBo: Machine Learning Pipeline Conditional Hierarchy Search and Configuration with Bayesian Optimization Embedded Reinforcement Learning. - Xudong Sun 002; Jiali Lin; Bernd Bischl (PKDD/ECML Workshops (1), 2019)

Robust Anomaly Detection in Images Using Adversarial Autoencoders. - Laura Beggel; Michael Pfeiffer 001; Bernd Bischl (ECML/PKDD (1), 2019)

Wearable-Based Parkinson's Disease Severity Monitoring Using Deep Learning. - Jann Goschenhofer; Franz Michael Josef Pfister; Kamer Ali Yuksel; Bernd Bischl; Urban Fietzek; Janek Thomas (ECML/PKDD (3), 2019)

Quantifying Model Complexity via Functional Decomposition for Better Post-hoc Interpretability. - Christoph Molnar; Giuseppe Casalicchio; Bernd Bischl (PKDD/ECML Workshops (1), 2019)

Sampling, Intervention, Prediction, Aggregation: A Generalized Framework for Model-Agnostic Interpretations. - Christian A. Scholbeck; Christoph Molnar; Christian Heumann; Bernd Bischl; Giuseppe Casalicchio (PKDD/ECML Workshops (1), 2019)

Tutorial and Survey on Probabilistic Graphical Model and Variational Inference in Deep Reinforcement Learning. - Xudong Sun 002; Bernd Bischl (SSCI, 2019)

Variational Resampling Based Assessment of Deep Neural Networks under Distribution Shift. - Xudong Sun 002; Alexej Gossmann; Yu Wang; Bernd Bischl (SSCI, 2019)

Quantifying Interpretability of Arbitrary Machine Learning Models Through Functional Decomposition. - Christoph Molnar; Giuseppe Casalicchio; Bernd Bischl (CoRR, 2019)

Component-Wise Boosting of Targets for Multi-Output Prediction. - Quay Au; Daniel Schalk; Giuseppe Casalicchio; Ramona Schödel; Clemens Stachl; Bernd Bischl (CoRR, 2019)

ReinBo: Machine Learning pipeline search and configuration with Bayesian Optimization embedded Reinforcement Learning. - Xudong Sun 002; Jiali Lin; Bernd Bischl (CoRR, 2019)

Resampling-based Assessment of Robustness to Distribution Shift for Deep Neural Networks. - Xudong Sun 002; Yu Wang; Alexej Gossmann; Bernd Bischl (CoRR, 2019)

An Open Source AutoML Benchmark. - Pieter Gijsbers; Erin LeDell; Janek Thomas; Sébastien Poirier; Bernd Bischl; Joaquin Vanschoren (CoRR, 2019)

Multi-Objective Automatic Machine Learning with AutoxgboostMC. - Florian Pfisterer; Stefan Coors; Janek Thomas; Bernd Bischl (CoRR, 2019)

Towards Human Centered AutoML. - Florian Pfisterer; Janek Thomas; Bernd Bischl (CoRR, 2019)

Benchmarking time series classification - Functional data vs machine learning approaches. - Florian Pfisterer; Laura Beggel; Xudong Sun 002; Fabian Scheipl; Bernd Bischl (CoRR, 2019)

Model-Agnostic Approaches to Multi-Objective Simultaneous Hyperparameter Tuning and Feature Selection. - Martin Binder; Julia Moosbauer; Janek Thomas; Bernd Bischl (CoRR, 2019)

Time series anomaly detection based on shapelet learning. - Laura Beggel; Bernhard X. Kausler; Martin Schiegg; Michael Pfeiffer 001; Bernd Bischl (Comput. Stat., 2019)

OpenML: An R package to connect to the machine learning platform OpenML. - Giuseppe Casalicchio; Jakob Bossek; Michel Lang; Dominik Kirchhoff; Pascal Kerschke; Benjamin Hofner; Heidi Seibold; Joaquin Vanschoren; Bernd Bischl (Comput. Stat., 2019)

Proceedings of Reisensburg 2016-2017. - Matthias Schmid; Bernd Bischl; Hans A. Kestler (Comput. Stat., 2019)

Tunability: Importance of Hyperparameters of Machine Learning Algorithms. - Philipp Probst; Anne-Laure Boulesteix; Bernd Bischl (J. Mach. Learn. Res., 2019)

mlr3: A modern object-oriented machine learning framework in R. - Michel Lang; Martin Binder; Jakob Richter; Patrick Schratz; Florian Pfisterer; Stefan Coors; Quay Au; Giuseppe Casalicchio; Lars Kotthoff; Bernd Bischl (J. Open Source Softw., 2019)

2019-feature-selection. - Patrick Schratz; Jannes Muenchow; Eugenia Iturritxa; Alexander Brenning; Bernd Bischl (Zenodo, 2019)

2019-feature-selection. - Patrick Schratz; Jannes Muenchow; Eugenia Iturritxa; Alexander Brenning; Bernd Bischl (Zenodo, 2019)

2019-feature-selection. - Patrick Schratz; Jannes Muenchow; Eugenia Iturritxa; Alexander Brenning; Bernd Bischl (Zenodo, 2019)

