Bischl, Bernd

57 publications

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

DMLR 2025 Constructing Confidence Intervals for “the” Generalization Error – A Comprehensive Benchmark Study Hannah Schulz-Kümpel, Sebastian Felix Fischer, Roman Hornung, Anne-Laure Boulesteix, Thomas Nagler, Bernd Bischl

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

ICLRW 2025 Differentiable Attention Sparsity via Structured $d$-Gating Chris Kolb, Bernd Bischl, David Rügamer

NeurIPS 2025 Differentiable Sparsity via $d$-Gating: Simple and Versatile Structured Penalization Chris Kolb, Laetitia Frost, Bernd Bischl, David Rügamer

ICLR 2025 Efficient and Accurate Explanation Estimation with Distribution Compression Hubert Baniecki, Giuseppe Casalicchio, Bernd Bischl, Przemyslaw Biecek

ICLRW 2025 Efficiently Warmstarting MCMC for BNNs David Rundel, Emanuel Sommer, Bernd Bischl, David Rügamer, Matthias Feurer

ECML-PKDD 2025 Explaining Bayesian Optimization by Shapley Values Facilitates Human-AI Collaboration for Exosuit Personalization Julian Rodemann, Federico Croppi, Philipp Arens, Yusuf Sale, Julia Herbinger, Bernd Bischl, Eyke Hüllermeier, Thomas Augustin, Conor J. Walsh, Giuseppe Casalicchio

ECML-PKDD 2025 On Training Survival Models with Scoring Rules Philipp Kopper, David Rügamer, Raphael Sonabend, Bernd Bischl, Andreas Bender

AutoML 2025 Overtuning in Hyperparameter Optimization Lennart Schneider, Bernd Bischl, Matthias Feurer

ICML 2025 Revisiting Unbiased Implicit Variational Inference Tobias Pielok, Bernd Bischl, David Rügamer

ICLRW 2025 Trust Me, I Know the Way: Predictive Uncertainty in the Presence of Shortcut Learning Lisa Wimmer, Bernd Bischl, Ludwig Bothmann

TMLR 2024 A Dual-Perspective Approach to Evaluating Feature Attribution Methods Yawei Li, Yang Zhang, Kenji Kawaguchi, Ashkan Khakzar, Bernd Bischl, Mina Rezaei

JMLR 2024 AMLB: An AutoML Benchmark Pieter Gijsbers, Marcos L. P. Bueno, Stefan Coors, Erin LeDell, Sébastien Poirier, Janek Thomas, Bernd Bischl, Joaquin Vanschoren

ECML-PKDD 2024 Attention-Driven Dropout: A Simple Method to Improve Self-Supervised Contrastive Sentence Embeddings Fabian Stermann, Ilias Chalkidis, Amihossein Vahidi, Bernd Bischl, Mina Rezaei

JAIR 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

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

WACV 2024 Constrained Probabilistic Mask Learning for Task-Specific Undersampled MRI Reconstruction Tobias Weber, Michael Ingrisch, Bernd Bischl, David Rügamer

JMLR 2024 Decomposing Global Feature Effects Based on Feature Interactions Julia Herbinger, Marvin N. Wright, Thomas Nagler, Bernd Bischl, Giuseppe Casalicchio

ECML-PKDD 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

NeurIPSW 2024 FinerCut: Finer-Grained Interpretable Layer Pruning for Large Language Models Yang Zhang, Yawei Li, Xinpeng Wang, Qianli Shen, Barbara Plank, Bernd Bischl, Mina Rezaei, Kenji Kawaguchi

ECML-PKDD 2024 On the Robustness of Global Feature Effect Explanations Hubert Baniecki, Giuseppe Casalicchio, Bernd Bischl, Przemyslaw Biecek

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

ICML 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

ICLR 2024 Probabilistic Self-Supervised Representation Learning via Scoring Rules Minimization Amirhossein Vahidi, Simon Schosser, Lisa Wimmer, Yawei Li, Bernd Bischl, Eyke Hüllermeier, Mina Rezaei

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

IJCAI 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

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

ICLR 2023 Approximate Bayesian Inference with Stein Functional Variational Gradient Descent Tobias Pielok, Bernd Bischl, David Rügamer

