Bernd Bischl - RankMe

313 papers A* 15A 12B 7C 4Journal 195Unranked 78

Year	Rank	Type	Title / Venue / Authors
2026	J	`jnl`	Analyzing Error Sources in Global Feature Effect Estimation. CoRR Timo Heiß, Coco Bögel, Bernd Bischl, Giuseppe Casalicchio
2026	J	`jnl`	CASHomon Sets: Efficient Rashomon Sets Across Multiple Model Classes and their Hyperparameters. CoRR Fiona Katharina Ewald, Martin Binder, Matthias Feurer, Bernd Bischl, Giuseppe Casalicchio
2026	J	`jnl`	Optimal Transport Group Counterfactual Explanations. CoRR Enrique Valero-Leal, Bernd Bischl, Pedro Larrañaga, Concha Bielza, Giuseppe Casalicchio
2026	J	`jnl`	Smoothing the Edges: Smooth Optimization for Sparse Regularization Using Hadamard Overparametrization. Mach. Learn. Chris Kolb, Christian L. Müller, Bernd Bischl, David Rügamer
2026	J	`jnl`	mlr3mbo: Bayesian Optimization in R. CoRR Marc Becker, Lennart Schneider, Martin Binder, Lars Kotthoff, Bernd Bischl
2026	J	`jnl`	xplainfi: Feature Importance and Statistical Inference for Machine Learning in R. CoRR Lukas Burk, Fiona Katharina Ewald, Giuseppe Casalicchio, Marvin N. Wright, Bernd Bischl
2025	A*	`conf`	Calibrating LLMs with Information-Theoretic Evidential Deep Learning. ICLR Yawei Li, David Rügamer, Bernd Bischl, Mina Rezaei
2025	J	`jnl`	Calibrating LLMs with Information-Theoretic Evidential Deep Learning. CoRR Yawei Li, David Rügamer, Bernd Bischl, Mina Rezaei
2025	J	`jnl`	Constructing Confidence Intervals for “the” Generalization Error – a Comprehensive Benchmark Study. J. Data-centric Mach. Learn. Res. Hannah Schulz-Kümpel, Sebastian Fischer, Roman Hornung, Anne-Laure Boulesteix, Thomas Nagler, Bernd Bischl
2025	A*	`conf`	Deep Weight Factorization: Sparse Learning Through the Lens of Artificial Symmetries. ICLR Chris Kolb, Tobias Weber, Bernd Bischl, David Rügamer
2025	J	`jnl`	Deep Weight Factorization: Sparse Learning Through the Lens of Artificial Symmetries. CoRR Chris Kolb, Tobias Weber, Bernd Bischl, David Rügamer
2025	J	`jnl`	Differentiable Sparsity via CoRR Chris Kolb, Laetitia Frost, Bernd Bischl, David Rügamer
2025	A*	`conf`	Efficient and Accurate Explanation Estimation with Distribution Compression. ICLR Hubert Baniecki, Giuseppe Casalicchio, Bernd Bischl, Przemyslaw Biecek
2025	—	`conf`	Enhancing Multi-Epitope Vaccine Effectiveness Through State-of-the-Art Proteasomal Cleavage Prediction With Deep Learning. BIBM Ingo Ziegler, Bolei Ma, Bernd Bischl, Benjamin Schubert, Emilio Dorigatti
2025	—	`conf`	Explaining Bayesian Optimization by Shapley Values Facilitates Human-AI Collaboration for Exosuit Personalization. ECML/PKDD (8) Julian Rodemann, Federico Croppi, Philipp Arens, Yusuf Sale, Julia Herbinger, Bernd Bischl, Eyke Hüllermeier, Thomas Augustin, Conor J. Walsh, Giuseppe Casalicchio
2025	—	`conf`	On Training Survival Models with Scoring Rules. ECML/PKDD (7) Philipp Kopper, David Rügamer, Raphael Sonabend, Bernd Bischl, Andreas Bender
2025	J	`jnl`	OpenML: Insights from 10 years and more than a thousand papers. Patterns Bernd Bischl, Giuseppe Casalicchio, Taniya Das, Matthias Feurer, Sebastian Fischer, Pieter Gijsbers, Subhaditya Mukherjee, Andreas C. Müller, László Németh, Luis Oala, Lennart Purucker, Sahithya Ravi, Jan N. van Rijn, Prabhant Singh, Joaquin Vanschoren, Jos van der Velde, Marcel Wever
2025	—	`conf`	Overtuning in Hyperparameter Optimization. AutoML Lennart Schneider, Bernd Bischl, Matthias Feurer
2025	J	`jnl`	Overtuning in Hyperparameter Optimization. CoRR Lennart Schneider, Bernd Bischl, Matthias Feurer
2025	—	`conf`	Preventing Sensitive Information Leakage via Post-hoc Orthogonalization with Application to Chest Radiograph Embeddings. PAKDD (4) Tobias Weber, Michael Ingrisch, Bernd Bischl, David Rügamer
2025	J	`jnl`	Privilege Scores. CoRR Ludwig Bothmann, Philip A. Boustani, José M. Álvarez, Giuseppe Casalicchio, Bernd Bischl, Susanne Dandl
2025	J	`jnl`	Revisiting Active Learning under (Human) Label Variation. CoRR Cornelia Gruber, Helen Alber, Bernd Bischl, Göran Kauermann, Barbara Plank, Matthias Aßenmacher
2025	A*	`conf`	Revisiting Unbiased Implicit Variational Inference. ICML Tobias Pielok, Bernd Bischl, David Rügamer
2025	J	`jnl`	Revisiting Unbiased Implicit Variational Inference. CoRR Tobias Pielok, Bernd Bischl, David Rügamer
2025	J	`jnl`	Semi-Implicit Variational Inference via Kernelized Path Gradient Descent. CoRR Tobias Pielok, Bernd Bischl, David Rügamer
2025	J	`jnl`	TABFAIRGDT: A Fast Fair Tabular Data Generator using Autoregressive Decision Trees. CoRR Emmanouil Panagiotou, Benoît Ronval, Arjun Roy, Ludwig Bothmann, Bernd Bischl, Siegfried Nijssen, Eirini Ntoutsi
2025	A*	`conf`	TabFairGDT: A Fast Fair Tabular Data Generator Using Autoregressive Decision Trees. ICDM Emmanouil Panagiotou, Benoît Ronval, Arjun Roy, Ludwig Bothmann, Bernd Bischl, Siegfried Nijssen, Eirini Ntoutsi
2025	J	`jnl`	Trust Me, I Know the Way: Predictive Uncertainty in the Presence of Shortcut Learning. CoRR Lisa Wimmer, Bernd Bischl, Ludwig Bothmann
2025	—	`conf`	What is Fairness? On Protected Attributes and Fictitious Worlds. EWAF Ludwig Bothmann, Kristina Peters, Bernd Bischl
2025	J	`jnl`	counterfactuals : An J. Stat. Softw. Susanne Dandl, Andreas Hofheinz, Martin Binder, Bernd Bischl, Giuseppe Casalicchio
2025	J	`jnl`	fmeffects: An R Package for Forward Marginal Effects. R J. Holger Löwe, Christian A. Scholbeck, Christian Heumann, Bernd Bischl, Giuseppe Casalicchio
2025	J	`jnl`	mlr3extralearners: Expanding the mlr3 Ecosystem with Community-Driven Learner Integration. J. Open Source Softw. Sebastian Fischer, John Zobolas, Raphael Sonabend, Marc Becker, Michel Lang, Martin Binder, Lennart Schneider, Lukas Burk, Patrick Schratz, Byron C. Jaeger, Stephen A. Lauer, Lorenz A. Kapsner, Maximilian Mücke, Zezhi Wang, Damir Pulatov, Keenan Ganz, Henri Funk, Liana Harutyunyan, Pierre Camilleri, Philipp Kopper, Andreas Bender, Baisu Zhou, Niko German, Lona Koers, Anna Nazarova, Bernd Bischl
2024	J	`jnl`	A Dual-Perspective Approach to Evaluating Feature Attribution Methods. Trans. Mach. Learn. Res. Yawei Li, Yang Zhang, Kenji Kawaguchi, Ashkan Khakzar, Bernd Bischl, Mina Rezaei
2024	—	`conf`	A Guide to Feature Importance Methods for Scientific Inference. xAI (2) Fiona Katharina Ewald, Ludwig Bothmann, Marvin N. Wright, Bernd Bischl, Giuseppe Casalicchio, Gunnar König
2024	J	`jnl`	A Guide to Feature Importance Methods for Scientific Inference. CoRR Fiona Katharina Ewald, Ludwig Bothmann, Marvin N. Wright, Bernd Bischl, Giuseppe Casalicchio, Gunnar König
2024	J	`jnl`	A Large-Scale Neutral Comparison Study of Survival Models on Low-Dimensional Data. CoRR Lukas Burk, John Zobolas, Bernd Bischl, Andreas Bender, Marvin N. Wright, Raphael Sonabend
2024	J	`jnl`	AMLB: an AutoML Benchmark. J. Mach. Learn. Res. Pieter Gijsbers, Marcos L. P. Bueno, Stefan Coors, Erin LeDell, Sébastien Poirier, Janek Thomas, Bernd Bischl, Joaquin Vanschoren
2024	—	`conf`	Attention-Driven Dropout: A Simple Method to Improve Self-supervised Contrastive Sentence Embeddings. ECML/PKDD (1) Fabian Stermann, Ilias Chalkidis, Amihossein Vahidi, Bernd Bischl, Mina Rezaei