2019-feature-selection. - Patrick Schratz; Jannes Muenchow; Eugenia Iturritxa; Alexander Brenning; Bernd Bischl (Zenodo, 2019)

mlr3: A modern object-oriented machine learning framework in R. - Michel Lang; Martin Binder; Jakob Richter; Patrick Schratz; Florian Pfisterer; Stefan Coors; Quay Au; Giuseppe Casalicchio; Lars Kotthoff; Bernd Bischl (Zenodo, 2019)

Multi-objective hyperparameter tuning and feature selection using filter ensembles. - Martin Binder; Julia Moosbauer; Janek Thomas; Bernd Bischl (GECCO, 2020)

General Pitfalls of Model-Agnostic Interpretation Methods for Machine Learning Models. - Christoph Molnar; Gunnar König; Julia Herbinger; Timo Freiesleben; Susanne Dandl; Christian A. Scholbeck; Giuseppe Casalicchio; Moritz Grosse-Wentrup; Bernd Bischl (xxAI@ICML, 2020)

Relative Feature Importance. - Gunnar König; Christoph Molnar; Bernd Bischl; Moritz Grosse-Wentrup (ICPR, 2020)

A General Machine Learning Framework for Survival Analysis. - Andreas Bender 001; David Rügamer; Fabian Scheipl; Bernd Bischl (ECML/PKDD (3), 2020)

Interpretable Machine Learning - A Brief History, State-of-the-Art and Challenges. - Christoph Molnar; Giuseppe Casalicchio; Bernd Bischl (PKDD/ECML Workshops, 2020)

Multi-Objective Counterfactual Explanations. - Susanne Dandl; Christoph Molnar; Martin Binder; Bernd Bischl (PPSN (1), 2020)

Pitfalls to Avoid when Interpreting Machine Learning Models. - Christoph Molnar; Gunnar König; Julia Herbinger; Timo Freiesleben; Susanne Dandl; Christian A. Scholbeck; Giuseppe Casalicchio; Moritz Grosse-Wentrup; Bernd Bischl (CoRR, 2020)

mlr3proba: Machine Learning Survival Analysis in R. - Raphael Sonabend; Franz J. Király; Andreas Bender 001; Bernd Bischl; Michel Lang (CoRR, 2020)

Neural Mixture Distributional Regression. - David Rügamer; Florian Pfisterer; Bernd Bischl (CoRR, 2020)

Debiasing classifiers: is reality at variance with expectation? - Ashrya Agrawal; Florian Pfisterer; Bernd Bischl; Jiahao Chen 001; Srijan Sood; Sameena Shah; Francois Buet-Golfouse; Bilal A. Mateen; Sebastian J. Vollmer (CoRR, 2020)

Benchmark for filter methods for feature selection in high-dimensional classification data. - Andrea Bommert; Xudong Sun 002; Bernd Bischl; Jörg Rahnenführer; Michel Lang (Comput. Stat. Data Anal., 2020)

Monitoring forest health using hyperspectral imagery: Does feature selection improve the performance of machine-learning techniques? - Patrick Schratz; Jannes Muenchow; Eugenia Iturritxa; José Cortés; Alexander Brenning; Bernd Bischl (Zenodo, 2020)

Meta-learning for symbolic hyperparameter defaults. - Pieter Gijsbers; Florian Pfisterer; Jan N. van Rijn; Bernd Bischl; Joaquin Vanschoren (GECCO Companion, 2021)

Learning multiple defaults for machine learning algorithms. - Florian Pfisterer; Jan N. van Rijn; Philipp Probst; Andreas C. Müller 002; Bernd Bischl (GECCO Companion, 2021)

Deep Semi-supervised Learning for Time Series Classification. - Jann Goschenhofer; Rasmus Hvingelby; David Rügamer; Janek Thomas; Moritz Wagner 001; Bernd Bischl (ICMLA, 2021)

OpenML Benchmarking Suites. - Bernd Bischl; Giuseppe Casalicchio; Matthias Feurer; Pieter Gijsbers; Frank Hutter; Michel Lang; Rafael Gomes Mantovani; Jan N. van Rijn; Joaquin Vanschoren (NeurIPS Datasets and Benchmarks, 2021)

Explaining Hyperparameter Optimization via Partial Dependence Plots. - Julia Moosbauer; Julia Herbinger; Giuseppe Casalicchio; Marius Lindauer; Bernd Bischl (NeurIPS, 2021)

Semi-Structured Deep Piecewise Exponential Models. - Philipp Kopper; Sebastian Pölsterl; Christian Wachinger; Bernd Bischl; Andreas Bender 001; David Rügamer (SPACA, 2021)

Automated Online Experiment-Driven Adaptation-Mechanics and Cost Aspects. - Ilias Gerostathopoulos; Frantisek Plásil; Christian Prehofer; Janek Thomas; Bernd Bischl (IEEE Access, 2021)

mlr3proba: an R package for machine learning in survival analysis. - Raphael Sonabend; Franz J. Király; Andreas Bender 001; Bernd Bischl; Michel Lang (Bioinform., 2021)