NeurIPSW 2023 AttributionLab: Faithfulness of Feature Attribution Under Controllable Environments Yang Zhang, Yawei Li, Hannah Brown, Mina Rezaei, Bernd Bischl, Philip Torr, Ashkan Khakzar, Kenji Kawaguchi

AISTATS 2023 Frequentist Uncertainty Quantification in Semi-Structured Neural Networks Emilio Dorigatti, Benjamin Schubert, Bernd Bischl, David Ruegamer

ECML-PKDD 2023 Interpretable Regional Descriptors: Hyperbox-Based Local Explanations Susanne Dandl, Giuseppe Casalicchio, Bernd Bischl, Ludwig Bothmann

AutoML 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 Hoos

UAI 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

AutoML 2023 Symbolic Explanations for Hyperparameter Optimization Sarah Segel, Helena Graf, Alexander Tornede, Bernd Bischl, Marius Lindauer

ECML-PKDD 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

AISTATS 2022 REPID: Regional Effect Plots with Implicit Interaction Detection Julia Herbinger, Bernd Bischl, Giuseppe Casalicchio

ECML-PKDD 2022 Efficient Automated Deep Learning for Time Series Forecasting Difan Deng, Florian Karl, Frank Hutter, Bernd Bischl, Marius Lindauer

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

NeurIPS 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 Wegner, Konrad Schindler

WACV 2022 Joint Classification and Trajectory Regression of Online Handwriting Using a Multi-Task Learning Approach Felix Ott, David Rügamer, Lucas Heublein, Bernd Bischl, Christopher Mutschler

AutoML 2022 Tackling Neural Architecture Search with Quality Diversity Optimization Lennart Schneider, Florian Pfisterer, Paul Kent, Juergen Branke, Bernd Bischl, Janek Thomas

NeurIPSW 2022 Transformer Model for Genome Sequence Analysis Noah Hurmer, Xiao-Yin To, Martin Binder, Hüseyin Anil Gündüz, Philipp C. Münch, René Mreches, Alice C McHardy, Bernd Bischl, Mina Rezaei

NeurIPSW 2022 What Cleaves? Is Proteasomal Cleavage Prediction Reaching a Ceiling? Ingo Ziegler, Bolei Ma, Ercong Nie, Bernd Bischl, David Rügamer, Benjamin Schubert, Emilio Dorigatti

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

NeurIPS 2021 Explaining Hyperparameter Optimization via Partial Dependence Plots Julia Moosbauer, Julia Herbinger, Giuseppe Casalicchio, Marius Lindauer, Bernd Bischl

MLOSS 2021 Mlr3pipelines - Flexible Machine Learning Pipelines in R Martin Binder, Florian Pfisterer, Michel Lang, Lennart Schneider, Lars Kotthoff, Bernd Bischl

ICMLW 2021 Mutation Is All You Need Lennart Schneider, Florian Pfisterer, Martin Binder, Bernd Bischl

ICMLW 2021 Towards Explaining Hyperparameter Optimization via Partial Dependence Plots Julia Moosbauer, Julia Herbinger, Giuseppe Casalicchio, Marius Lindauer, Bernd Bischl

NeurIPSW 2021 Towards Modelling Hazard Factors in Unstructured Data Spaces Using Gradient-Based Latent Interpolation Tobias Weber, Michael Ingrisch, Bernd Bischl, David Rügamer

ECML-PKDD 2020 A General Machine Learning Framework for Survival Analysis Andreas Bender, David Rügamer, Fabian Scheipl, Bernd Bischl

ECML-PKDD 2019 Robust Anomaly Detection in Images Using Adversarial Autoencoders Laura Beggel, Michael Pfeiffer, Bernd Bischl

JMLR 2019 Tunability: Importance of Hyperparameters of Machine Learning Algorithms Philipp Probst, Anne-Laure Boulesteix, Bernd Bischl

ECML-PKDD 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 2018 Visualizing the Feature Importance for Black Box Models Giuseppe Casalicchio, Christoph Molnar, Bernd Bischl

JMLR 2016 Mlr: Machine Learning in R Bernd Bischl, Michel Lang, Lars Kotthoff, Julia Schiffner, Jakob Richter, Erich Studerus, Giuseppe Casalicchio, Zachary M. Jones

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