2024	J	`jnl`	Can Fairness be Automated? Guidelines and Opportunities for Fairness-aware AutoML. J. Artif. Intell. Res. Hilde J. P. Weerts, Florian Pfisterer, Matthias Feurer, Katharina Eggensperger, Edward Bergman, Noor H. Awad, Joaquin Vanschoren, Mykola Pechenizkiy, Bernd Bischl, Frank Hutter
2024	A*	`conf`	Connecting the Dots: Is Mode-Connectedness the Key to Feasible Sample-Based Inference in Bayesian Neural Networks? ICML Emanuel Sommer, Lisa Wimmer, Theodore Papamarkou, Ludwig Bothmann, Bernd Bischl, David Rügamer
2024	J	`jnl`	Connecting the Dots: Is Mode-Connectedness the Key to Feasible Sample-Based Inference in Bayesian Neural Networks? CoRR Emanuel Sommer, Lisa Wimmer, Theodore Papamarkou, Ludwig Bothmann, Bernd Bischl, David Rügamer
2024	A	`conf`	Constrained Probabilistic Mask Learning for Task-specific Undersampled MRI Reconstruction. WACV Tobias Weber, Michael Ingrisch, Bernd Bischl, David Rügamer
2024	J	`jnl`	Constructing Confidence Intervals for 'the' Generalization Error - a Comprehensive Benchmark Study. CoRR Hannah Schulz-Kümpel, Sebastian Fischer, Thomas Nagler, Anne-Laure Boulesteix, Bernd Bischl, Roman Hornung
2024	J	`jnl`	Correction: Marginal effects for non-linear prediction functions. Data Min. Knowl. Discov. Christian A. Scholbeck, Giuseppe Casalicchio, Christoph Molnar, Bernd Bischl, Christian Heumann
2024	—	`conf`	CountARFactuals - Generating Plausible Model-Agnostic Counterfactual Explanations with Adversarial Random Forests. xAI (3) Susanne Dandl, Kristin Blesch, Timo Freiesleben, Gunnar König, Jan Kapar, Bernd Bischl, Marvin N. Wright
2024	J	`jnl`	CountARFactuals - Generating plausible model-agnostic counterfactual explanations with adversarial random forests. CoRR Susanne Dandl, Kristin Blesch, Timo Freiesleben, Gunnar König, Jan Kapar, Bernd Bischl, Marvin N. Wright
2024	J	`jnl`	Decomposing Global Feature Effects Based on Feature Interactions. J. Mach. Learn. Res. Julia Herbinger, Marvin N. Wright, Thomas Nagler, Bernd Bischl, Giuseppe Casalicchio
2024	J	`jnl`	Deep learning for survival analysis: a review. Artif. Intell. Rev. Simon Wiegrebe, Philipp Kopper, Raphael Sonabend, Bernd Bischl, Andreas Bender
2024	—	`conf`	Diversified Ensemble of Independent Sub-networks for Robust Self-supervised Representation Learning. ECML/PKDD (1) Amihossein Vahidi, Lisa Wimmer, Hüseyin Anil Gündüz, Bernd Bischl, Eyke Hüllermeier, Mina Rezaei
2024	J	`jnl`	Effector: A Python package for regional explanations. CoRR Vasilis Gkolemis, Christos Diou, Eirini Ntoutsi, Theodore Dalamagas, Bernd Bischl, Julia Herbinger, Giuseppe Casalicchio
2024	J	`jnl`	Efficient and Accurate Explanation Estimation with Distribution Compression. CoRR Hubert Baniecki, Giuseppe Casalicchio, Bernd Bischl, Przemyslaw Biecek
2024	J	`jnl`	Explaining Bayesian Optimization by Shapley Values Facilitates Human-AI Collaboration. CoRR Julian Rodemann, Federico Croppi, Philipp Arens, Yusuf Sale, Julia Herbinger, Bernd Bischl, Eyke Hüllermeier, Thomas Augustin, Conor J. Walsh, Giuseppe Casalicchio
2024	J	`jnl`	FinerCut: Finer-grained Interpretable Layer Pruning for Large Language Models. CoRR Yang Zhang, Yawei Li, Xinpeng Wang, Qianli Shen, Barbara Plank, Bernd Bischl, Mina Rezaei, Kenji Kawaguchi
2024	J	`jnl`	Fusing structure from motion and simulation-augmented pose regression from optical flow for challenging indoor environments. J. Vis. Commun. Image Represent. Felix Ott, Lucas Heublein, David Rügamer, Bernd Bischl, Christopher Mutschler
2024	J	`jnl`	Marginal effects for non-linear prediction functions. Data Min. Knowl. Discov. Christian A. Scholbeck, Giuseppe Casalicchio, Christoph Molnar, Bernd Bischl, Christian Heumann
2024	J	`jnl`	Model-agnostic feature importance and effects with dependent features: a conditional subgroup approach. Data Min. Knowl. Discov. Christoph Molnar, Gunnar König, Bernd Bischl, Giuseppe Casalicchio
2024	—	`conf`	On the Robustness of Global Feature Effect Explanations. ECML/PKDD (2) Hubert Baniecki, Giuseppe Casalicchio, Bernd Bischl, Przemyslaw Biecek
2024	J	`jnl`	On the Robustness of Global Feature Effect Explanations. CoRR Hubert Baniecki, Giuseppe Casalicchio, Bernd Bischl, Przemyslaw Biecek
2024	A*	`conf`	Position: A Call to Action for a Human-Centered AutoML Paradigm. ICML Marius Lindauer, Florian Karl, Anne Klier, Julia Moosbauer, Alexander Tornede, Andreas Müller, Frank Hutter, Matthias Feurer, Bernd Bischl
2024	J	`jnl`	Position: A Call to Action for a Human-Centered AutoML Paradigm. CoRR Marius Lindauer, Florian Karl, Anne Klier, Julia Moosbauer, Alexander Tornede, Andreas Müller, Frank Hutter, Matthias Feurer, Bernd Bischl
2024	A*	`conf`	Position: Why We Must Rethink Empirical Research in Machine Learning. ICML Moritz Herrmann, F. Julian D. Lange, Katharina Eggensperger, Giuseppe Casalicchio, Marcel Wever, Matthias Feurer, David Rügamer, Eyke Hüllermeier, Anne-Laure Boulesteix, Bernd Bischl
2024	J	`jnl`	Position: Why We Must Rethink Empirical Research in Machine Learning. CoRR Moritz Herrmann, F. Julian D. Lange, Katharina Eggensperger, Giuseppe Casalicchio, Marcel Wever, Matthias Feurer, David Rügamer, Eyke Hüllermeier, Anne-Laure Boulesteix, Bernd Bischl
2024	J	`jnl`	Privacy-preserving and lossless distributed estimation of high-dimensional generalized additive mixed models. Stat. Comput. Daniel Schalk, Bernd Bischl, David Rügamer
2024	A*	`conf`	Probabilistic Self-supervised Representation Learning via Scoring Rules Minimization. ICLR Amirhossein Vahidi, Simon Schoßer, Lisa Wimmer, Yawei Li, Bernd Bischl, Eyke Hüllermeier, Mina Rezaei
2024	A*	`conf`	Reshuffling Resampling Splits Can Improve Generalization of Hyperparameter Optimization. NeurIPS Thomas Nagler, Lennart Schneider, Bernd Bischl, Matthias Feurer
2024	J	`jnl`	Reshuffling Resampling Splits Can Improve Generalization of Hyperparameter Optimization. CoRR Thomas Nagler, Lennart Schneider, Bernd Bischl, Matthias Feurer
2024	A*	`conf`	Towards Efficient MCMC Sampling in Bayesian Neural Networks by Exploiting Symmetry (Extended Abstract). IJCAI Jonas Gregor Wiese, Lisa Wimmer, Theodore Papamarkou, Bernd Bischl, Stephan Günnemann, David Rügamer
2024	J	`jnl`	Training Survival Models using Scoring Rules. CoRR Philipp Kopper, David Rügamer, Raphael Sonabend, Bernd Bischl, Andreas Bender
2024	—	`conf`	mlr3summary: Concise and interpretable summaries for machine learning models. xAI (Late-breaking Work, Demos, Doctoral Consortium) Susanne Dandl, Marc Becker, Bernd Bischl, Giuseppe Casalicchio, Ludwig Bothmann
2024	J	`jnl`	mlr3summary: Concise and interpretable summaries for machine learning models. CoRR Susanne Dandl, Marc Becker, Bernd Bischl, Giuseppe Casalicchio, Ludwig Bothmann
2023	J	`jnl`	A Dual-Perspective Approach to Evaluating Feature Attribution Methods. CoRR Yawei Li, Yang Zhang, Kenji Kawaguchi, Ashkan Khakzar, Bernd Bischl, Mina Rezaei
2023	J	`jnl`	Accelerated Componentwise Gradient Boosting Using Efficient Data Representation and Momentum-Based Optimization. J. Comput. Graph. Stat. Daniel Schalk, Bernd Bischl, David Rügamer
2023	—	`conf`	ActiveGLAE: A Benchmark for Deep Active Learning with Transformers. ECML/PKDD (1) Lukas Rauch, Matthias Aßenmacher, Denis Huseljic, Moritz Wirth, Bernd Bischl, Bernhard Sick
2023	J	`jnl`	ActiveGLAE: A Benchmark for Deep Active Learning with Transformers. CoRR Lukas Rauch, Matthias Aßenmacher, Denis Huseljic, Moritz Wirth, Bernd Bischl, Bernhard Sick