Improved Outcome Prediction Across Data Sources Through Robust Parameter Tuning. - Nicole Ellenbach; Anne-Laure Boulesteix; Bernd Bischl; Kristian Unger; Roman Hornung (J. Classif., 2021)

deepregression: a Flexible Neural Network Framework for Semi-Structured Deep Distributional Regression. - David Rügamer; Ruolin Shen; Christina Bukas; Lisa Barros de Andrade e Sousa; Dominik Thalmeier; Nadja Klein; Chris Kolb; Florian Pfisterer; Philipp Kopper; Bernd Bischl; Christian L. Müller (CoRR, 2021)

Decomposition of Global Feature Importance into Direct and Associative Components (DEDACT). - Gunnar König; Timo Freiesleben; Bernd Bischl; Giuseppe Casalicchio; Moritz Grosse-Wentrup (CoRR, 2021)

Mutation is all you need. - Lennart Schneider; Florian Pfisterer; Martin Binder; Bernd Bischl (CoRR, 2021)

Relating the Partial Dependence Plot and Permutation Feature Importance to the Data Generating Process. - Christoph Molnar; Timo Freiesleben; Gunnar König; Giuseppe Casalicchio; Marvin N. Wright; Bernd Bischl (CoRR, 2021)

YAHPO Gym - Design Criteria and a new Multifidelity Benchmark for Hyperparameter Optimization. - Florian Pfisterer; Lennart Schneider; Julia Moosbauer; Martin Binder; Bernd Bischl (CoRR, 2021)

Learning Statistical Representation with Joint Deep Embedded Clustering. - Mina Rezaei; Emilio Dorigatti; David Rügamer; Bernd Bischl (CoRR, 2021)

Automatic Componentwise Boosting: An Interpretable AutoML System. - Stefan Coors; Daniel Schalk; Bernd Bischl; David Rügamer (CoRR, 2021)

Deep Bregman Divergence for Contrastive Learning of Visual Representations. - Mina Rezaei; Farzin Soleymani; Bernd Bischl; Shekoofeh Azizi (CoRR, 2021)

Survival-oriented embeddings for improving accessibility to complex data structures. - Tobias Weber; Michael Ingrisch; Matthias Fabritius; Bernd Bischl; David Rügamer (CoRR, 2021)

Towards modelling hazard factors in unstructured data spaces using gradient-based latent interpolation. - Tobias Weber; Michael Ingrisch; Bernd Bischl; David Rügamer (CoRR, 2021)

mlr3pipelines - Flexible Machine Learning Pipelines in R. - Martin Binder; Florian Pfisterer; Michel Lang; Lennart Schneider; Lars Kotthoff; Bernd Bischl (J. Mach. Learn. Res., 2021)

mcboost: Multi-Calibration Boosting for R. - Florian Pfisterer; Christoph Kern 001; Susanne Dandl; Matthew Sun; Michael P. Kim; Bernd Bischl (J. Open Source Softw., 2021)

Monitoring Forest Health Using Hyperspectral Imagery: Does Feature Selection Improve the Performance of Machine-Learning Techniques? - Patrick Schratz; Jannes Muenchow; Eugenia Iturritxa; José Cortés; Bernd Bischl; Alexander Brenning (Remote. Sens., 2021)

redmod-team/SympGPR v1.0. - Katharina Rath; Christopher G. Albert; Bernd Bischl; Udo von Toussaint (Zenodo, 2021)

mcboost: Multi-Calibration Boosting for R. - Florian Pfisterer; Christoph Kern 001; Susanne Dandl; Mathew Sun; Michael P. Kim; Bernd Bischl (Zenodo, 2021)

REPID: Regional Effect Plots with implicit Interaction Detection. - Julia Herbinger; Bernd Bischl; Giuseppe Casalicchio (AISTATS, 2022)

YAHPO Gym - An Efficient Multi-Objective Multi-Fidelity Benchmark for Hyperparameter Optimization. - Florian Pfisterer; Lennart Schneider; Julia Moosbauer; Martin Binder; Bernd Bischl (AutoML, 2022)

Tackling Neural Architecture Search With Quality Diversity Optimization. - Lennart Schneider; Florian Pfisterer; Paul Kent; Jürgen Branke; Bernd Bischl; Janek Thomas (AutoML, 2022)

Multi-objective counterfactual fairness. - Susanne Dandl; Florian Pfisterer; Bernd Bischl (GECCO Companion, 2022)

A collection of quality diversity optimization problems derived from hyperparameter optimization of machine learning models. - Lennart Schneider; Florian Pfisterer; Janek Thomas; Bernd Bischl (GECCO Companion, 2022)

Joint Debiased Representation Learning and Imbalanced Data Clustering. - Mina Rezaei; Emilio Dorigatti; David Rügamer; Bernd Bischl (ICDM (Workshops), 2022)

Representation Learning for Tablet and Paper Domain Adaptation in Favor of Online Handwriting Recognition. - Felix Ott 001; David Rügamer; Lucas Heublein; Bernd Bischl; Christopher Mutschler (ICPR Workshops (1), 2022)

Uncertainty-aware Evaluation of Time-series Classification for Online Handwriting Recognition with Domain Shift. - Andreas Klaß; Sven M. Lorenz; Martin W. Lauer-Schmaltz; David Rügamer; Bernd Bischl; Christopher Mutschler; Felix Ott 001 (STRL@IJCAI, 2022)

Bayesian uncertainty estimation for detection of long-tail and unseen conditions in abdominal images. - Mina Rezaei; Janne J. Näppi; Bernd Bischl; Hiroyuki Yoshida (Computer-Aided Diagnosis, 2022)

Implicit Embeddings via GAN Inversion for High Resolution Chest Radiographs. - Tobias Weber; Michael Ingrisch; Bernd Bischl; David Rügamer (MAD@MICCAI, 2022)

Deep variational clustering framework for self-labeling large-scale medical images. - Farzin Soleymani; Mohammad Eslami; Tobias Elze; Bernd Bischl; Mina Rezaei (Image Processing, 2022)

Domain Adaptation for Time-Series Classification to Mitigate Covariate Shift. - Felix Ott 001; David Rügamer; Lucas Heublein; Bernd Bischl; Christopher Mutschler (ACM Multimedia, 2022)

FiLM-Ensemble: Probabilistic Deep Learning via Feature-wise Linear Modulation. - Mehmet Ozgur Turkoglu; Alexander Becker; Hüseyin Anil Gündüz; Mina Rezaei; Bernd Bischl; Rodrigo Caye Daudt; Stefano D'Aronco; Jan D. Wegner; Konrad Schindler (NeurIPS, 2022)

DeepPAMM: Deep Piecewise Exponential Additive Mixed Models for Complex Hazard Structures in Survival Analysis. - Philipp Kopper; Simon Wiegrebe; Bernd Bischl; Andreas Bender 001; David Rügamer (PAKDD (2), 2022)

Efficient Automated Deep Learning for Time Series Forecasting. - Difan Deng; Florian Karl; Frank Hutter; Bernd Bischl; Marius Lindauer (ECML/PKDD (3), 2022)

Factorized Structured Regression for Large-Scale Varying Coefficient Models. - David Rügamer; Andreas Bender 001; Simon Wiegrebe; Daniel Racek; Bernd Bischl; Christian L. Müller; Clemens Stachl (ECML/PKDD (5), 2022)

HPO ˟ ELA: Investigating Hyperparameter Optimization Landscapes by Means of Exploratory Landscape Analysis. - Lennart Schneider; Lennart Schäpermeier; Raphael Patrick Prager; Bernd Bischl; Heike Trautmann; Pascal Kerschke (PPSN (1), 2022)

Developing Open Source Educational Resources for Machine Learning and Data Science. - Ludwig Bothmann; Sven Strickroth; Giuseppe Casalicchio; David Rügamer; Marius Lindauer; Fabian Scheipl; Bernd Bischl (Teaching ML, 2022)

Joint Classification and Trajectory Regression of Online Handwriting using a Multi-Task Learning Approach. - Felix Ott 001; David Rügamer; Lucas Heublein; Bernd Bischl; Christopher Mutschler (WACV, 2022)

Positive-Unlabeled Learning with Uncertainty-aware Pseudo-label Selection. - Emilio Dorigatti; Jann Goschenhofer; Benjamin Schubert; Mina Rezaei; Bernd Bischl (CoRR, 2022)

Cross-Modal Common Representation Learning with Triplet Loss Functions. - Felix Ott 001; David Rügamer; Lucas Heublein; Bernd Bischl; Christopher Mutschler (CoRR, 2022)

Distributed non-disclosive validation of predictive models by a modified ROC-GLM. - Daniel Schalk; Verena S. Hoffmann; Bernd Bischl; Ulrich Mansmann (CoRR, 2022)

Analyzing the Effects of Handling Data Imbalance on Learned Features from Medical Images by Looking Into the Models. - Ashkan Khakzar; Yawei Li; Yang Zhang; Mirac Sanisoglu; Seong Tae Kim 001; Mina Rezaei; Bernd Bischl; Nassir Navab (CoRR, 2022)

What Is Fairness? Implications For FairML. - Ludwig Bothmann; Kristina Peters; Bernd Bischl (CoRR, 2022)

Enhancing Explainability of Hyperparameter Optimization via Bayesian Algorithm Execution. - Julia Moosbauer; Giuseppe Casalicchio; Marius Lindauer; Bernd Bischl (CoRR, 2022)

Multi-Objective Hyperparameter Optimization - An Overview. - Florian Karl; Tobias Pielok; Julia Moosbauer; Florian Pfisterer; Stefan Coors; Martin Binder; Lennart Schneider; Janek Thomas; Jakob Richter; Michel Lang; Eduardo C. Garrido-Merchán; Jürgen Branke; et al. (CoRR, 2022)

HPO X ELA: Investigating Hyperparameter Optimization Landscapes by Means of Exploratory Landscape Analysis. - Lennart Schneider; Lennart Schäpermeier; Raphael Patrick Prager; Bernd Bischl; Heike Trautmann; Pascal Kerschke (CoRR, 2022)

Benchmarking Visual-Inertial Deep Multimodal Fusion for Relative Pose Regression and Odometry-aided Absolute Pose Regression. - Felix Ott 001; Nisha Lakshmana Raichur; David Rügamer; Tobias Feigl; Heiko Neumann; Bernd Bischl; Christopher Mutschler (CoRR, 2022)

Robust and Efficient Imbalanced Positive-Unlabeled Learning with Self-supervision. - Emilio Dorigatti; Jonas Schweisthal; Bernd Bischl; Mina Rezaei (CoRR, 2022)