2023	A*	`conf`	Approximate Bayesian Inference with Stein Functional Variational Gradient Descent. ICLR Tobias Pielok, Bernd Bischl, David Rügamer
2023	J	`jnl`	AttributionLab: Faithfulness of Feature Attribution Under Controllable Environments. CoRR Yang Zhang, Yawei Li, Hannah Brown, Mina Rezaei, Bernd Bischl, Philip H. S. Torr, Ashkan Khakzar, Kenji Kawaguchi
2023	J	`jnl`	Auxiliary Cross-Modal Representation Learning With Triplet Loss Functions for Online Handwriting Recognition. IEEE Access Felix Ott, David Rügamer, Lucas Heublein, Bernd Bischl, Christopher Mutschler
2023	—	`conf`	Bayesian Optimization. GECCO Companion Ivo Couckuyt, Sebastian Rojas-Gonzalez, Jürgen Branke, Bernd Bischl
2023	J	`jnl`	Can Fairness be Automated? Guidelines and Opportunities for Fairness-aware AutoML. CoRR Hilde J. P. Weerts, Florian Pfisterer, Matthias Feurer, Katharina Eggensperger, Edward Bergman, Noor H. Awad, Joaquin Vanschoren, Mykola Pechenizkiy, Bernd Bischl, Frank Hutter
2023	—	`conf`	Cascaded Latent Diffusion Models for High-Resolution Chest X-ray Synthesis. PAKDD (3) Tobias Weber, Michael Ingrisch, Bernd Bischl, David Rügamer
2023	J	`jnl`	Cascaded Latent Diffusion Models for High-Resolution Chest X-ray Synthesis. CoRR Tobias Weber, Michael Ingrisch, Bernd Bischl, David Rügamer
2023	J	`jnl`	Constrained Probabilistic Mask Learning for Task-specific Undersampled MRI Reconstruction. CoRR Tobias Weber, Michael Ingrisch, Bernd Bischl, David Rügamer
2023	B	`conf`	ConstraintMatch for Semi-constrained Clustering. IJCNN Jann Goschenhofer, Bernd Bischl, Zsolt Kira
2023	J	`jnl`	ConstraintMatch for Semi-constrained Clustering. CoRR Jann Goschenhofer, Bernd Bischl, Zsolt Kira
2023	J	`jnl`	Decomposing Global Feature Effects Based on Feature Interactions. CoRR Julia Herbinger, Bernd Bischl, Giuseppe Casalicchio
2023	J	`jnl`	Deep Bregman divergence for self-supervised representations learning. Comput. Vis. Image Underst. Mina Rezaei, Farzin Soleymani, Bernd Bischl, Shekoofeh Azizi
2023	J	`jnl`	Diversified Ensemble of Independent Sub-Networks for Robust Self-Supervised Representation Learning. CoRR Amirhossein Vahidi, Lisa Wimmer, Hüseyin Anil Gündüz, Bernd Bischl, Eyke Hüllermeier, Mina Rezaei
2023	—	`conf`	Efficient Document Embeddings via Self-Contrastive Bregman Divergence Learning. ACL (Findings) Daniel Saggau, Mina Rezaei, Bernd Bischl, Ilias Chalkidis
2023	J	`jnl`	Efficient Document Embeddings via Self-Contrastive Bregman Divergence Learning. CoRR Daniel Saggau, Mina Rezaei, Bernd Bischl, Ilias Chalkidis
2023	J	`jnl`	Evaluating machine learning models in non-standard settings: An overview and new findings. CoRR Roman Hornung, Malte Nalenz, Lennart Schneider, Andreas Bender, Ludwig Bothmann, Bernd Bischl, Thomas Augustin, Anne-Laure Boulesteix
2023	J	`jnl`	Fairness Audits and Debiasing Using \pkg{mlr3fairness}. R J. Florian Pfisterer, Siyi Wei, Sebastian J. Vollmer, Michel Lang, Bernd Bischl
2023	A	`conf`	Frequentist Uncertainty Quantification in Semi-Structured Neural Networks. AISTATS Emilio Dorigatti, Benjamin Schubert, Bernd Bischl, David Rügamer
2023	J	`jnl`	Fusing Structure from Motion and Simulation-Augmented Pose Regression from Optical Flow for Challenging Indoor Environments. CoRR Felix Ott, Lucas Heublein, David Rügamer, Bernd Bischl, Christopher Mutschler
2023	J	`jnl`	How Different Is Stereotypical Bias Across Languages? CoRR Ibrahim Tolga Öztürk, Rostislav Nedelchev, Christian Heumann, Esteban Garces Arias, Marius Roger, Bernd Bischl, Matthias Aßenmacher
2023	—	`conf`	How Different is Stereotypical Bias Across Languages? PKDD/ECML Workshops (1) Ibrahim Tolga Öztürk, Rostislav Nedelchev, Christian Heumann, Esteban Garces Arias, Marius Roger, Bernd Bischl, Matthias Aßenmacher
2023	J	`jnl`	Hyperparameter optimization: Foundations, algorithms, best practices, and open challenges. WIREs Data. Mining. Knowl. Discov. 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
2023	—	`conf`	Interpretable Regional Descriptors: Hyperbox-Based Local Explanations. ECML/PKDD (3) Susanne Dandl, Giuseppe Casalicchio, Bernd Bischl, Ludwig Bothmann
2023	J	`jnl`	Interpretable Regional Descriptors: Hyperbox-Based Local Explanations. CoRR Susanne Dandl, Giuseppe Casalicchio, Bernd Bischl, Ludwig Bothmann
2023	B	`conf`	Mind the Gap: Measuring Generalization Performance Across Multiple Objectives. IDA Matthias Feurer, Katharina Eggensperger, Edward Bergman, Florian Pfisterer, Bernd Bischl, Frank Hutter
2023	J	`jnl`	Multi-Objective Hyperparameter Optimization in Machine Learning - An Overview. ACM Trans. Evol. Learn. Optim. 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, Bernd Bischl
2023	A	`conf`	Multi-Objective Optimization of Performance and Interpretability of Tabular Supervised Machine Learning Models. GECCO Lennart Schneider, Bernd Bischl, Janek Thomas
2023	J	`jnl`	Multi-Objective Optimization of Performance and Interpretability of Tabular Supervised Machine Learning Models. CoRR Lennart Schneider, Bernd Bischl, Janek Thomas
2023	C	`conf`	Neural Architecture Search for Genomic Sequence Data. CIBCB Amadeu Scheppach, Hüseyin Anil Gündüz, Emilio Dorigatti, Philipp C. Münch, Alice C. McHardy, Bernd Bischl, Mina Rezaei, Martin Binder
2023	A	`conf`	Neural Networks as Black-Box Benchmark Functions Optimized for Exploratory Landscape Features. FOGA Raphael Patrick Prager, Konstantin Dietrich, Lennart Schneider, Lennart Schäpermeier, Bernd Bischl, Pascal Kerschke, Heike Trautmann, Olaf Mersmann
2023	J	`jnl`	Position Paper: Bridging the Gap Between Machine Learning and Sensitivity Analysis. CoRR Christian A. Scholbeck, Julia Moosbauer, Giuseppe Casalicchio, Hoshin Gupta, Bernd Bischl, Christian Heumann
2023	J	`jnl`	Probabilistic Self-supervised Learning via Scoring Rules Minimization. CoRR Amirhossein Vahidi, Simon Schoßer, Lisa Wimmer, Yawei Li, Bernd Bischl, Eyke Hüllermeier, Mina Rezaei
2023	—	`conf`	Q(D)O-ES: Population-based Quality (Diversity) Optimisation for Post Hoc Ensemble Selection in AutoML. AutoML Lennart Oswald Purucker, Lennart Schneider, Marie Anastacio, Joeran Beel, Bernd Bischl, Holger H. Hoos
2023	J	`jnl`	Q(D)O-ES: Population-based Quality (Diversity) Optimisation for Post Hoc Ensemble Selection in AutoML. CoRR Lennart Purucker, Lennart Schneider, Marie Anastacio, Joeran Beel, Bernd Bischl, Holger H. Hoos
2023	A	`conf`	Quantifying aleatoric and epistemic uncertainty in machine learning: Are conditional entropy and mutual information appropriate measures? UAI Lisa Wimmer, Yusuf Sale, Paul Hofman, Bernd Bischl, Eyke Hüllermeier
2023	—	`conf`	Relating the Partial Dependence Plot and Permutation Feature Importance to the Data Generating Process. xAI (1) Christoph Molnar, Timo Freiesleben, Gunnar König, Julia Herbinger, Tim Reisinger, Giuseppe Casalicchio, Marvin N. Wright, Bernd Bischl
2023	J	`jnl`	Representation Learning for Tablet and Paper Domain Adaptation in Favor of Online Handwriting Recognition. CoRR Felix Ott, David Rügamer, Lucas Heublein, Bernd Bischl, Christopher Mutschler
2023	J	`jnl`	Smoothing the Edges: A General Framework for Smooth Optimization in Sparse Regularization using Hadamard Overparametrization. CoRR Chris Kolb, Christian L. Müller, Bernd Bischl, David Rügamer