Joint Debiased Representation and Image Clustering Learning with Self-Supervision. - Shunjie-Fabian Zheng; JaeEun Nam; Emilio Dorigatti; Bernd Bischl; Shekoofeh Azizi; Mina Rezaei (CoRR, 2022)

Improved proteasomal cleavage prediction with positive-unlabeled learning. - Emilio Dorigatti; Bernd Bischl; Benjamin Schubert (CoRR, 2022)

Regularized target encoding outperforms traditional methods in supervised machine learning with high cardinality features. - Florian Pargent; Florian Pfisterer; Janek Thomas; Bernd Bischl (Comput. Stat., 2022)

Grouped feature importance and combined features effect plot. - Quay Au; Julia Herbinger; Clemens Stachl; Bernd Bischl; Giuseppe Casalicchio (Data Min. Knowl. Discov., 2022)

Benchmarking online sequence-to-sequence and character-based handwriting recognition from IMU-enhanced pens. - Felix Ott 001; David Rügamer; Lucas Heublein; Tim Hamann; Jens Barth; Bernd Bischl; Christopher Mutschler (Int. J. Document Anal. Recognit., 2022)

Automated Benchmark-Driven Design and Explanation of Hyperparameter Optimizers. - Julia Moosbauer; Martin Binder; Lennart Schneider; Florian Pfisterer; Marc Becker; Michel Lang; Lars Kotthoff; Bernd Bischl (IEEE Trans. Evol. Comput., 2022)

Efficient Document Embeddings via Self-Contrastive Bregman Divergence Learning. - Daniel Saggau; Mina Rezaei; Bernd Bischl; Ilias Chalkidis (ACL (Findings), 2023)

Frequentist Uncertainty Quantification in Semi-Structured Neural Networks. - Emilio Dorigatti; Benjamin Schubert; Bernd Bischl; David Rügamer (AISTATS, 2023)

Q(D)O-ES: Population-based Quality (Diversity) Optimisation for Post Hoc Ensemble Selection in AutoML. - Lennart Oswald Purucker; Lennart Schneider; Marie Anastacio; Joeran Beel; Bernd Bischl; Holger H. Hoos (AutoML, 2023)

Symbolic Explanations for Hyperparameter Optimization. - Sarah Segel; Helena Graf; Alexander Tornede; Bernd Bischl; Marius Lindauer (AutoML, 2023)

Neural Architecture Search for Genomic Sequence Data. - Amadeu Scheppach; Hüseyin Anil Gündüz; Emilio Dorigatti; Philipp C. Münch; Alice C. McHardy; Bernd Bischl; Mina Rezaei; Martin Binder (CIBCB, 2023)

Neural Networks as Black-Box Benchmark Functions Optimized for Exploratory Landscape Features. - Raphael Patrick Prager; Konstantin Dietrich; Lennart Schneider; Lennart Schäpermeier; Bernd Bischl; Pascal Kerschke; Heike Trautmann; Olaf Mersmann (FOGA, 2023)

Bayesian Optimization. - Ivo Couckuyt; Sebastian Rojas-Gonzalez; Jürgen Branke; Bernd Bischl (GECCO Companion, 2023)

Multi-Objective Optimization of Performance and Interpretability of Tabular Supervised Machine Learning Models. - Lennart Schneider; Bernd Bischl; Janek Thomas (GECCO, 2023)

Towards Enhancing Deep Active Learning with Weak Supervision and Constrained Clustering. - Matthias Aßenmacher; Lukas Rauch; Jann Goschenhofer; Andreas Stephan; Bernd Bischl; Benjamin Roth 001; Bernhard Sick (IAL@PKDD/ECML, 2023)

Approximate Bayesian Inference with Stein Functional Variational Gradient Descent. - Tobias Pielok; Bernd Bischl; David Rügamer (ICLR, 2023)

Uncertainty Quantification for Deep Learning Models Predicting the Regulatory Activity of DNA Sequences. - Hüseyin Anil Gündüz; Sheetal Giri; Martin Binder; Bernd Bischl; Mina Rezaei (ICMLA, 2023)

Mind the Gap: Measuring Generalization Performance Across Multiple Objectives. - Matthias Feurer; Katharina Eggensperger; Edward Bergman; Florian Pfisterer; Bernd Bischl; Frank Hutter (IDA, 2023)

ConstraintMatch for Semi-constrained Clustering. - Jann Goschenhofer; Bernd Bischl; Zsolt Kira (IJCNN, 2023)

Cascaded Latent Diffusion Models for High-Resolution Chest X-ray Synthesis. - Tobias Weber; Michael Ingrisch; Bernd Bischl; David Rügamer (PAKDD (3), 2023)

Interpretable Regional Descriptors: Hyperbox-Based Local Explanations. - Susanne Dandl; Giuseppe Casalicchio; Bernd Bischl; Ludwig Bothmann (ECML/PKDD (3), 2023)

How Different is Stereotypical Bias Across Languages? - Ibrahim Tolga Öztürk; Rostislav Nedelchev; Christian Heumann; Esteban Garces Arias; Marius Roger; Bernd Bischl; Matthias Aßenmacher (PKDD/ECML Workshops (1), 2023)

ActiveGLAE: A Benchmark for Deep Active Learning with Transformers. - Lukas Rauch; Matthias Aßenmacher; Denis Huseljic; Moritz Wirth; Bernd Bischl; Bernhard Sick (ECML/PKDD (1), 2023)

Towards Efficient MCMC Sampling in Bayesian Neural Networks by Exploiting Symmetry. - Jonas Gregor Wiese; Lisa Wimmer; Theodore Papamarkou; Bernd Bischl; Stephan Günnemann; David Rügamer (ECML/PKDD (1), 2023)

Quantifying aleatoric and epistemic uncertainty in machine learning: Are conditional entropy and mutual information appropriate measures? - Lisa Wimmer; Yusuf Sale; Paul Hofman; Bernd Bischl; Eyke Hüllermeier (UAI, 2023)

Relating the Partial Dependence Plot and Permutation Feature Importance to the Data Generating Process. - Christoph Molnar; Timo Freiesleben; Gunnar König; Julia Herbinger; Tim Reisinger; Giuseppe Casalicchio; Marvin N. Wright; Bernd Bischl (xAI (1), 2023)

Auxiliary Cross-Modal Representation Learning With Triplet Loss Functions for Online Handwriting Recognition. - Felix Ott 001; David Rügamer; Lucas Heublein; Bernd Bischl; Christopher Mutschler (IEEE Access, 2023)

counterfactuals: An R Package for Counterfactual Explanation Methods. - Susanne Dandl; Andreas Hofheinz; Martin Binder; Bernd Bischl; Giuseppe Casalicchio (CoRR, 2023)

Decomposing Global Feature Effects Based on Feature Interactions. - Julia Herbinger; Bernd Bischl; Giuseppe Casalicchio (CoRR, 2023)

Smoothing the Edges: A General Framework for Smooth Optimization in Sparse Regularization using Hadamard Overparametrization. - Chris Kolb; Christian L. Müller; Bernd Bischl; David Rügamer (CoRR, 2023)

Diversified Ensemble of Independent Sub-Networks for Robust Self-Supervised Representation Learning. - Amirhossein Vahidi; Lisa Wimmer; Hüseyin Anil Gündüz; Bernd Bischl; Eyke Hüllermeier; Mina Rezaei (CoRR, 2023)

Probabilistic Self-supervised Learning via Scoring Rules Minimization. - Amirhossein Vahidi; Simon Schoßer; Lisa Wimmer; Yawei Li; Bernd Bischl; Eyke Hüllermeier; Mina Rezaei (CoRR, 2023)

AttributionLab: Faithfulness of Feature Attribution Under Controllable Environments. - Yang Zhang; Yawei Li; Hannah Brown; Mina Rezaei; Bernd Bischl; Philip H. S. Torr; Ashkan Khakzar; Kenji Kawaguchi (CoRR, 2023)

Evaluating machine learning models in non-standard settings: An overview and new findings. - Roman Hornung; Malte Nalenz; Lennart Schneider; Andreas Bender 001; Ludwig Bothmann; Bernd Bischl; Thomas Augustin 001; Anne-Laure Boulesteix (CoRR, 2023)

Unreading Race: Purging Protected Features from Chest X-ray Embeddings. - Tobias Weber; Michael Ingrisch; Bernd Bischl; David Rügamer (CoRR, 2023)

Position Paper: Bridging the Gap Between Machine Learning and Sensitivity Analysis. - Christian A. Scholbeck; Julia Moosbauer; Giuseppe Casalicchio; Hoshin Gupta; Bernd Bischl; Christian Heumann (CoRR, 2023)

deepregression: A Flexible Neural Network Framework for Semi-Structured Deep Distributional Regression. - 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 F. M. Baumann; Lucas Kook; et al. (J. Stat. Softw., 2023)

Hyperparameter optimization: Foundations, algorithms, best practices, and open challenges. - Bernd Bischl; Martin Binder; Michel Lang; Tobias Pielok; Jakob Richter; Stefan Coors; Janek Thomas; Theresa Ullmann; Marc Becker; Anne-Laure Boulesteix; Difan Deng; Marius Lindauer (WIREs Data. Mining. Knowl. Discov., 2023)

dsBinVal: Conducting distributed ROC analysis using DataSHIELD. - Daniel Schalk; Verena S. Hoffmann; Bernd Bischl; Ulrich Mansmann (J. Open Source Softw., 2023)

Fairness Audits and Debiasing Using pkg{mlr3fairness}. - Florian Pfisterer; Siyi Wei; Sebastian J. Vollmer; Michel Lang; Bernd Bischl (R J., 2023)

Accelerated Componentwise Gradient Boosting Using Efficient Data Representation and Momentum-Based Optimization. - Daniel Schalk; Bernd Bischl; David Rügamer (J. Comput. Graph. Stat., 2023)

Structured Verification of Machine Learning Models in Industrial Settings. - Sai Rahul Kaminwar; Jann Goschenhofer; Janek Thomas; Ingo Thon; Bernd Bischl (Big Data, 2023)

Deep Bregman divergence for self-supervised representations learning. - Mina Rezaei; Farzin Soleymani; Bernd Bischl; Shekoofeh Azizi (Comput. Vis. Image Underst., 2023)