2023	J	`jnl`	Structured Verification of Machine Learning Models in Industrial Settings. Big Data Sai Rahul Kaminwar, Jann Goschenhofer, Janek Thomas, Ingo Thon, Bernd Bischl
2023	—	`conf`	Symbolic Explanations for Hyperparameter Optimization. AutoML Sarah Segel, Helena Graf, Alexander Tornede, Bernd Bischl, Marius Lindauer
2023	—	`conf`	Towards Efficient MCMC Sampling in Bayesian Neural Networks by Exploiting Symmetry. ECML/PKDD (1) Jonas Gregor Wiese, Lisa Wimmer, Theodore Papamarkou, Bernd Bischl, Stephan Günnemann, David Rügamer
2023	J	`jnl`	Towards Efficient MCMC Sampling in Bayesian Neural Networks by Exploiting Symmetry. CoRR Jonas Gregor Wiese, Lisa Wimmer, Theodore Papamarkou, Bernd Bischl, Stephan Günnemann, David Rügamer
2023	—	`conf`	Towards Enhancing Deep Active Learning with Weak Supervision and Constrained Clustering. IAL@PKDD/ECML Matthias Aßenmacher, Lukas Rauch, Jann Goschenhofer, Andreas Stephan, Bernd Bischl, Benjamin Roth, Bernhard Sick
2023	C	`conf`	Uncertainty Quantification for Deep Learning Models Predicting the Regulatory Activity of DNA Sequences. ICMLA Hüseyin Anil Gündüz, Sheetal Giri, Martin Binder, Bernd Bischl, Mina Rezaei
2023	J	`jnl`	Unreading Race: Purging Protected Features from Chest X-ray Embeddings. CoRR Tobias Weber, Michael Ingrisch, Bernd Bischl, David Rügamer
2023	J	`jnl`	counterfactuals: An R Package for Counterfactual Explanation Methods. CoRR Susanne Dandl, Andreas Hofheinz, Martin Binder, Bernd Bischl, Giuseppe Casalicchio
2023	J	`jnl`	deepregression: A Flexible Neural Network Framework for Semi-Structured Deep Distributional Regression. J. Stat. Softw. 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, Nadja Klein, Christian L. Müller
2023	J	`jnl`	dsBinVal: Conducting distributed ROC analysis using DataSHIELD. J. Open Source Softw. Daniel Schalk, Verena S. Hoffmann, Bernd Bischl, Ulrich Mansmann
2023	J	`jnl`	fmeffects: An R Package for Forward Marginal Effects. CoRR Holger Löwe, Christian A. Scholbeck, Christian Heumann, Bernd Bischl, Giuseppe Casalicchio
2022	J	`jnl`	A Collection of Quality Diversity Optimization Problems Derived from Hyperparameter Optimization of Machine Learning Models. CoRR Lennart Schneider, Florian Pfisterer, Janek Thomas, Bernd Bischl
2022	—	`conf`	A collection of quality diversity optimization problems derived from hyperparameter optimization of machine learning models. GECCO Companion Lennart Schneider, Florian Pfisterer, Janek Thomas, Bernd Bischl
2022	J	`jnl`	AMLB: an AutoML Benchmark. CoRR Pieter Gijsbers, Marcos L. P. Bueno, Stefan Coors, Erin LeDell, Sébastien Poirier, Janek Thomas, Bernd Bischl, Joaquin Vanschoren
2022	J	`jnl`	Analyzing the Effects of Handling Data Imbalance on Learned Features from Medical Images by Looking Into the Models. CoRR Ashkan Khakzar, Yawei Li, Yang Zhang, Mirac Sanisoglu, Seong Tae Kim, Mina Rezaei, Bernd Bischl, Nassir Navab
2022	J	`jnl`	Automated Benchmark-Driven Design and Explanation of Hyperparameter Optimizers. IEEE Trans. Evol. Comput. Julia Moosbauer, Martin Binder, Lennart Schneider, Florian Pfisterer, Marc Becker, Michel Lang, Lars Kotthoff, Bernd Bischl
2022	—	`conf`	Bayesian uncertainty estimation for detection of long-tail and unseen conditions in abdominal images. Computer-Aided Diagnosis Mina Rezaei, Janne J. Näppi, Bernd Bischl, Hiroyuki Yoshida
2022	J	`jnl`	Benchmarking Online Sequence-to-Sequence and Character-based Handwriting Recognition from IMU-Enhanced Pens. CoRR Felix Ott, David Rügamer, Lucas Heublein, Tim Hamann, Jens Barth, Bernd Bischl, Christopher Mutschler
2022	J	`jnl`	Benchmarking Visual-Inertial Deep Multimodal Fusion for Relative Pose Regression and Odometry-aided Absolute Pose Regression. CoRR Felix Ott, Nisha Lakshmana Raichur, David Rügamer, Tobias Feigl, Heiko Neumann, Bernd Bischl, Christopher Mutschler
2022	J	`jnl`	Benchmarking online sequence-to-sequence and character-based handwriting recognition from IMU-enhanced pens. Int. J. Document Anal. Recognit. Felix Ott, David Rügamer, Lucas Heublein, Tim Hamann, Jens Barth, Bernd Bischl, Christopher Mutschler
2022	J	`jnl`	Cross-Modal Common Representation Learning with Triplet Loss Functions. CoRR Felix Ott, David Rügamer, Lucas Heublein, Bernd Bischl, Christopher Mutschler
2022	B	`conf`	Deep variational clustering framework for self-labeling large-scale medical images. Image Processing Farzin Soleymani, Mohammad Eslami, Tobias Elze, Bernd Bischl, Mina Rezaei
2022	—	`conf`	DeepPAMM: Deep Piecewise Exponential Additive Mixed Models for Complex Hazard Structures in Survival Analysis. PAKDD (2) Philipp Kopper, Simon Wiegrebe, Bernd Bischl, Andreas Bender, David Rügamer
2022	J	`jnl`	DeepPAMM: Deep Piecewise Exponential Additive Mixed Models for Complex Hazard Structures in Survival Analysis. CoRR Philipp Kopper, Simon Wiegrebe, Bernd Bischl, Andreas Bender, David Rügamer
2022	—	`conf`	Developing Open Source Educational Resources for Machine Learning and Data Science. Teaching ML Ludwig Bothmann, Sven Strickroth, Giuseppe Casalicchio, David Rügamer, Marius Lindauer, Fabian Scheipl, Bernd Bischl
2022	J	`jnl`	Distributed non-disclosive validation of predictive models by a modified ROC-GLM. CoRR Daniel Schalk, Verena S. Hoffmann, Bernd Bischl, Ulrich Mansmann
2022	A*	`conf`	Domain Adaptation for Time-Series Classification to Mitigate Covariate Shift. ACM Multimedia Felix Ott, David Rügamer, Lucas Heublein, Bernd Bischl, Christopher Mutschler
2022	J	`jnl`	Domain Adaptation for Time-Series Classification to Mitigate Covariate Shift. CoRR Felix Ott, David Rügamer, Lucas Heublein, Bernd Bischl, Christopher Mutschler
2022	—	`conf`	Efficient Automated Deep Learning for Time Series Forecasting. ECML/PKDD (3) Difan Deng, Florian Karl, Frank Hutter, Bernd Bischl, Marius Lindauer
2022	J	`jnl`	Efficient Automated Deep Learning for Time Series Forecasting. CoRR Difan Deng, Florian Karl, Frank Hutter, Bernd Bischl, Marius Lindauer
2022	J	`jnl`	Enhancing Explainability of Hyperparameter Optimization via Bayesian Algorithm Execution. CoRR Julia Moosbauer, Giuseppe Casalicchio, Marius Lindauer, Bernd Bischl
2022	—	`conf`	Factorized Structured Regression for Large-Scale Varying Coefficient Models. ECML/PKDD (5) David Rügamer, Andreas Bender, Simon Wiegrebe, Daniel Racek, Bernd Bischl, Christian L. Müller, Clemens Stachl
2022	J	`jnl`	Factorized Structured Regression for Large-Scale Varying Coefficient Models. CoRR David Rügamer, Andreas Bender, Simon Wiegrebe, Daniel Racek, Bernd Bischl, Christian L. Müller, Clemens Stachl
2022	A*	`conf`	FiLM-Ensemble: Probabilistic Deep Learning via Feature-wise Linear Modulation. NeurIPS Mehmet Ozgur Turkoglu, Alexander Becker, Hüseyin Anil Gündüz, Mina Rezaei, Bernd Bischl, Rodrigo Caye Daudt, Stefano D'Aronco, Jan D. Wegner, Konrad Schindler
2022	J	`jnl`	FiLM-Ensemble: Probabilistic Deep Learning via Feature-wise Linear Modulation. CoRR Mehmet Ozgur Turkoglu, Alexander Becker, Hüseyin Anil Gündüz, Mina Rezaei, Bernd Bischl, Rodrigo Caye Daudt, Stefano D'Aronco, Jan Dirk Wegner, Konrad Schindler