Multi-Objective Hyperparameter Optimization in Machine Learning - An Overview. - Florian Karl; Tobias Pielok; Julia Moosbauer; Florian Pfisterer; Stefan Coors; Martin Binder; Lennart Schneider; Janek Thomas; Jakob Richter; Michel Lang; Eduardo C. Garrido-Merchán; Jürgen Branke; et al. (ACM Trans. Evol. Learn. Optim., 2023)

Probabilistic Self-supervised Representation Learning via Scoring Rules Minimization. - Amirhossein Vahidi; Simon Schoßer; Lisa Wimmer; Yawei Li; Bernd Bischl; Eyke Hüllermeier; Mina Rezaei (ICLR, 2024)

Position: Why We Must Rethink Empirical Research in Machine Learning. - Moritz Herrmann; F. Julian D. Lange; Katharina Eggensperger; Giuseppe Casalicchio; Marcel Wever; Matthias Feurer; David Rügamer; Eyke Hüllermeier; Anne-Laure Boulesteix; Bernd Bischl (ICML, 2024)

Position: A Call to Action for a Human-Centered AutoML Paradigm. - Marius Lindauer; Florian Karl; Anne Klier; Julia Moosbauer; Alexander Tornede; Andreas Müller 024; Frank Hutter; Matthias Feurer; Bernd Bischl (ICML, 2024)

Connecting the Dots: Is Mode-Connectedness the Key to Feasible Sample-Based Inference in Bayesian Neural Networks? - Emanuel Sommer; Lisa Wimmer; Theodore Papamarkou; Ludwig Bothmann; Bernd Bischl; David Rügamer (ICML, 2024)

Towards Efficient MCMC Sampling in Bayesian Neural Networks by Exploiting Symmetry (Extended Abstract). - Jonas Gregor Wiese; Lisa Wimmer; Theodore Papamarkou; Bernd Bischl; Stephan Günnemann; David Rügamer (IJCAI, 2024)

Reshuffling Resampling Splits Can Improve Generalization of Hyperparameter Optimization. - Thomas Nagler; Lennart Schneider; Bernd Bischl; Matthias Feurer (NeurIPS, 2024)

On the Robustness of Global Feature Effect Explanations. - Hubert Baniecki; Giuseppe Casalicchio; Bernd Bischl; Przemyslaw Biecek (ECML/PKDD (2), 2024)

Attention-Driven Dropout: A Simple Method to Improve Self-supervised Contrastive Sentence Embeddings. - Fabian Stermann; Ilias Chalkidis; Amihossein Vahidi; Bernd Bischl; Mina Rezaei (ECML/PKDD (1), 2024)

Diversified Ensemble of Independent Sub-networks for Robust Self-supervised Representation Learning. - Amihossein Vahidi; Lisa Wimmer; Hüseyin Anil Gündüz; Bernd Bischl; Eyke Hüllermeier; Mina Rezaei (ECML/PKDD (1), 2024)

Constrained Probabilistic Mask Learning for Task-specific Undersampled MRI Reconstruction. - Tobias Weber; Michael Ingrisch; Bernd Bischl; David Rügamer (WACV, 2024)

mlr3summary: Concise and interpretable summaries for machine learning models. - Susanne Dandl; Marc Becker; Bernd Bischl; Giuseppe Casalicchio; Ludwig Bothmann (xAI (Late-breaking Work, Demos, Doctoral Consortium), 2024)

CountARFactuals - Generating Plausible Model-Agnostic Counterfactual Explanations with Adversarial Random Forests. - Susanne Dandl; Kristin Blesch; Timo Freiesleben; Gunnar König; Jan Kapar; Bernd Bischl; Marvin N. Wright (xAI (3), 2024)

A Guide to Feature Importance Methods for Scientific Inference. - Fiona Katharina Ewald; Ludwig Bothmann; Marvin N. Wright; Bernd Bischl; Giuseppe Casalicchio; Gunnar König (xAI (2), 2024)

Explaining Bayesian Optimization by Shapley Values Facilitates Human-AI Collaboration. - Julian Rodemann; Federico Croppi; Philipp Arens; Yusuf Sale; Julia Herbinger; Bernd Bischl; Eyke Hüllermeier; Thomas Augustin 001; Conor J. Walsh; Giuseppe Casalicchio (CoRR, 2024)

Training Survival Models using Scoring Rules. - Philipp Kopper; David Rügamer; Raphael Sonabend; Bernd Bischl; Andreas Bender 001 (CoRR, 2024)

Effector: A Python package for regional explanations. - Vasilis Gkolemis; Christos Diou; Eirini Ntoutsi; Theodore Dalamagas 001; Bernd Bischl; Julia Herbinger; Giuseppe Casalicchio (CoRR, 2024)

FinerCut: Finer-grained Interpretable Layer Pruning for Large Language Models. - Yang Zhang; Yawei Li; Xinpeng Wang 003; Qianli Shen; Barbara Plank; Bernd Bischl; Mina Rezaei; Kenji Kawaguchi (CoRR, 2024)

A Large-Scale Neutral Comparison Study of Survival Models on Low-Dimensional Data. - Lukas Burk; John Zobolas; Bernd Bischl; Andreas Bender 001; Marvin N. Wright; Raphael Sonabend (CoRR, 2024)