2022	J	`jnl`	Grouped feature importance and combined features effect plot. Data Min. Knowl. Discov. Quay Au, Julia Herbinger, Clemens Stachl, Bernd Bischl, Giuseppe Casalicchio
2022	J	`jnl`	HPO X ELA: Investigating Hyperparameter Optimization Landscapes by Means of Exploratory Landscape Analysis. CoRR Lennart Schneider, Lennart Schäpermeier, Raphael Patrick Prager, Bernd Bischl, Heike Trautmann, Pascal Kerschke
2022	—	`conf`	HPO ˟ ELA: Investigating Hyperparameter Optimization Landscapes by Means of Exploratory Landscape Analysis. PPSN (1) Lennart Schneider, Lennart Schäpermeier, Raphael Patrick Prager, Bernd Bischl, Heike Trautmann, Pascal Kerschke
2022	—	`conf`	Implicit Embeddings via GAN Inversion for High Resolution Chest Radiographs. MAD@MICCAI Tobias Weber, Michael Ingrisch, Bernd Bischl, David Rügamer
2022	J	`jnl`	Improved proteasomal cleavage prediction with positive-unlabeled learning. CoRR Emilio Dorigatti, Bernd Bischl, Benjamin Schubert
2022	A	`conf`	Joint Classification and Trajectory Regression of Online Handwriting using a Multi-Task Learning Approach. WACV Felix Ott, David Rügamer, Lucas Heublein, Bernd Bischl, Christopher Mutschler
2022	—	`conf`	Joint Debiased Representation Learning and Imbalanced Data Clustering. ICDM (Workshops) Mina Rezaei, Emilio Dorigatti, David Rügamer, Bernd Bischl
2022	J	`jnl`	Joint Debiased Representation and Image Clustering Learning with Self-Supervision. CoRR Shunjie-Fabian Zheng, JaeEun Nam, Emilio Dorigatti, Bernd Bischl, Shekoofeh Azizi, Mina Rezaei
2022	J	`jnl`	Marginal Effects for Non-Linear Prediction Functions. CoRR Christian A. Scholbeck, Giuseppe Casalicchio, Christoph Molnar, Bernd Bischl, Christian Heumann
2022	J	`jnl`	Mind the Gap: Measuring Generalization Performance Across Multiple Objectives. CoRR Matthias Feurer, Katharina Eggensperger, Edward Bergman, Florian Pfisterer, Bernd Bischl, Frank Hutter
2022	J	`jnl`	Multi-Objective Hyperparameter Optimization - An Overview. CoRR 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, Bernd Bischl
2022	—	`conf`	Multi-objective counterfactual fairness. GECCO Companion Susanne Dandl, Florian Pfisterer, Bernd Bischl
2022	J	`jnl`	Positive-Unlabeled Learning with Uncertainty-aware Pseudo-label Selection. CoRR Emilio Dorigatti, Jann Goschenhofer, Benjamin Schubert, Mina Rezaei, Bernd Bischl
2022	J	`jnl`	Privacy-Preserving and Lossless Distributed Estimation of High-Dimensional Generalized Additive Mixed Models. CoRR Daniel Schalk, Bernd Bischl, David Rügamer
2022	A	`conf`	REPID: Regional Effect Plots with implicit Interaction Detection. AISTATS Julia Herbinger, Bernd Bischl, Giuseppe Casalicchio
2022	J	`jnl`	REPID: Regional Effect Plots with implicit Interaction Detection. CoRR Julia Herbinger, Bernd Bischl, Giuseppe Casalicchio
2022	J	`jnl`	Regularized target encoding outperforms traditional methods in supervised machine learning with high cardinality features. Comput. Stat. Florian Pargent, Florian Pfisterer, Janek Thomas, Bernd Bischl
2022	—	`conf`	Representation Learning for Tablet and Paper Domain Adaptation in Favor of Online Handwriting Recognition. ICPR Workshops (1) Felix Ott, David Rügamer, Lucas Heublein, Bernd Bischl, Christopher Mutschler
2022	J	`jnl`	Robust and Efficient Imbalanced Positive-Unlabeled Learning with Self-supervision. CoRR Emilio Dorigatti, Jonas Schweisthal, Bernd Bischl, Mina Rezaei
2022	—	`conf`	Tackling Neural Architecture Search With Quality Diversity Optimization. AutoML Lennart Schneider, Florian Pfisterer, Paul Kent, Jürgen Branke, Bernd Bischl, Janek Thomas
2022	J	`jnl`	Tackling Neural Architecture Search With Quality Diversity Optimization. CoRR Lennart Schneider, Florian Pfisterer, Paul Kent, Jürgen Branke, Bernd Bischl, Janek Thomas
2022	J	`jnl`	Uncertainty-aware Evaluation of Time-Series Classification for Online Handwriting Recognition with Domain Shift. CoRR Andreas Klaß, Sven M. Lorenz, Martin W. Lauer-Schmaltz, David Rügamer, Bernd Bischl, Christopher Mutschler, Felix Ott
2022	—	`conf`	Uncertainty-aware Evaluation of Time-series Classification for Online Handwriting Recognition with Domain Shift. STRL@IJCAI Andreas Klaß, Sven M. Lorenz, Martin W. Lauer-Schmaltz, David Rügamer, Bernd Bischl, Christopher Mutschler, Felix Ott
2022	J	`jnl`	What Is Fairness? Implications For FairML. CoRR Ludwig Bothmann, Kristina Peters, Bernd Bischl
2022	—	`conf`	YAHPO Gym - An Efficient Multi-Objective Multi-Fidelity Benchmark for Hyperparameter Optimization. AutoML Florian Pfisterer, Lennart Schneider, Julia Moosbauer, Martin Binder, Bernd Bischl
2021	J	`jnl`	Accelerated Componentwise Gradient Boosting using Efficient Data Representation and Momentum-based Optimization. CoRR Daniel Schalk, Bernd Bischl, David Rügamer
2021	J	`jnl`	Automated Benchmark-Driven Design and Explanation of Hyperparameter Optimizers. CoRR Julia Moosbauer, Martin Binder, Lennart Schneider, Florian Pfisterer, Marc Becker, Michel Lang, Lars Kotthoff, Bernd Bischl
2021	J	`jnl`	Automated Online Experiment-Driven Adaptation-Mechanics and Cost Aspects. IEEE Access Ilias Gerostathopoulos, Frantisek Plásil, Christian Prehofer, Janek Thomas, Bernd Bischl
2021	J	`jnl`	Automatic Componentwise Boosting: An Interpretable AutoML System. CoRR Stefan Coors, Daniel Schalk, Bernd Bischl, David Rügamer
2021	J	`jnl`	Decomposition of Global Feature Importance into Direct and Associative Components (DEDACT). CoRR Gunnar König, Timo Freiesleben, Bernd Bischl, Giuseppe Casalicchio, Moritz Grosse-Wentrup
2021	J	`jnl`	Deep Bregman Divergence for Contrastive Learning of Visual Representations. CoRR Mina Rezaei, Farzin Soleymani, Bernd Bischl, Shekoofeh Azizi
2021	J	`jnl`	Deep Semi-Supervised Learning for Time Series Classification. CoRR Jann Goschenhofer, Rasmus Hvingelby, David Rügamer, Janek Thomas, Moritz Wagner, Bernd Bischl
2021	C	`conf`	Deep Semi-supervised Learning for Time Series Classification. ICMLA Jann Goschenhofer, Rasmus Hvingelby, David Rügamer, Janek Thomas, Moritz Wagner, Bernd Bischl
2021	J	`jnl`	Deep Variational Clustering Framework for Self-labeling of Large-scale Medical Images. CoRR Farzin Soleymani, Mohammad Eslami, Tobias Elze, Bernd Bischl, Mina Rezaei
2021	J	`jnl`	Developing Open Source Educational Resources for Machine Learning and Data Science. CoRR Ludwig Bothmann, Sven Strickroth, Giuseppe Casalicchio, David Rügamer, Marius Lindauer, Fabian Scheipl, Bernd Bischl
2021	A*	`conf`	Explaining Hyperparameter Optimization via Partial Dependence Plots. NeurIPS Julia Moosbauer, Julia Herbinger, Giuseppe Casalicchio, Marius Lindauer, Bernd Bischl
2021	J	`jnl`	Explaining Hyperparameter Optimization via Partial Dependence Plots. CoRR Julia Moosbauer, Julia Herbinger, Giuseppe Casalicchio, Marius Lindauer, Bernd Bischl
2021	J	`jnl`	Grouped Feature Importance and Combined Features Effect Plot. CoRR Quay Au, Julia Herbinger, Clemens Stachl, Bernd Bischl, Giuseppe Casalicchio
2021	J	`jnl`	Hyperparameter Optimization: Foundations, Algorithms, Best Practices and Open Challenges. CoRR 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
2021	J	`jnl`	Improved Outcome Prediction Across Data Sources Through Robust Parameter Tuning. J. Classif. Nicole Ellenbach, Anne-Laure Boulesteix, Bernd Bischl, Kristian Unger, Roman Hornung
2021	J	`jnl`	Learning Statistical Representation with Joint Deep Embedded Clustering. CoRR Mina Rezaei, Emilio Dorigatti, David Rügamer, Bernd Bischl
2021	—	`conf`	Learning multiple defaults for machine learning algorithms. GECCO Companion Florian Pfisterer, Jan N. van Rijn, Philipp Probst, Andreas C. Müller, Bernd Bischl
2021	J	`jnl`	Meta-Learning for Symbolic Hyperparameter Defaults. CoRR Pieter Gijsbers, Florian Pfisterer, Jan N. van Rijn, Bernd Bischl, Joaquin Vanschoren
2021	—	`conf`	Meta-learning for symbolic hyperparameter defaults. GECCO Companion Pieter Gijsbers, Florian Pfisterer, Jan N. van Rijn, Bernd Bischl, Joaquin Vanschoren
2021	J	`jnl`	Monitoring Forest Health Using Hyperspectral Imagery: Does Feature Selection Improve the Performance of Machine-Learning Techniques? Remote. Sens. Patrick Schratz, Jannes Muenchow, Eugenia Iturritxa, José Cortés, Bernd Bischl, Alexander Brenning
2021	J	`jnl`	Mutation is all you need. CoRR Lennart Schneider, Florian Pfisterer, Martin Binder, Bernd Bischl
2021	—	`conf`	OpenML Benchmarking Suites. NeurIPS Datasets and Benchmarks Bernd Bischl, Giuseppe Casalicchio, Matthias Feurer, Pieter Gijsbers, Frank Hutter, Michel Lang, Rafael Gomes Mantovani, Jan N. van Rijn, Joaquin Vanschoren
2021	J	`jnl`	Regularized target encoding outperforms traditional methods in supervised machine learning with high cardinality features. CoRR Florian Pargent, Florian Pfisterer, Janek Thomas, Bernd Bischl
2021	J	`jnl`	Relating the Partial Dependence Plot and Permutation Feature Importance to the Data Generating Process. CoRR Christoph Molnar, Timo Freiesleben, Gunnar König, Giuseppe Casalicchio, Marvin N. Wright, Bernd Bischl
2021	—	`conf`	Semi-Structured Deep Piecewise Exponential Models. SPACA Philipp Kopper, Sebastian Pölsterl, Christian Wachinger, Bernd Bischl, Andreas Bender, David Rügamer
2021	J	`jnl`	Survival-oriented embeddings for improving accessibility to complex data structures. CoRR Tobias Weber, Michael Ingrisch, Matthias Fabritius, Bernd Bischl, David Rügamer
2021	J	`jnl`	Towards modelling hazard factors in unstructured data spaces using gradient-based latent interpolation. CoRR Tobias Weber, Michael Ingrisch, Bernd Bischl, David Rügamer
2021	J	`jnl`	YAHPO Gym - Design Criteria and a new Multifidelity Benchmark for Hyperparameter Optimization. CoRR Florian Pfisterer, Lennart Schneider, Julia Moosbauer, Martin Binder, Bernd Bischl
2021	J	`jnl`	deepregression: a Flexible Neural Network Framework for Semi-Structured Deep Distributional Regression. CoRR 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
2021	J	`jnl`	mcboost: Multi-Calibration Boosting for R. J. Open Source Softw. Florian Pfisterer, Christoph Kern, Susanne Dandl, Matthew Sun, Michael P. Kim, Bernd Bischl
2021	J	`jnl`	mlr3pipelines - Flexible Machine Learning Pipelines in R. J. Mach. Learn. Res. Martin Binder, Florian Pfisterer, Michel Lang, Lennart Schneider, Lars Kotthoff, Bernd Bischl
2021	J	`jnl`	mlr3proba: an R package for machine learning in survival analysis. Bioinform. Raphael Sonabend, Franz J. Király, Andreas Bender, Bernd Bischl, Michel Lang
2020	—	`conf`	A General Machine Learning Framework for Survival Analysis. ECML/PKDD (3) Andreas Bender, David Rügamer, Fabian Scheipl, Bernd Bischl
2020	J	`jnl`	A General Machine Learning Framework for Survival Analysis. CoRR Andreas Bender, David Rügamer, Fabian Scheipl, Bernd Bischl
2020	J	`jnl`	Benchmark for filter methods for feature selection in high-dimensional classification data. Comput. Stat. Data Anal. Andrea Bommert, Xudong Sun, Bernd Bischl, Jörg Rahnenführer, Michel Lang
2020	J	`jnl`	Debiasing classifiers: is reality at variance with expectation? CoRR Ashrya Agrawal, Florian Pfisterer, Bernd Bischl, Jiahao Chen, Srijan Sood, Sameena Shah, Francois Buet-Golfouse, Bilal A. Mateen, Sebastian J. Vollmer
2020	—	`conf`	General Pitfalls of Model-Agnostic Interpretation Methods for Machine Learning Models. xxAI@ICML Christoph Molnar, Gunnar König, Julia Herbinger, Timo Freiesleben, Susanne Dandl, Christian A. Scholbeck, Giuseppe Casalicchio, Moritz Grosse-Wentrup, Bernd Bischl
2020	—	`conf`	Interpretable Machine Learning - A Brief History, State-of-the-Art and Challenges. PKDD/ECML Workshops Christoph Molnar, Giuseppe Casalicchio, Bernd Bischl
2020	J	`jnl`	Interpretable Machine Learning - A Brief History, State-of-the-Art and Challenges. CoRR Christoph Molnar, Giuseppe Casalicchio, Bernd Bischl
2020	J	`jnl`	Model-agnostic Feature Importance and Effects with Dependent Features - A Conditional Subgroup Approach. CoRR Christoph Molnar, Gunnar König, Bernd Bischl, Giuseppe Casalicchio
2020	—	`conf`	Multi-Objective Counterfactual Explanations. PPSN (1) Susanne Dandl, Christoph Molnar, Martin Binder, Bernd Bischl
2020	J	`jnl`	Multi-Objective Counterfactual Explanations. CoRR Susanne Dandl, Christoph Molnar, Martin Binder, Bernd Bischl
2020	A	`conf`	Multi-objective hyperparameter tuning and feature selection using filter ensembles. GECCO Martin Binder, Julia Moosbauer, Janek Thomas, Bernd Bischl
2020	J	`jnl`	Neural Mixture Distributional Regression. CoRR David Rügamer, Florian Pfisterer, Bernd Bischl
2020	J	`jnl`	Pitfalls to Avoid when Interpreting Machine Learning Models. CoRR Christoph Molnar, Gunnar König, Julia Herbinger, Timo Freiesleben, Susanne Dandl, Christian A. Scholbeck, Giuseppe Casalicchio, Moritz Grosse-Wentrup, Bernd Bischl
2020	—	`ed.`	Proceedings of the First Teaching Machine Learning and Artificial Intelligence Workshop, September 8+14, 2020, Virtual Conference. Teaching ML Bernd Bischl, Oliver Guhr, Heidi Seibold, Peter Steinbach
2020	B	`conf`	Relative Feature Importance. ICPR Gunnar König, Christoph Molnar, Bernd Bischl, Moritz Grosse-Wentrup
2020	J	`jnl`	Relative Feature Importance. CoRR Gunnar König, Christoph Molnar, Bernd Bischl, Moritz Grosse-Wentrup
2020	J	`jnl`	Semi-Structured Deep Piecewise Exponential Models. CoRR Philipp Kopper, Sebastian Pölsterl, Christian Wachinger, Bernd Bischl, Andreas Bender, David Rügamer
2020	J	`jnl`	mlr3proba: Machine Learning Survival Analysis in R. CoRR Raphael Sonabend, Franz J. Király, Andreas Bender, Bernd Bischl, Michel Lang
2019	J	`jnl`	An Open Source AutoML Benchmark. CoRR Pieter Gijsbers, Erin LeDell, Janek Thomas, Sébastien Poirier, Bernd Bischl, Joaquin Vanschoren
2019	J	`jnl`	Benchmarking time series classification - Functional data vs machine learning approaches. CoRR Florian Pfisterer, Laura Beggel, Xudong Sun, Fabian Scheipl, Bernd Bischl
2019	J	`jnl`	Component-Wise Boosting of Targets for Multi-Output Prediction. CoRR Quay Au, Daniel Schalk, Giuseppe Casalicchio, Ramona Schödel, Clemens Stachl, Bernd Bischl
2019	—	`conf`	High Dimensional Restrictive Federated Model Selection with Multi-objective Bayesian Optimization over Shifted Distributions. IntelliSys (1) Xudong Sun, Andrea Bommert, Florian Pfisterer, Jörg Rahnenführer, Michel Lang, Bernd Bischl
2019	J	`jnl`	High Dimensional Restrictive Federated Model Selection with multi-objective Bayesian Optimization over shifted distributions. CoRR Xudong Sun, Andrea Bommert, Florian Pfisterer, Jörg Rahnenführer, Michel Lang, Bernd Bischl
2019	J	`jnl`	Model-Agnostic Approaches to Multi-Objective Simultaneous Hyperparameter Tuning and Feature Selection. CoRR Martin Binder, Julia Moosbauer, Janek Thomas, Bernd Bischl