Constructing Confidence Intervals for 'the' Generalization Error - a Comprehensive Benchmark Study. - Hannah Schulz-Kümpel; Sebastian Fischer; Thomas Nagler; Anne-Laure Boulesteix; Bernd Bischl; Roman Hornung (CoRR, 2024)

Can Fairness be Automated? Guidelines and Opportunities for Fairness-aware AutoML. - Hilde J. P. Weerts; Florian Pfisterer; Matthias Feurer; Katharina Eggensperger; Edward Bergman; Noor H. Awad; Joaquin Vanschoren; Mykola Pechenizkiy; Bernd Bischl; Frank Hutter (J. Artif. Intell. Res., 2024)

AMLB: an AutoML Benchmark. - Pieter Gijsbers; Marcos L. P. Bueno; Stefan Coors; Erin LeDell; Sébastien Poirier; Janek Thomas; Bernd Bischl; Joaquin Vanschoren (J. Mach. Learn. Res., 2024)

Fusing structure from motion and simulation-augmented pose regression from optical flow for challenging indoor environments. - Felix Ott 001; Lucas Heublein; David Rügamer; Bernd Bischl; Christopher Mutschler (J. Vis. Commun. Image Represent., 2024)

A Dual-Perspective Approach to Evaluating Feature Attribution Methods. - Yawei Li; Yang Zhang; Kenji Kawaguchi; Ashkan Khakzar; Bernd Bischl; Mina Rezaei (Trans. Mach. Learn. Res., 2024)

Privacy-preserving and lossless distributed estimation of high-dimensional generalized additive mixed models. - Daniel Schalk; Bernd Bischl; David Rügamer (Stat. Comput., 2024)

Deep learning for survival analysis: a review. - Simon Wiegrebe; Philipp Kopper; Raphael Sonabend; Bernd Bischl; Andreas Bender 001 (Artif. Intell. Rev., 2024)

Model-agnostic feature importance and effects with dependent features: a conditional subgroup approach. - Christoph Molnar; Gunnar König; Bernd Bischl; Giuseppe Casalicchio (Data Min. Knowl. Discov., 2024)

Marginal effects for non-linear prediction functions. - Christian A. Scholbeck; Giuseppe Casalicchio; Christoph Molnar; Bernd Bischl; Christian Heumann (Data Min. Knowl. Discov., 2024)

Correction: Marginal effects for non-linear prediction functions. - Christian A. Scholbeck; Giuseppe Casalicchio; Christoph Molnar; Bernd Bischl; Christian Heumann (Data Min. Knowl. Discov., 2024)

What is Fairness? On Protected Attributes and Fictitious Worlds. - Ludwig Bothmann; Kristina Peters; Bernd Bischl (EWAF, 2025)

Efficient and Accurate Explanation Estimation with Distribution Compression. - Hubert Baniecki; Giuseppe Casalicchio; Bernd Bischl; Przemyslaw Biecek (ICLR, 2025)

Deep Weight Factorization: Sparse Learning Through the Lens of Artificial Symmetries. - Chris Kolb; Tobias Weber; Bernd Bischl; David Rügamer (ICLR, 2025)

Calibrating LLMs with Information-Theoretic Evidential Deep Learning. - Yawei Li; David Rügamer; Bernd Bischl; Mina Rezaei (ICLR, 2025)

Preventing Sensitive Information Leakage via Post-hoc Orthogonalization with Application to Chest Radiograph Embeddings. - Tobias Weber; Michael Ingrisch; Bernd Bischl; David Rügamer (PAKDD (4), 2025)

OpenML: Insights from 10 years and more than a thousand papers. - Bernd Bischl; Giuseppe Casalicchio; Taniya Das; Matthias Feurer; Sebastian Fischer; Pieter Gijsbers; Subhaditya Mukherjee; Andreas C. Müller 002; László Németh; Luis Oala; Lennart Purucker; Sahithya Ravi; et al. (Patterns, 2025)

fmeffects: An R Package for Forward Marginal Effects. - Holger Löwe; Christian A. Scholbeck; Christian Heumann; Bernd Bischl; Giuseppe Casalicchio (R J., 2025)

Privilege Scores. - Ludwig Bothmann; Philip A. Boustani; José M. Álvarez 002; Giuseppe Casalicchio; Bernd Bischl; Susanne Dandl (CoRR, 2025)

Trust Me, I Know the Way: Predictive Uncertainty in the Presence of Shortcut Learning. - Lisa Wimmer; Bernd Bischl; Ludwig Bothmann (CoRR, 2025)

Revisiting Unbiased Implicit Variational Inference. - Tobias Pielok; Bernd Bischl; David Rügamer (CoRR, 2025)

Semi-Implicit Variational Inference via Kernelized Path Gradient Descent. - Tobias Pielok; Bernd Bischl; David Rügamer (CoRR, 2025)

Overtuning in Hyperparameter Optimization. - Lennart Schneider; Bernd Bischl; Matthias Feurer (CoRR, 2025)

Revisiting Active Learning under (Human) Label Variation. - Cornelia Gruber; Helen Alber; Bernd Bischl; Göran Kauermann; Barbara Plank; Matthias Aßenmacher (CoRR, 2025)