2019	J	`jnl`	Multi-Objective Automatic Machine Learning with AutoxgboostMC. CoRR Florian Pfisterer, Stefan Coors, Janek Thomas, Bernd Bischl
2019	J	`jnl`	OpenML: An R package to connect to the machine learning platform OpenML. Comput. Stat. Giuseppe Casalicchio, Jakob Bossek, Michel Lang, Dominik Kirchhoff, Pascal Kerschke, Benjamin Hofner, Heidi Seibold, Joaquin Vanschoren, Bernd Bischl
2019	J	`jnl`	Proceedings of Reisensburg 2016-2017. Comput. Stat. Matthias Schmid, Bernd Bischl, Hans A. Kestler
2019	J	`jnl`	Quantifying Interpretability of Arbitrary Machine Learning Models Through Functional Decomposition. CoRR Christoph Molnar, Giuseppe Casalicchio, Bernd Bischl
2019	—	`conf`	Quantifying Model Complexity via Functional Decomposition for Better Post-hoc Interpretability. PKDD/ECML Workshops (1) Christoph Molnar, Giuseppe Casalicchio, Bernd Bischl
2019	—	`conf`	ReinBo: Machine Learning Pipeline Conditional Hierarchy Search and Configuration with Bayesian Optimization Embedded Reinforcement Learning. PKDD/ECML Workshops (1) Xudong Sun, Jiali Lin, Bernd Bischl
2019	J	`jnl`	ReinBo: Machine Learning pipeline search and configuration with Bayesian Optimization embedded Reinforcement Learning. CoRR Xudong Sun, Jiali Lin, Bernd Bischl
2019	J	`jnl`	Resampling-based Assessment of Robustness to Distribution Shift for Deep Neural Networks. CoRR Xudong Sun, Yu Wang, Alexej Gossmann, Bernd Bischl
2019	—	`conf`	Robust Anomaly Detection in Images Using Adversarial Autoencoders. ECML/PKDD (1) Laura Beggel, Michael Pfeiffer, Bernd Bischl
2019	J	`jnl`	Robust Anomaly Detection in Images using Adversarial Autoencoders. CoRR Laura Beggel, Michael Pfeiffer, Bernd Bischl
2019	J	`jnl`	Sampling, Intervention, Prediction, Aggregation: A Generalized Framework for Model Agnostic Interpretations. CoRR Christian A. Scholbeck, Christoph Molnar, Christian Heumann, Bernd Bischl, Giuseppe Casalicchio
2019	—	`conf`	Sampling, Intervention, Prediction, Aggregation: A Generalized Framework for Model-Agnostic Interpretations. PKDD/ECML Workshops (1) Christian A. Scholbeck, Christoph Molnar, Christian Heumann, Bernd Bischl, Giuseppe Casalicchio
2019	J	`jnl`	Time series anomaly detection based on shapelet learning. Comput. Stat. Laura Beggel, Bernhard X. Kausler, Martin Schiegg, Michael Pfeiffer, Bernd Bischl
2019	J	`jnl`	Towards Human Centered AutoML. CoRR Florian Pfisterer, Janek Thomas, Bernd Bischl
2019	J	`jnl`	Tunability: Importance of Hyperparameters of Machine Learning Algorithms. J. Mach. Learn. Res. Philipp Probst, Anne-Laure Boulesteix, Bernd Bischl
2019	—	`conf`	Tutorial and Survey on Probabilistic Graphical Model and Variational Inference in Deep Reinforcement Learning. SSCI Xudong Sun, Bernd Bischl
2019	J	`jnl`	Tutorial and Survey on Probabilistic Graphical Model and Variational Inference in Deep Reinforcement Learning. CoRR Xudong Sun, Bernd Bischl
2019	—	`conf`	Variational Resampling Based Assessment of Deep Neural Networks under Distribution Shift. SSCI Xudong Sun, Alexej Gossmann, Yu Wang, Bernd Bischl
2019	—	`conf`	Wearable-Based Parkinson's Disease Severity Monitoring Using Deep Learning. ECML/PKDD (3) Jann Goschenhofer, Franz Michael Josef Pfister, Kamer Ali Yuksel, Bernd Bischl, Urban Fietzek, Janek Thomas
2019	J	`jnl`	Wearable-based Parkinson's Disease Severity Monitoring using Deep Learning. CoRR Jann Goschenhofer, Franz Michael Josef Pfister, Kamer Ali Yuksel, Bernd Bischl, Urban Fietzek, Janek Thomas
2019	J	`jnl`	mlr3: A modern object-oriented machine learning framework in R. J. Open Source Softw. Michel Lang, Martin Binder, Jakob Richter, Patrick Schratz, Florian Pfisterer, Stefan Coors, Quay Au, Giuseppe Casalicchio, Lars Kotthoff, Bernd Bischl
2018	B	`conf`	A Regression-Based Methodology for Online Algorithm Selection. SOCS Hans Degroote, Patrick De Causmaecker, Bernd Bischl, Lars Kotthoff
2018	J	`jnl`	A comparative study on large scale kernelized support vector machines. Adv. Data Anal. Classif. Daniel Horn, Aydin Demircioglu, Bernd Bischl, Tobias Glasmachers, Claus Weihs
2018	J	`jnl`	Automatic Exploration of Machine Learning Experiments on OpenML. CoRR Daniel Kühn, Philipp Probst, Janek Thomas, Bernd Bischl
2018	J	`jnl`	Automatic Gradient Boosting. CoRR Janek Thomas, Stefan Coors, Bernd Bischl
2018	J	`jnl`	Corrigendum to "Probing for Sparse and Fast Variable Selection with Model-Based Boosting". Comput. Math. Methods Medicine Janek Thomas, Tobias Hepp, Andreas Mayr, Bernd Bischl
2018	J	`jnl`	Gradient boosting for distributional regression: faster tuning and improved variable selection via noncyclical updates. Stat. Comput. Janek Thomas, Andreas Mayr, Bernd Bischl, Matthias Schmid, Adam Smith, Benjamin Hofner
2018	J	`jnl`	Learning Multiple Defaults for Machine Learning Algorithms. CoRR Florian Pfisterer, Jan N. van Rijn, Philipp Probst, Andreas C. Müller, Bernd Bischl
2018	J	`jnl`	Proceedings of Reisensburg 2014-2015. Comput. Stat. Hans A. Kestler, Bernd Bischl, Matthias Schmid
2018	—	`conf`	Visualizing the Feature Importance for Black Box Models. ECML/PKDD (1) Giuseppe Casalicchio, Christoph Molnar, Bernd Bischl
2018	J	`jnl`	Visualizing the Feature Importance for Black Box Models. CoRR Giuseppe Casalicchio, Christoph Molnar, Bernd Bischl
2018	J	`jnl`	compboost: Modular Framework for Component-Wise Boosting. J. Open Source Softw. Daniel Schalk, Janek Thomas, Bernd Bischl
2018	J	`jnl`	iml: An R package for Interpretable Machine Learning. J. Open Source Softw. Christoph Molnar, Giuseppe Casalicchio, Bernd Bischl
2017	—	`conf`	Evaluating random forest models for irace. GECCO (Companion) Leslie Pérez Cáceres, Bernd Bischl, Thomas Stützle
2017	C	`conf`	First Investigations on Noisy Model-Based Multi-objective Optimization. EMO Daniel Horn, Melanie Dagge, Xudong Sun, Bernd Bischl
2017	J	`jnl`	Multilabel Classification with R Package mlr. R J. Philipp Probst, Quay Au, Giuseppe Casalicchio, Clemens Stachl, Bernd Bischl
2017	J	`jnl`	OpenML Benchmarking Suites and the OpenML100. CoRR Bernd Bischl, Giuseppe Casalicchio, Matthias Feurer, Frank Hutter, Michel Lang, Rafael Gomes Mantovani, Jan N. van Rijn, Joaquin Vanschoren
2017	J	`jnl`	OpenML: An R Package to Connect to the Networked Machine Learning Platform OpenML. CoRR Giuseppe Casalicchio, Jakob Bossek, Michel Lang, Dominik Kirchhoff, Pascal Kerschke, Benjamin Hofner, Heidi Seibold, Joaquin Vanschoren, Bernd Bischl
2017	J	`jnl`	Probing for Sparse and Fast Variable Selection with Model-Based Boosting. Comput. Math. Methods Medicine Janek Thomas, Tobias Hepp, Andreas Mayr, Bernd Bischl
2017	—	`conf`	RAMBO: Resource-Aware Model-Based Optimization with Scheduling for Heterogeneous Runtimes and a Comparison with Asynchronous Model-Based Optimization. LION Helena Kotthaus, Jakob Richter, Andreas Lang, Janek Thomas, Bernd Bischl, Peter Marwedel, Jörg Rahnenführer, Michel Lang
2017	J	`jnl`	batchtools: Tools for R to work on batch systems. J. Open Source Softw. Michel Lang, Bernd Bischl, Dirk Surmann
2016	J	`jnl`	ASlib: A benchmark library for algorithm selection. Artif. Intell. Bernd Bischl, Pascal Kerschke, Lars Kotthoff, Marius Lindauer, Yuri Malitsky, Alexandre Fréchette, Holger H. Hoos, Frank Hutter, Kevin Leyton-Brown, Kevin Tierney, Joaquin Vanschoren
2016	J	`jnl`	Fast model selection by limiting SVM training times. CoRR Aydin Demircioglu, Daniel Horn, Tobias Glasmachers, Bernd Bischl, Claus Weihs
2016	—	`conf`	Faster Model-Based Optimization Through Resource-Aware Scheduling Strategies. LION Jakob Richter, Helena Kotthaus, Bernd Bischl, Peter Marwedel, Jörg Rahnenführer, Michel Lang
2016	—	`conf`	Multi-objective parameter configuration of machine learning algorithms using model-based optimization. SSCI Daniel Horn, Bernd Bischl
2016	—	`conf`	Reinforcement Learning for Automatic Online Algorithm Selection - an Empirical Study. ITAT Hans Degroote, Bernd Bischl, Lars Kotthoff, Patrick De Causmaecker
2016	J	`jnl`	mlr Tutorial. CoRR 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
2016	J	`jnl`	mlr: Machine Learning in R. J. Mach. Learn. Res. Bernd Bischl, Michel Lang, Lars Kotthoff, Julia Schiffner, Jakob Richter, Erich Studerus, Giuseppe Casalicchio, Zachary M. Jones
2015	J	`jnl`	ASlib: A Benchmark Library for Algorithm Selection. CoRR Bernd Bischl, Pascal Kerschke, Lars Kotthoff, Marius Lindauer, Yuri Malitsky, Alexandre Fréchette, Holger H. Hoos, Frank Hutter, Kevin Leyton-Brown, Kevin Tierney, Joaquin Vanschoren
2015	J	`jnl`	Analyzing the BBOB Results by Means of Benchmarking Concepts. Evol. Comput. Olaf Mersmann, Mike Preuss, Heike Trautmann, Bernd Bischl, Claus Weihs
2015	—	`conf`	Applying Model-Based Optimization to Hyperparameter Optimization in Machine Learning. MetaSel@PKDD/ECML Bernd Bischl
2015	B	`conf`	Effectiveness of Random Search in SVM hyper-parameter tuning. IJCNN Rafael Gomes Mantovani, André Luis Debiaso Rossi, Joaquin Vanschoren, Bernd Bischl, André C. P. L. F. de Carvalho
2015	A	`conf`	Learning Feature-Parameter Mappings for Parameter Tuning via the Profile Expected Improvement. GECCO Jakob Bossek, Bernd Bischl, Tobias Wagner, Günter Rudolph
2015	—	`conf`	Model-Based Multi-objective Optimization: Taxonomy, Multi-Point Proposal, Toolbox and Benchmark. EMO (1) Daniel Horn, Tobias Wagner, Dirk Biermann, Claus Weihs, Bernd Bischl
2015	—	`conf`	Taking machine learning research online with OpenML. BigMine Joaquin Vanschoren, Jan N. van Rijn, Bernd Bischl
2015	—	`conf`	The Impact of Initial Designs on the Performance of MATSuMoTo on the Noiseless BBOB-2015 Testbed: A Preliminary Study. GECCO (Companion) Dimo Brockhoff, Bernd Bischl, Tobias Wagner
2015	B	`conf`	To tune or not to tune: Recommending when to adjust SVM hyper-parameters via meta-learning. IJCNN Rafael Gomes Mantovani, André Luis Debiaso Rossi, Joaquin Vanschoren, Bernd Bischl, André C. P. L. F. de Carvalho
2014	—	`conf`	Big Data Classification : Aspects on Many Features and Many Observations. ECDA Claus Weihs, Daniel Horn, Bernd Bischl
2014	—	`conf`	Fast Model Based Optimization of Tone Onset Detection by Instance Sampling. ECDA Nadja Bauer, Klaus Friedrichs, Bernd Bischl, Claus Weihs
2014	—	`conf`	MOI-MBO: Multiobjective Infill for Parallel Model-Based Optimization. LION Bernd Bischl, Simon Wessing, Nadja Bauer, Klaus Friedrichs, Claus Weihs
2014	—	`—`	Model and Algorithm Selection in Statistical Learning and Optimization. Bernd Bischl
2014	—	`conf`	On Class Imbalance Correction for Classification Algorithms in Credit Scoring. OR Bernd Bischl, Tobias Kühn, Gero Szepannek
2014	J	`jnl`	OpenML: networked science in machine learning. CoRR Joaquin Vanschoren, Jan N. van Rijn, Bernd Bischl, Luís Torgo
2013	A	`conf`	A feature-based comparison of local search and the christofides algorithm for the travelling salesperson problem. FOGA Samadhi Nallaperuma, Markus Wagner, Frank Neumann, Bernd Bischl, Olaf Mersmann, Heike Trautmann
2013	J	`jnl`	A novel feature-based approach to characterize algorithm performance for the traveling salesperson problem. Ann. Math. Artif. Intell. Olaf Mersmann, Bernd Bischl, Heike Trautmann, Markus Wagner, Jakob Bossek, Frank Neumann
2013	J	`jnl`	Benchmarking local classification methods. Comput. Stat. Bernd Bischl, Julia Schiffner, Claus Weihs
2013	—	`conf`	OpenML: A Collaborative Science Platform. ECML/PKDD (3) Jan N. van Rijn, Bernd Bischl, Luís Torgo, Bo Gao, Venkatesh Umaashankar, Simon Fischer, Patrick Winter, Bernd Wiswedel, Michael R. Berthold, Joaquin Vanschoren
2013	J	`jnl`	OpenML: networked science in machine learning. SIGKDD Explor. Joaquin Vanschoren, Jan N. van Rijn, Bernd Bischl, Luís Torgo
2013	—	`conf`	PROGRESS: Progressive Reinforcement-Learning-Based Surrogate Selection. LION Stefan Hess, Tobias Wagner, Bernd Bischl
2012	J	`jnl`	A Novel Feature-Based Approach to Characterize Algorithm Performance for the Traveling Salesman Problem CoRR Olaf Mersmann, Bernd Bischl, Heike Trautmann, Markus Wagner, Frank Neumann
2012	A	`conf`	Algorithm selection based on exploratory landscape analysis and cost-sensitive learning. GECCO Bernd Bischl, Olaf Mersmann, Heike Trautmann, Mike Preuß
2012	—	`conf`	Benchmarking Classification Algorithms on High-Performance Computing Clusters. GfKl Bernd Bischl, Julia Schiffner, Claus Weihs
2012	—	`conf`	Local Search and the Traveling Salesman Problem: A Feature-Based Characterization of Problem Hardness. LION Olaf Mersmann, Bernd Bischl, Jakob Bossek, Heike Trautmann, Markus Wagner, Frank Neumann
2012	J	`jnl`	Resampling Methods for Meta-Model Validation with Recommendations for Evolutionary Computation. Evol. Comput. Bernd Bischl, Olaf Mersmann, Heike Trautmann, Claus Weihs
2012	—	`conf`	Statistical Comparison of Classifiers for Multi-objective Feature Selection in Instrument Recognition. GfKl Igor Vatolkin, Bernd Bischl, Günter Rudolph, Claus Weihs
2012	—	`conf`	Support Vector Machines on Large Data Sets: Simple Parallel Approaches. GfKl Oliver Meyer, Bernd Bischl, Claus Weihs
2012	J	`jnl`	Tuning and evolution of support vector kernels. Evol. Intell. Patrick Koch, Bernd Bischl, Oliver Flasch, Thomas Bartz-Beielstein, Claus Weihs, Wolfgang Konen
2011	A	`conf`	Exploratory landscape analysis. GECCO Olaf Mersmann, Bernd Bischl, Heike Trautmann, Mike Preuss, Claus Weihs, Günter Rudolph
2011	J	`jnl`	Huge Music Archives on Mobile Devices. IEEE Signal Process. Mag. Holger Blume, Bernd Bischl, Martin Botteck, Christian Igel, Rainer Martin, Günther Rötter, Günter Rudolph, Wolfgang M. Theimer, Igor Vatolkin, Claus Weihs
2010	—	`conf`	A Case Study on the Use of Statistical Classification Methods in Particle Physics. GfKl Claus Weihs, Olaf Mersmann, Bernd Bischl, Arno Fritsch, Heike Trautmann, Till Moritz Karbach, Bernhard Spaan
2010	—	`conf`	Bias-Variance Analysis of Local Classification Methods. GfKl Julia Schiffner, Bernd Bischl, Claus Weihs
2010	—	`conf`	Selecting Groups of Audio Features by Statistical Tests and the Group Lasso. Sprachkommunikation Bernd Bischl, Markus Eichhoff, Claus Weihs
2010	—	`conf`	Selecting Small Audio Feature Sets in Music Classification by Means of Asymmetric Mutation. PPSN (1) Bernd Bischl, Igor Vatolkin, Mike Preuss
2009	J	`jnl`	On the combination of locally optimal pairwise classifiers. Eng. Appl. Artif. Intell. Gero Szepannek, Bernd Bischl, Claus Weihs
2007	—	`conf`	On the Combination of Locally Optimal Pairwise Classifiers. MLDM Gero Szepannek, Bernd Bischl, Claus Weihs