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ORCIDBernd Bischl
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- affiliation: LMU Munich, Department of Statistics, Germany
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2020 – today
- 2024
[j47]Simon Wiegrebe, Philipp Kopper, Raphael Sonabend, Bernd Bischl, Andreas Bender:
Deep learning for survival analysis: a review. Artif. Intell. Rev. 57(3): 65 (2024)- [j46]Hilde J. P. Weerts
, Florian Pfisterer, Matthias Feurer, Katharina Eggensperger, Edward Bergman, Noor H. Awad, Joaquin Vanschoren, Mykola Pechenizkiy, Bernd Bischl, Frank Hutter:
Can Fairness be Automated? Guidelines and Opportunities for Fairness-aware AutoML. J. Artif. Intell. Res. 79: 639-677 (2024) - [j45]Daniel Schalk, Bernd Bischl, David Rügamer:
Privacy-preserving and lossless distributed estimation of high-dimensional generalized additive mixed models. Stat. Comput. 34(1): 31 (2024) - [c92]Tobias Weber, Michael Ingrisch, Bernd Bischl, David Rügamer:
Constrained Probabilistic Mask Learning for Task-specific Undersampled MRI Reconstruction. WACV 2024: 7650-7659 - [i108]Emanuel Sommer, Lisa Wimmer, Theodore Papamarkou, Ludwig Bothmann, Bernd Bischl, David Rügamer:
Connecting the Dots: Is Mode-Connectedness the Key to Feasible Sample-Based Inference in Bayesian Neural Networks? CoRR abs/2402.01484 (2024) - [i107]Julian Rodemann, Federico Croppi, Philipp Arens, Yusuf Sale, Julia Herbinger, Bernd Bischl, Eyke Hüllermeier, Thomas Augustin, Conor J. Walsh, Giuseppe Casalicchio:
Explaining Bayesian Optimization by Shapley Values Facilitates Human-AI Collaboration. CoRR abs/2403.04629 (2024) - [i106]Philipp Kopper, David Rügamer, Raphael Sonabend, Bernd Bischl, Andreas Bender:
Training Survival Models using Scoring Rules. CoRR abs/2403.13150 (2024) - 2023
- [j44]Felix Ott, David Rügamer, Lucas Heublein, Bernd Bischl, Christopher Mutschler:
Auxiliary Cross-Modal Representation Learning With Triplet Loss Functions for Online Handwriting Recognition. IEEE Access 11: 94148-94172 (2023) - [j43]Sai Rahul Kaminwar, Jann Goschenhofer, Janek Thomas, Ingo Thon, Bernd Bischl:
Structured Verification of Machine Learning Models in Industrial Settings. Big Data 11(3): 181-198 (2023) - [j42]Mina Rezaei, Farzin Soleymani, Bernd Bischl, Shekoofeh Azizi:
Deep Bregman divergence for self-supervised representations learning. Comput. Vis. Image Underst. 235: 103801 (2023) - [j41]Daniel Schalk, Bernd Bischl, David Rügamer:
Accelerated Componentwise Gradient Boosting Using Efficient Data Representation and Momentum-Based Optimization. J. Comput. Graph. Stat. 32(2): 631-641 (2023) - [j40]Daniel Schalk, Verena S. Hoffmann, Bernd Bischl, Ulrich Mansmann:
dsBinVal: Conducting distributed ROC analysis using DataSHIELD. J. Open Source Softw. 8(83): 4545 (2023) - [j39]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:
deepregression: A Flexible Neural Network Framework for Semi-Structured Deep Distributional Regression. J. Stat. Softw. 105(2) (2023) - [j38]Florian Pfisterer, Siyi Wei, Sebastian J. Vollmer, Michel Lang, Bernd Bischl:
Fairness Audits and Debiasing Using \pkg{mlr3fairness}. R J. 15(1): 234-253 (2023) - [j37]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:
Multi-Objective Hyperparameter Optimization in Machine Learning - An Overview. ACM Trans. Evol. Learn. Optim. 3(4): 16:1-16:50 (2023) - [j36]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:
Hyperparameter optimization: Foundations, algorithms, best practices, and open challenges. WIREs Data. Mining. Knowl. Discov. 13(2) (2023) - [c91]Daniel Saggau, Mina Rezaei, Bernd Bischl, Ilias Chalkidis:
Efficient Document Embeddings via Self-Contrastive Bregman Divergence Learning. ACL (Findings) 2023: 12181-12190 - [c90]Emilio Dorigatti, Benjamin Schubert, Bernd Bischl, David Rügamer:
Frequentist Uncertainty Quantification in Semi-Structured Neural Networks. AISTATS 2023: 1924-1941 - [c89]Sarah Segel, Helena Graf, Alexander Tornede, Bernd Bischl, Marius Lindauer:
Symbolic Explanations for Hyperparameter Optimization. AutoML 2023: 2/1-22 - [c88]Lennart Oswald Purucker, Lennart Schneider, Marie Anastacio, Joeran Beel, Bernd Bischl, Holger H. Hoos:
Q(D)O-ES: Population-based Quality (Diversity) Optimisation for Post Hoc Ensemble Selection in AutoML. AutoML 2023: 10/1-34 - [c87]Amadeu Scheppach, Hüseyin Anil Gündüz, Emilio Dorigatti, Philipp C. Münch, Alice C. McHardy, Bernd Bischl, Mina Rezaei, Martin Binder:
Neural Architecture Search for Genomic Sequence Data. CIBCB 2023: 1-10 - [c86]Raphael Patrick Prager, Konstantin Dietrich, Lennart Schneider, Lennart Schäpermeier, Bernd Bischl, Pascal Kerschke, Heike Trautmann, Olaf Mersmann:
Neural Networks as Black-Box Benchmark Functions Optimized for Exploratory Landscape Features. FOGA 2023: 129-139 - [c85]Lennart Schneider, Bernd Bischl, Janek Thomas:
Multi-Objective Optimization of Performance and Interpretability of Tabular Supervised Machine Learning Models. GECCO 2023: 538-547 - [c84]Ivo Couckuyt, Sebastian Rojas-Gonzalez, Jürgen Branke, Bernd Bischl:
Bayesian Optimization. GECCO Companion 2023: 895-912 - [c83]Matthias Aßenmacher, Lukas Rauch, Jann Goschenhofer, Andreas Stephan, Bernd Bischl, Benjamin Roth, Bernhard Sick:
Towards Enhancing Deep Active Learning with Weak Supervision and Constrained Clustering. IAL@PKDD/ECML 2023: 65-73 - [c82]Tobias Pielok, Bernd Bischl, David Rügamer:
Approximate Bayesian Inference with Stein Functional Variational Gradient Descent. ICLR 2023 - [c81]Hüseyin Anil Gündüz, Sheetal Giri, Martin Binder, Bernd Bischl, Mina Rezaei:
Uncertainty Quantification for Deep Learning Models Predicting the Regulatory Activity of DNA Sequences. ICMLA 2023: 566-573 - [c80]Matthias Feurer, Katharina Eggensperger, Edward Bergman, Florian Pfisterer, Bernd Bischl, Frank Hutter:
Mind the Gap: Measuring Generalization Performance Across Multiple Objectives. IDA 2023: 130-142 - [c79]Jann Goschenhofer, Bernd Bischl, Zsolt Kira:
ConstraintMatch for Semi-constrained Clustering. IJCNN 2023: 1-10 - [c78]Tobias Weber, Michael Ingrisch, Bernd Bischl, David Rügamer:
Cascaded Latent Diffusion Models for High-Resolution Chest X-ray Synthesis. PAKDD (3) 2023: 180-191 - [c77]Lukas Rauch, Matthias Aßenmacher, Denis Huseljic, Moritz Wirth, Bernd Bischl, Bernhard Sick:
ActiveGLAE: A Benchmark for Deep Active Learning with Transformers. ECML/PKDD (1) 2023: 55-74 - [c76]Jonas Gregor Wiese, Lisa Wimmer, Theodore Papamarkou, Bernd Bischl, Stephan Günnemann, David Rügamer:
Towards Efficient MCMC Sampling in Bayesian Neural Networks by Exploiting Symmetry. ECML/PKDD (1) 2023: 459-474 - [c75]Susanne Dandl, Giuseppe Casalicchio, Bernd Bischl, Ludwig Bothmann:
Interpretable Regional Descriptors: Hyperbox-Based Local Explanations. ECML/PKDD (3) 2023: 479-495 - [c74]Lisa Wimmer, Yusuf Sale, Paul Hofman, Bernd Bischl, Eyke Hüllermeier:
Quantifying aleatoric and epistemic uncertainty in machine learning: Are conditional entropy and mutual information appropriate measures? UAI 2023: 2282-2292 - [c73]Christoph Molnar, Timo Freiesleben, Gunnar König, Julia Herbinger, Tim Reisinger, Giuseppe Casalicchio, Marvin N. Wright, Bernd Bischl:
Relating the Partial Dependence Plot and Permutation Feature Importance to the Data Generating Process. xAI (1) 2023: 456-479 - [i105]Felix Ott, David Rügamer, Lucas Heublein, Bernd Bischl, Christopher Mutschler:
Representation Learning for Tablet and Paper Domain Adaptation in Favor of Online Handwriting Recognition. CoRR abs/2301.06293 (2023) - [i104]Hilde J. P. Weerts, Florian Pfisterer, Matthias Feurer, Katharina Eggensperger, Edward Bergman, Noor H. Awad, Joaquin Vanschoren, Mykola Pechenizkiy, Bernd Bischl, Frank Hutter:
Can Fairness be Automated? Guidelines and Opportunities for Fairness-aware AutoML. CoRR abs/2303.08485 (2023) - [i103]Tobias Weber, Michael Ingrisch, Bernd Bischl, David Rügamer:
Cascaded Latent Diffusion Models for High-Resolution Chest X-ray Synthesis. CoRR abs/2303.11224 (2023) - [i102]Jonas Gregor Wiese, Lisa Wimmer, Theodore Papamarkou, Bernd Bischl, Stephan Günnemann, David Rügamer:
Towards Efficient MCMC Sampling in Bayesian Neural Networks by Exploiting Symmetry. CoRR abs/2304.02902 (2023) - [i101]Susanne Dandl, Andreas Hofheinz, Martin Binder, Bernd Bischl, Giuseppe Casalicchio:
counterfactuals: An R Package for Counterfactual Explanation Methods. CoRR abs/2304.06569 (2023) - [i100]Felix Ott, Lucas Heublein, David Rügamer, Bernd Bischl, Christopher Mutschler:
Fusing Structure from Motion and Simulation-Augmented Pose Regression from Optical Flow for Challenging Indoor Environments. CoRR abs/2304.07250 (2023) - [i99]Susanne Dandl, Giuseppe Casalicchio, Bernd Bischl, Ludwig Bothmann:
Interpretable Regional Descriptors: Hyperbox-Based Local Explanations. CoRR abs/2305.02780 (2023) - [i98]Daniel Saggau, Mina Rezaei, Bernd Bischl, Ilias Chalkidis:
Efficient Document Embeddings via Self-Contrastive Bregman Divergence Learning. CoRR abs/2305.16031 (2023) - [i97]Tobias Weber, Michael Ingrisch, Bernd Bischl, David Rügamer:
Constrained Probabilistic Mask Learning for Task-specific Undersampled MRI Reconstruction. CoRR abs/2305.16376 (2023) - [i96]Julia Herbinger, Bernd Bischl, Giuseppe Casalicchio:
Decomposing Global Feature Effects Based on Feature Interactions. CoRR abs/2306.00541 (2023) - [i95]Lukas Rauch, Matthias Aßenmacher, Denis Huseljic, Moritz Wirth, Bernd Bischl, Bernhard Sick:
ActiveGLAE: A Benchmark for Deep Active Learning with Transformers. CoRR abs/2306.10087 (2023) - [i94]Chris Kolb, Christian L. Müller, Bernd Bischl, David Rügamer:
Smoothing the Edges: A General Framework for Smooth Optimization in Sparse Regularization using Hadamard Overparametrization. CoRR abs/2307.03571 (2023) - [i93]Ibrahim Tolga Öztürk, Rostislav Nedelchev, Christian Heumann, Esteban Garces Arias, Marius Roger, Bernd Bischl, Matthias Aßenmacher:
How Different Is Stereotypical Bias Across Languages? CoRR abs/2307.07331 (2023) - [i92]Lennart Schneider, Bernd Bischl, Janek Thomas:
Multi-Objective Optimization of Performance and Interpretability of Tabular Supervised Machine Learning Models. CoRR abs/2307.08175 (2023) - [i91]Lennart Purucker, Lennart Schneider, Marie Anastacio, Joeran Beel, Bernd Bischl, Holger H. Hoos:
Q(D)O-ES: Population-based Quality (Diversity) Optimisation for Post Hoc Ensemble Selection in AutoML. CoRR abs/2307.08364 (2023) - [i90]Yawei Li, Yang Zhang, Kenji Kawaguchi, Ashkan Khakzar, Bernd Bischl, Mina Rezaei:
A Dual-Perspective Approach to Evaluating Feature Attribution Methods. CoRR abs/2308.08949 (2023) - [i89]Amirhossein Vahidi, Lisa Wimmer, Hüseyin Anil Gündüz, Bernd Bischl, Eyke Hüllermeier, Mina Rezaei:
Diversified Ensemble of Independent Sub-Networks for Robust Self-Supervised Representation Learning. CoRR abs/2308.14705 (2023) - [i88]Amirhossein Vahidi, Simon Schoßer, Lisa Wimmer, Yawei Li, Bernd Bischl, Eyke Hüllermeier, Mina Rezaei:
Probabilistic Self-supervised Learning via Scoring Rules Minimization. CoRR abs/2309.02048 (2023) - [i87]Holger Löwe, Christian A. Scholbeck, Christian Heumann, Bernd Bischl, Giuseppe Casalicchio:
fmeffects: An R Package for Forward Marginal Effects. CoRR abs/2310.02008 (2023) - [i86]Yang Zhang, Yawei Li, Hannah Brown, Mina Rezaei, Bernd Bischl, Philip H. S. Torr, Ashkan Khakzar, Kenji Kawaguchi:
AttributionLab: Faithfulness of Feature Attribution Under Controllable Environments. CoRR abs/2310.06514 (2023) - [i85]Roman Hornung, Malte Nalenz, Lennart Schneider, Andreas Bender, Ludwig Bothmann, Bernd Bischl, Thomas Augustin, Anne-Laure Boulesteix:
Evaluating machine learning models in non-standard settings: An overview and new findings. CoRR abs/2310.15108 (2023) - [i84]Tobias Weber, Michael Ingrisch, Bernd Bischl, David Rügamer:
Unreading Race: Purging Protected Features from Chest X-ray Embeddings. CoRR abs/2311.01349 (2023) - [i83]Jann Goschenhofer, Bernd Bischl, Zsolt Kira:
ConstraintMatch for Semi-constrained Clustering. CoRR abs/2311.15395 (2023) - [i82]Christian A. Scholbeck, Julia Moosbauer, Giuseppe Casalicchio, Hoshin Gupta, Bernd Bischl, Christian Heumann:
Position Paper: Bridging the Gap Between Machine Learning and Sensitivity Analysis. CoRR abs/2312.13234 (2023) - 2022
- [j35]Florian Pargent, Florian Pfisterer, Janek Thomas, Bernd Bischl:
Regularized target encoding outperforms traditional methods in supervised machine learning with high cardinality features. Comput. Stat. 37(5): 2671-2692 (2022) - [j34]Quay Au, Julia Herbinger, Clemens Stachl, Bernd Bischl, Giuseppe Casalicchio:
Grouped feature importance and combined features effect plot. Data Min. Knowl. Discov. 36(4): 1401-1450 (2022) - [j33]Felix Ott, David Rügamer, Lucas Heublein, Tim Hamann, Jens Barth, Bernd Bischl, Christopher Mutschler:
Benchmarking online sequence-to-sequence and character-based handwriting recognition from IMU-enhanced pens. Int. J. Document Anal. Recognit. 25(4): 385-414 (2022) - [j32]Julia Moosbauer, Martin Binder, Lennart Schneider, Florian Pfisterer, Marc Becker, Michel Lang, Lars Kotthoff, Bernd Bischl:
Automated Benchmark-Driven Design and Explanation of Hyperparameter Optimizers. IEEE Trans. Evol. Comput. 26(6): 1336-1350 (2022) - [c72]Julia Herbinger, Bernd Bischl, Giuseppe Casalicchio:
REPID: Regional Effect Plots with implicit Interaction Detection. AISTATS 2022: 10209-10233 - [c71]Florian Pfisterer, Lennart Schneider, Julia Moosbauer, Martin Binder, Bernd Bischl:
YAHPO Gym - An Efficient Multi-Objective Multi-Fidelity Benchmark for Hyperparameter Optimization. AutoML 2022: 3/1-39 - [c70]Lennart Schneider, Florian Pfisterer, Paul Kent, Jürgen Branke, Bernd Bischl, Janek Thomas:
Tackling Neural Architecture Search With Quality Diversity Optimization. AutoML 2022: 9/1-30 - [c69]Susanne Dandl, Florian Pfisterer, Bernd Bischl:
Multi-objective counterfactual fairness. GECCO Companion 2022: 328-331 - [c68]Lennart Schneider, Florian Pfisterer, Janek Thomas, Bernd Bischl:
A collection of quality diversity optimization problems derived from hyperparameter optimization of machine learning models. GECCO Companion 2022: 2136-2142 - [c67]Mina Rezaei, Emilio Dorigatti, David Rügamer, Bernd Bischl:
Joint Debiased Representation Learning and Imbalanced Data Clustering. ICDM (Workshops) 2022: 55-62 - [c66]Felix Ott, David Rügamer, Lucas Heublein, Bernd Bischl, Christopher Mutschler:
Representation Learning for Tablet and Paper Domain Adaptation in Favor of Online Handwriting Recognition. ICPR Workshops (1) 2022: 373-383 - [c65]Andreas Klaß, Sven M. Lorenz, Martin W. Lauer-Schmaltz, David Rügamer, Bernd Bischl, Christopher Mutschler, Felix Ott:
Uncertainty-aware Evaluation of Time-series Classification for Online Handwriting Recognition with Domain Shift. STRL@IJCAI 2022 - [c64]Mina Rezaei, Janne J. Näppi, Bernd Bischl, Hiroyuki Yoshida:
Bayesian uncertainty estimation for detection of long-tail and unseen conditions in abdominal images. Medical Imaging: Computer-Aided Diagnosis 2022 - [c63]Tobias Weber, Michael Ingrisch, Bernd Bischl, David Rügamer:
Implicit Embeddings via GAN Inversion for High Resolution Chest Radiographs. MAD@MICCAI 2022: 22-32 - [c62]Farzin Soleymani, Mohammad Eslami, Tobias Elze, Bernd Bischl, Mina Rezaei:
Deep variational clustering framework for self-labeling large-scale medical images. Medical Imaging: Image Processing 2022 - [c61]Felix Ott, David Rügamer, Lucas Heublein, Bernd Bischl, Christopher Mutschler:
Domain Adaptation for Time-Series Classification to Mitigate Covariate Shift. ACM Multimedia 2022: 5934-5943 - [c60]Mehmet Ozgur Turkoglu, Alexander Becker, Hüseyin Anil Gündüz, Mina Rezaei, Bernd Bischl, Rodrigo Caye Daudt, Stefano D'Aronco, Jan D. Wegner, Konrad Schindler:
FiLM-Ensemble: Probabilistic Deep Learning via Feature-wise Linear Modulation. NeurIPS 2022 - [c59]Philipp Kopper, Simon Wiegrebe, Bernd Bischl, Andreas Bender, David Rügamer:
DeepPAMM: Deep Piecewise Exponential Additive Mixed Models for Complex Hazard Structures in Survival Analysis. PAKDD (2) 2022: 249-261 - [c58]David Rügamer, Andreas Bender, Simon Wiegrebe, Daniel Racek, Bernd Bischl, Christian L. Müller, Clemens Stachl:
Factorized Structured Regression for Large-Scale Varying Coefficient Models. ECML/PKDD (5) 2022: 20-35 - [c57]Difan Deng, Florian Karl, Frank Hutter, Bernd Bischl, Marius Lindauer:
Efficient Automated Deep Learning for Time Series Forecasting. ECML/PKDD (3) 2022: 664-680 - [c56]Lennart Schneider, Lennart Schäpermeier, Raphael Patrick Prager, Bernd Bischl, Heike Trautmann, Pascal Kerschke:
HPO ˟ ELA: Investigating Hyperparameter Optimization Landscapes by Means of Exploratory Landscape Analysis. PPSN (1) 2022: 575-589 - [c55]Ludwig Bothmann, Sven Strickroth, Giuseppe Casalicchio, David Rügamer, Marius Lindauer, Fabian Scheipl, Bernd Bischl:
Developing Open Source Educational Resources for Machine Learning and Data Science. Teaching ML 2022: 1-6 - [c54]Felix Ott, David Rügamer, Lucas Heublein, Bernd Bischl, Christopher Mutschler:
Joint Classification and Trajectory Regression of Online Handwriting using a Multi-Task Learning Approach. WACV 2022: 1244-1254 - [i81]Christian A. Scholbeck, Giuseppe Casalicchio, Christoph Molnar, Bernd Bischl, Christian Heumann:
Marginal Effects for Non-Linear Prediction Functions. CoRR abs/2201.08837 (2022) - [i80]Emilio Dorigatti, Jann Goschenhofer, Benjamin Schubert, Mina Rezaei, Bernd Bischl:
Positive-Unlabeled Learning with Uncertainty-aware Pseudo-label Selection. CoRR abs/2201.13192 (2022) - [i79]Felix Ott, David Rügamer, Lucas Heublein, Tim Hamann, Jens Barth, Bernd Bischl, Christopher Mutschler:
Benchmarking Online Sequence-to-Sequence and Character-based Handwriting Recognition from IMU-Enhanced Pens. CoRR abs/2202.07036 (2022) - [i78]Julia Herbinger, Bernd Bischl, Giuseppe Casalicchio:
REPID: Regional Effect Plots with implicit Interaction Detection. CoRR abs/2202.07254 (2022) - [i77]Philipp Kopper, Simon Wiegrebe, Bernd Bischl, Andreas Bender, David Rügamer:
DeepPAMM: Deep Piecewise Exponential Additive Mixed Models for Complex Hazard Structures in Survival Analysis. CoRR abs/2202.07423 (2022) - [i76]Felix Ott, David Rügamer, Lucas Heublein, Bernd Bischl, Christopher Mutschler:
Cross-Modal Common Representation Learning with Triplet Loss Functions. CoRR abs/2202.07901 (2022) - [i75]Daniel Schalk, Verena S. Hoffmann, Bernd Bischl, Ulrich Mansmann:
Distributed non-disclosive validation of predictive models by a modified ROC-GLM. CoRR abs/2203.10828 (2022) - [i74]Ashkan Khakzar, Yawei Li, Yang Zhang, Mirac Sanisoglu, Seong Tae Kim, Mina Rezaei, Bernd Bischl, Nassir Navab:
Analyzing the Effects of Handling Data Imbalance on Learned Features from Medical Images by Looking Into the Models. CoRR abs/2204.01729 (2022) - [i73]Felix Ott, David Rügamer, Lucas Heublein, Bernd Bischl, Christopher Mutschler:
Domain Adaptation for Time-Series Classification to Mitigate Covariate Shift. CoRR abs/2204.03342 (2022) - [i72]Lennart Schneider, Florian Pfisterer, Janek Thomas, Bernd Bischl:
A Collection of Quality Diversity Optimization Problems Derived from Hyperparameter Optimization of Machine Learning Models. CoRR abs/2204.14061 (2022) - [i71]Difan Deng, Florian Karl, Frank Hutter, Bernd Bischl, Marius Lindauer:
Efficient Automated Deep Learning for Time Series Forecasting. CoRR abs/2205.05511 (2022) - [i70]Ludwig Bothmann, Kristina Peters, Bernd Bischl:
What Is Fairness? Implications For FairML. CoRR abs/2205.09622 (2022) - [i69]David Rügamer, Andreas Bender, Simon Wiegrebe, Daniel Racek, Bernd Bischl, Christian L. Müller, Clemens Stachl:
Factorized Structured Regression for Large-Scale Varying Coefficient Models. CoRR abs/2205.13080 (2022) - [i68]Mehmet Ozgur Turkoglu, Alexander Becker, Hüseyin Anil Gündüz, Mina Rezaei, Bernd Bischl, Rodrigo Caye Daudt, Stefano D'Aronco, Jan Dirk Wegner, Konrad Schindler:
FiLM-Ensemble: Probabilistic Deep Learning via Feature-wise Linear Modulation. CoRR abs/2206.00050 (2022) - [i67]Julia Moosbauer, Giuseppe Casalicchio, Marius Lindauer, Bernd Bischl:
Enhancing Explainability of Hyperparameter Optimization via Bayesian Algorithm Execution. CoRR abs/2206.05447 (2022) - [i66]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:
Multi-Objective Hyperparameter Optimization - An Overview. CoRR abs/2206.07438 (2022) - [i65]Andreas Klaß, Sven M. Lorenz, Martin W. Lauer-Schmaltz, David Rügamer, Bernd Bischl, Christopher Mutschler, Felix Ott:
Uncertainty-aware Evaluation of Time-Series Classification for Online Handwriting Recognition with Domain Shift. CoRR abs/2206.08640 (2022) - [i64]Pieter Gijsbers, Marcos L. P. Bueno, Stefan Coors, Erin LeDell, Sébastien Poirier, Janek Thomas, Bernd Bischl, Joaquin Vanschoren:
AMLB: an AutoML Benchmark. CoRR abs/2207.12560 (2022) - [i63]Lennart Schneider, Florian Pfisterer, Paul Kent, Jürgen Branke, Bernd Bischl, Janek Thomas:
Tackling Neural Architecture Search With Quality Diversity Optimization. CoRR abs/2208.00204 (2022) - [i62]Lennart Schneider, Lennart Schäpermeier, Raphael Patrick Prager, Bernd Bischl, Heike Trautmann, Pascal Kerschke:
HPO X ELA: Investigating Hyperparameter Optimization Landscapes by Means of Exploratory Landscape Analysis. CoRR abs/2208.00220 (2022) - [i61]Felix Ott, Nisha Lakshmana Raichur, David Rügamer, Tobias Feigl, Heiko Neumann, Bernd Bischl, Christopher Mutschler:
Benchmarking Visual-Inertial Deep Multimodal Fusion for Relative Pose Regression and Odometry-aided Absolute Pose Regression. CoRR abs/2208.00919 (2022) - [i60]Emilio Dorigatti, Jonas Schweisthal, Bernd Bischl, Mina Rezaei:
Robust and Efficient Imbalanced Positive-Unlabeled Learning with Self-supervision. CoRR abs/2209.02459 (2022) - [i59]Shunjie-Fabian Zheng, JaeEun Nam, Emilio Dorigatti, Bernd Bischl, Shekoofeh Azizi, Mina Rezaei:
Joint Debiased Representation and Image Clustering Learning with Self-Supervision. CoRR abs/2209.06941 (2022) - [i58]Emilio Dorigatti, Bernd Bischl, Benjamin Schubert:
Improved proteasomal cleavage prediction with positive-unlabeled learning. CoRR abs/2209.07527 (2022) - [i57]Daniel Schalk, Bernd Bischl, David Rügamer:
Privacy-Preserving and Lossless Distributed Estimation of High-Dimensional Generalized Additive Mixed Models. CoRR abs/2210.07723 (2022) - [i56]Matthias Feurer, Katharina Eggensperger, Edward Bergman, Florian Pfisterer, Bernd Bischl, Frank Hutter:
Mind the Gap: Measuring Generalization Performance Across Multiple Objectives. CoRR abs/2212.04183 (2022) - 2021
- [j31]Ilias Gerostathopoulos, Frantisek Plásil, Christian Prehofer, Janek Thomas, Bernd Bischl:
Automated Online Experiment-Driven Adaptation-Mechanics and Cost Aspects. IEEE Access 9: 58079-58087 (2021) - [j30]Raphael Sonabend, Franz J. Király, Andreas Bender, Bernd Bischl, Michel Lang:
mlr3proba: an R package for machine learning in survival analysis. Bioinform. 37(17): 2789-2791 (2021) - [j29]Nicole Ellenbach, Anne-Laure Boulesteix, Bernd Bischl, Kristian Unger, Roman Hornung:
Improved Outcome Prediction Across Data Sources Through Robust Parameter Tuning. J. Classif. 38(2): 212-231 (2021) - [j28]Martin Binder, Florian Pfisterer, Michel Lang, Lennart Schneider, Lars Kotthoff, Bernd Bischl:
mlr3pipelines - Flexible Machine Learning Pipelines in R. J. Mach. Learn. Res. 22: 184:1-184:7 (2021) - [j27]Florian Pfisterer, Christoph Kern, Susanne Dandl, Matthew Sun, Michael Kim, Bernd Bischl:
mcboost: Multi-Calibration Boosting for R. J. Open Source Softw. 6(64): 3453 (2021) - [j26]Patrick Schratz, Jannes Muenchow, Eugenia Iturritxa, José Cortés, Bernd Bischl, Alexander Brenning:
Monitoring Forest Health Using Hyperspectral Imagery: Does Feature Selection Improve the Performance of Machine-Learning Techniques? Remote. Sens. 13(23): 4832 (2021) - [c53]Pieter Gijsbers, Florian Pfisterer, Jan N. van Rijn, Bernd Bischl, Joaquin Vanschoren:
Meta-learning for symbolic hyperparameter defaults. GECCO Companion 2021: 151-152 - [c52]Florian Pfisterer, Jan N. van Rijn, Philipp Probst, Andreas C. Müller, Bernd Bischl:
Learning multiple defaults for machine learning algorithms. GECCO Companion 2021: 241-242 - [c51]Jann Goschenhofer, Rasmus Hvingelby, David Rügamer, Janek Thomas, Moritz Wagner, Bernd Bischl:
Deep Semi-supervised Learning for Time Series Classification. ICMLA 2021: 422-428 - [c50]Bernd Bischl, Giuseppe Casalicchio, Matthias Feurer, Pieter Gijsbers, Frank Hutter, Michel Lang, Rafael Gomes Mantovani, Jan N. van Rijn, Joaquin Vanschoren:
OpenML Benchmarking Suites. NeurIPS Datasets and Benchmarks 2021 - [c49]Julia Moosbauer, Julia Herbinger, Giuseppe Casalicchio, Marius Lindauer, Bernd Bischl:
Explaining Hyperparameter Optimization via Partial Dependence Plots. NeurIPS 2021: 2280-2291 - [c48]Philipp Kopper, Sebastian Pölsterl, Christian Wachinger, Bernd Bischl, Andreas Bender, David Rügamer:
Semi-Structured Deep Piecewise Exponential Models. SPACA 2021: 40-53 - [i55]Jann Goschenhofer, Rasmus Hvingelby, David Rügamer, Janek Thomas, Moritz Wagner, Bernd Bischl:
Deep Semi-Supervised Learning for Time Series Classification. CoRR abs/2102.03622 (2021) - [i54]Florian Pargent, Florian Pfisterer, Janek Thomas, Bernd Bischl:
Regularized target encoding outperforms traditional methods in supervised machine learning with high cardinality features. CoRR abs/2104.00629 (2021) - [i53]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:
deepregression: a Flexible Neural Network Framework for Semi-Structured Deep Distributional Regression. CoRR abs/2104.02705 (2021) - [i52]Quay Au, Julia Herbinger, Clemens Stachl, Bernd Bischl, Giuseppe Casalicchio:
Grouped Feature Importance and Combined Features Effect Plot. CoRR abs/2104.11688 (2021) - [i51]Pieter Gijsbers, Florian Pfisterer, Jan N. van Rijn, Bernd Bischl, Joaquin Vanschoren:
Meta-Learning for Symbolic Hyperparameter Defaults. CoRR abs/2106.05767 (2021) - [i50]Gunnar König, Timo Freiesleben, Bernd Bischl, Giuseppe Casalicchio, Moritz Grosse-Wentrup:
Decomposition of Global Feature Importance into Direct and Associative Components (DEDACT). CoRR abs/2106.08086 (2021) - [i49]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:
Hyperparameter Optimization: Foundations, Algorithms, Best Practices and Open Challenges. CoRR abs/2107.05847 (2021) - [i48]Lennart Schneider, Florian Pfisterer, Martin Binder, Bernd Bischl:
Mutation is all you need. CoRR abs/2107.07343 (2021) - [i47]Ludwig Bothmann, Sven Strickroth, Giuseppe Casalicchio, David Rügamer, Marius Lindauer, Fabian Scheipl, Bernd Bischl:
Developing Open Source Educational Resources for Machine Learning and Data Science. CoRR abs/2107.14330 (2021) - [i46]Christoph Molnar, Timo Freiesleben, Gunnar König, Giuseppe Casalicchio, Marvin N. Wright, Bernd Bischl:
Relating the Partial Dependence Plot and Permutation Feature Importance to the Data Generating Process. CoRR abs/2109.01433 (2021) - [i45]Florian Pfisterer, Lennart Schneider, Julia Moosbauer, Martin Binder, Bernd Bischl:
YAHPO Gym - Design Criteria and a new Multifidelity Benchmark for Hyperparameter Optimization. CoRR abs/2109.03670 (2021) - [i44]Mina Rezaei, Emilio Dorigatti, David Rügamer, Bernd Bischl:
Learning Statistical Representation with Joint Deep Embedded Clustering. CoRR abs/2109.05232 (2021) - [i43]Stefan Coors, Daniel Schalk, Bernd Bischl, David Rügamer:
Automatic Componentwise Boosting: An Interpretable AutoML System. CoRR abs/2109.05583 (2021) - [i42]Mina Rezaei, Farzin Soleymani, Bernd Bischl, Shekoofeh Azizi:
Deep Bregman Divergence for Contrastive Learning of Visual Representations. CoRR abs/2109.07455 (2021) - [i41]Farzin Soleymani, Mohammad Eslami, Tobias Elze, Bernd Bischl, Mina Rezaei:
Deep Variational Clustering Framework for Self-labeling of Large-scale Medical Images. CoRR abs/2109.10777 (2021) - [i40]Daniel Schalk, Bernd Bischl, David Rügamer:
Accelerated Componentwise Gradient Boosting using Efficient Data Representation and Momentum-based Optimization. CoRR abs/2110.03513 (2021) - [i39]Tobias Weber, Michael Ingrisch, Matthias Fabritius, Bernd Bischl, David Rügamer:
Survival-oriented embeddings for improving accessibility to complex data structures. CoRR abs/2110.11303 (2021) - [i38]Tobias Weber, Michael Ingrisch, Bernd Bischl, David Rügamer:
Towards modelling hazard factors in unstructured data spaces using gradient-based latent interpolation. CoRR abs/2110.11312 (2021) - [i37]Julia Moosbauer, Julia Herbinger, Giuseppe Casalicchio, Marius Lindauer, Bernd Bischl:
Explaining Hyperparameter Optimization via Partial Dependence Plots. CoRR abs/2111.04820 (2021) - [i36]Julia Moosbauer, Martin Binder, Lennart Schneider, Florian Pfisterer, Marc Becker, Michel Lang, Lars Kotthoff, Bernd Bischl:
Automated Benchmark-Driven Design and Explanation of Hyperparameter Optimizers. CoRR abs/2111.14756 (2021) - 2020
- [j25]Andrea Bommert, Xudong Sun, Bernd Bischl, Jörg Rahnenführer, Michel Lang:
Benchmark for filter methods for feature selection in high-dimensional classification data. Comput. Stat. Data Anal. 143 (2020) - [c47]Martin Binder, Julia Moosbauer, Janek Thomas, Bernd Bischl:
Multi-objective hyperparameter tuning and feature selection using filter ensembles. GECCO 2020: 471-479 - [c46]Christoph Molnar, Gunnar König, Julia Herbinger, Timo Freiesleben, Susanne Dandl, Christian A. Scholbeck, Giuseppe Casalicchio, Moritz Grosse-Wentrup, Bernd Bischl:
General Pitfalls of Model-Agnostic Interpretation Methods for Machine Learning Models. xxAI@ICML 2020: 39-68 - [c45]Gunnar König, Christoph Molnar, Bernd Bischl, Moritz Grosse-Wentrup:
Relative Feature Importance. ICPR 2020: 9318-9325 - [c44]Andreas Bender, David Rügamer, Fabian Scheipl, Bernd Bischl:
A General Machine Learning Framework for Survival Analysis. ECML/PKDD (3) 2020: 158-173 - [c43]Christoph Molnar, Giuseppe Casalicchio, Bernd Bischl:
Interpretable Machine Learning - A Brief History, State-of-the-Art and Challenges. PKDD/ECML Workshops 2020: 417-431 - [c42]Susanne Dandl, Christoph Molnar, Martin Binder, Bernd Bischl:
Multi-Objective Counterfactual Explanations. PPSN (1) 2020: 448-469 - [e1]Bernd Bischl, Oliver Guhr, Heidi Seibold, Peter Steinbach:
Proceedings of the First Teaching Machine Learning and Artificial Intelligence Workshop, September 8+14, 2020, Virtual Conference. Proceedings of Machine Learning Research 141, PMLR 2020 [contents] - [i35]Susanne Dandl, Christoph Molnar, Martin Binder, Bernd Bischl:
Multi-Objective Counterfactual Explanations. CoRR abs/2004.11165 (2020) - [i34]Christoph Molnar, Gunnar König, Bernd Bischl, Giuseppe Casalicchio:
Model-agnostic Feature Importance and Effects with Dependent Features - A Conditional Subgroup Approach. CoRR abs/2006.04628 (2020) - [i33]Andreas Bender, David Rügamer, Fabian Scheipl, Bernd Bischl:
A General Machine Learning Framework for Survival Analysis. CoRR abs/2006.15442 (2020) - [i32]Christoph Molnar, Gunnar König, Julia Herbinger, Timo Freiesleben, Susanne Dandl, Christian A. Scholbeck, Giuseppe Casalicchio, Moritz Grosse-Wentrup, Bernd Bischl:
Pitfalls to Avoid when Interpreting Machine Learning Models. CoRR abs/2007.04131 (2020) - [i31]Gunnar König, Christoph Molnar, Bernd Bischl, Moritz Grosse-Wentrup:
Relative Feature Importance. CoRR abs/2007.08283 (2020) - [i30]Raphael Sonabend, Franz J. Király, Andreas Bender, Bernd Bischl, Michel Lang:
mlr3proba: Machine Learning Survival Analysis in R. CoRR abs/2008.08080 (2020) - [i29]David Rügamer, Florian Pfisterer, Bernd Bischl:
Neural Mixture Distributional Regression. CoRR abs/2010.06889 (2020) - [i28]Christoph Molnar, Giuseppe Casalicchio, Bernd Bischl:
Interpretable Machine Learning - A Brief History, State-of-the-Art and Challenges. CoRR abs/2010.09337 (2020) - [i27]Ashrya Agrawal, Florian Pfisterer, Bernd Bischl, Jiahao Chen, Srijan Sood, Sameena Shah, Francois Buet-Golfouse, Bilal A. Mateen, Sebastian J. Vollmer:
Debiasing classifiers: is reality at variance with expectation? CoRR abs/2011.02407 (2020) - [i26]Philipp Kopper, Sebastian Pölsterl, Christian Wachinger, Bernd Bischl, Andreas Bender, David Rügamer:
Semi-Structured Deep Piecewise Exponential Models. CoRR abs/2011.05824 (2020)
2010 – 2019
- 2019
- [j24]Matthias Schmid, Bernd Bischl, Hans A. Kestler:
Proceedings of Reisensburg 2016-2017. Comput. Stat. 34(3): 943-944 (2019) - [j23]Laura Beggel, Bernhard X. Kausler, Martin Schiegg, Michael Pfeiffer, Bernd Bischl:
Time series anomaly detection based on shapelet learning. Comput. Stat. 34(3): 945-976 (2019) - [j22]Giuseppe Casalicchio, Jakob Bossek, Michel Lang, Dominik Kirchhoff, Pascal Kerschke, Benjamin Hofner, Heidi Seibold, Joaquin Vanschoren, Bernd Bischl:
OpenML: An R package to connect to the machine learning platform OpenML. Comput. Stat. 34(3): 977-991 (2019) - [j21]Philipp Probst, Anne-Laure Boulesteix, Bernd Bischl:
Tunability: Importance of Hyperparameters of Machine Learning Algorithms. J. Mach. Learn. Res. 20: 53:1-53:32 (2019) - [j20]Michel Lang, Martin Binder, Jakob Richter, Patrick Schratz, Florian Pfisterer, Stefan Coors, Quay Au, Giuseppe Casalicchio, Lars Kotthoff, Bernd Bischl:
mlr3: A modern object-oriented machine learning framework in R. J. Open Source Softw. 4(44): 1903 (2019) - [c41]Xudong Sun, Andrea Bommert, Florian Pfisterer, Jörg Rahnenführer, Michel Lang, Bernd Bischl:
High Dimensional Restrictive Federated Model Selection with Multi-objective Bayesian Optimization over Shifted Distributions. IntelliSys (1) 2019: 629-647 - [c40]Xudong Sun, Jiali Lin, Bernd Bischl:
ReinBo: Machine Learning Pipeline Conditional Hierarchy Search and Configuration with Bayesian Optimization Embedded Reinforcement Learning. PKDD/ECML Workshops (1) 2019: 68-84 - [c39]Christoph Molnar, Giuseppe Casalicchio, Bernd Bischl:
Quantifying Model Complexity via Functional Decomposition for Better Post-hoc Interpretability. PKDD/ECML Workshops (1) 2019: 193-204 - [c38]Christian A. Scholbeck, Christoph Molnar, Christian Heumann, Bernd Bischl, Giuseppe Casalicchio:
Sampling, Intervention, Prediction, Aggregation: A Generalized Framework for Model-Agnostic Interpretations. PKDD/ECML Workshops (1) 2019: 205-216 - [c37]Laura Beggel, Michael Pfeiffer, Bernd Bischl:
Robust Anomaly Detection in Images Using Adversarial Autoencoders. ECML/PKDD (1) 2019: 206-222 - [c36]Jann Goschenhofer, Franz Michael Josef Pfister, Kamer Ali Yuksel, Bernd Bischl, Urban Fietzek, Janek Thomas:
Wearable-Based Parkinson's Disease Severity Monitoring Using Deep Learning. ECML/PKDD (3) 2019: 400-415 - [c35]Xudong Sun, Bernd Bischl:
Tutorial and Survey on Probabilistic Graphical Model and Variational Inference in Deep Reinforcement Learning. SSCI 2019: 110-119 - [c34]Xudong Sun, Alexej Gossmann, Yu Wang, Bernd Bischl:
Variational Resampling Based Assessment of Deep Neural Networks under Distribution Shift. SSCI 2019: 1344-1353 - [i25]Laura Beggel, Michael Pfeiffer, Bernd Bischl:
Robust Anomaly Detection in Images using Adversarial Autoencoders. CoRR abs/1901.06355 (2019) - [i24]Xudong Sun, Andrea Bommert, Florian Pfisterer, Jörg Rahnenführer, Michel Lang, Bernd Bischl:
High Dimensional Restrictive Federated Model Selection with multi-objective Bayesian Optimization over shifted distributions. CoRR abs/1902.08999 (2019) - [i23]Christoph Molnar, Giuseppe Casalicchio, Bernd Bischl:
Quantifying Interpretability of Arbitrary Machine Learning Models Through Functional Decomposition. CoRR abs/1904.03867 (2019) - [i22]Quay Au, Daniel Schalk, Giuseppe Casalicchio, Ramona Schödel, Clemens Stachl, Bernd Bischl:
Component-Wise Boosting of Targets for Multi-Output Prediction. CoRR abs/1904.03943 (2019) - [i21]Christian A. Scholbeck, Christoph Molnar, Christian Heumann, Bernd Bischl, Giuseppe Casalicchio:
Sampling, Intervention, Prediction, Aggregation: A Generalized Framework for Model Agnostic Interpretations. CoRR abs/1904.03959 (2019) - [i20]Xudong Sun, Jiali Lin, Bernd Bischl:
ReinBo: Machine Learning pipeline search and configuration with Bayesian Optimization embedded Reinforcement Learning. CoRR abs/1904.05381 (2019) - [i19]Jann Goschenhofer, Franz Michael Josef Pfister, Kamer Ali Yuksel, Bernd Bischl, Urban Fietzek, Janek Thomas:
Wearable-based Parkinson's Disease Severity Monitoring using Deep Learning. CoRR abs/1904.10829 (2019) - [i18]Xudong Sun, Yu Wang, Alexej Gossmann, Bernd Bischl:
Resampling-based Assessment of Robustness to Distribution Shift for Deep Neural Networks. CoRR abs/1906.02972 (2019) - [i17]Pieter Gijsbers, Erin LeDell, Janek Thomas, Sébastien Poirier, Bernd Bischl, Joaquin Vanschoren:
An Open Source AutoML Benchmark. CoRR abs/1907.00909 (2019) - [i16]Xudong Sun, Bernd Bischl:
Tutorial and Survey on Probabilistic Graphical Model and Variational Inference in Deep Reinforcement Learning. CoRR abs/1908.09381 (2019) - [i15]Florian Pfisterer, Stefan Coors, Janek Thomas, Bernd Bischl:
Multi-Objective Automatic Machine Learning with AutoxgboostMC. CoRR abs/1908.10796 (2019) - [i14]Florian Pfisterer, Janek Thomas, Bernd Bischl:
Towards Human Centered AutoML. CoRR abs/1911.02391 (2019) - [i13]Florian Pfisterer, Laura Beggel, Xudong Sun, Fabian Scheipl, Bernd Bischl:
Benchmarking time series classification - Functional data vs machine learning approaches. CoRR abs/1911.07511 (2019) - [i12]Martin Binder, Julia Moosbauer, Janek Thomas, Bernd Bischl:
Model-Agnostic Approaches to Multi-Objective Simultaneous Hyperparameter Tuning and Feature Selection. CoRR abs/1912.12912 (2019) - 2018
- [j19]Daniel Horn, Aydin Demircioglu, Bernd Bischl, Tobias Glasmachers, Claus Weihs:
A comparative study on large scale kernelized support vector machines. Adv. Data Anal. Classif. 12(4): 867-883 (2018) - [j18]Janek Thomas, Tobias Hepp, Andreas Mayr, Bernd Bischl:
Corrigendum to "Probing for Sparse and Fast Variable Selection with Model-Based Boosting". Comput. Math. Methods Medicine 2018: 2430438:1 (2018) - [j17]Hans A. Kestler, Bernd Bischl, Matthias Schmid:
Proceedings of Reisensburg 2014-2015. Comput. Stat. 33(3): 1125-1126 (2018) - [j16]Christoph Molnar, Giuseppe Casalicchio, Bernd Bischl:
iml: An R package for Interpretable Machine Learning. J. Open Source Softw. 3(26): 786 (2018) - [j15]Daniel Schalk, Janek Thomas, Bernd Bischl:
compboost: Modular Framework for Component-Wise Boosting. J. Open Source Softw. 3(30): 967 (2018) - [j14]Janek Thomas, Andreas Mayr, Bernd Bischl, Matthias Schmid, Adam Smith, Benjamin Hofner:
Gradient boosting for distributional regression: faster tuning and improved variable selection via noncyclical updates. Stat. Comput. 28(3): 673-687 (2018) - [c33]Giuseppe Casalicchio, Christoph Molnar, Bernd Bischl:
Visualizing the Feature Importance for Black Box Models. ECML/PKDD (1) 2018: 655-670 - [c32]Hans Degroote, Patrick De Causmaecker, Bernd Bischl, Lars Kotthoff:
A Regression-Based Methodology for Online Algorithm Selection. SOCS 2018: 37-45 - [i11]Giuseppe Casalicchio, Christoph Molnar, Bernd Bischl:
Visualizing the Feature Importance for Black Box Models. CoRR abs/1804.06620 (2018) - [i10]Daniel Kühn, Philipp Probst, Janek Thomas, Bernd Bischl:
Automatic Exploration of Machine Learning Experiments on OpenML. CoRR abs/1806.10961 (2018) - [i9]Janek Thomas, Stefan Coors, Bernd Bischl:
Automatic Gradient Boosting. CoRR abs/1807.03873 (2018) - [i8]Florian Pfisterer, Jan N. van Rijn, Philipp Probst, Andreas C. Müller, Bernd Bischl:
Learning Multiple Defaults for Machine Learning Algorithms. CoRR abs/1811.09409 (2018) - 2017
- [j13]Janek Thomas, Tobias Hepp, Andreas Mayr, Bernd Bischl:
Probing for Sparse and Fast Variable Selection with Model-Based Boosting. Comput. Math. Methods Medicine 2017: 1421409:1-1421409:8 (2017) - [j12]Michel Lang, Bernd Bischl, Dirk Surmann:
batchtools: Tools for R to work on batch systems. J. Open Source Softw. 2(10): 135 (2017) - [j11]Philipp Probst, Quay Au, Giuseppe Casalicchio, Clemens Stachl, Bernd Bischl:
Multilabel Classification with R Package mlr. R J. 9(1): 352 (2017) - [c31]Daniel Horn, Melanie Dagge, Xudong Sun, Bernd Bischl:
First Investigations on Noisy Model-Based Multi-objective Optimization. EMO 2017: 298-313 - [c30]Leslie Pérez Cáceres, Bernd Bischl, Thomas Stützle:
Evaluating random forest models for irace. GECCO (Companion) 2017: 1146-1153 - [c29]Helena Kotthaus, Jakob Richter, Andreas Lang, Janek Thomas, Bernd Bischl, Peter Marwedel, Jörg Rahnenführer, Michel Lang:
RAMBO: Resource-Aware Model-Based Optimization with Scheduling for Heterogeneous Runtimes and a Comparison with Asynchronous Model-Based Optimization. LION 2017: 180-195 - [i7]Giuseppe Casalicchio, Jakob Bossek, Michel Lang, Dominik Kirchhoff, Pascal Kerschke, Benjamin Hofner, Heidi Seibold, Joaquin Vanschoren, Bernd Bischl:
OpenML: An R Package to Connect to the Networked Machine Learning Platform OpenML. CoRR abs/1701.01293 (2017) - [i6]Bernd Bischl, Giuseppe Casalicchio, Matthias Feurer, Frank Hutter, Michel Lang, Rafael Gomes Mantovani, Jan N. van Rijn, Joaquin Vanschoren:
OpenML Benchmarking Suites and the OpenML100. CoRR abs/1708.03731 (2017) - 2016
- [j10]Bernd Bischl, Pascal Kerschke, Lars Kotthoff, Marius Lindauer, Yuri Malitsky, Alexandre Fréchette, Holger H. Hoos, Frank Hutter, Kevin Leyton-Brown, Kevin Tierney, Joaquin Vanschoren:
ASlib: A benchmark library for algorithm selection. Artif. Intell. 237: 41-58 (2016) - [j9]Bernd Bischl, Michel Lang, Lars Kotthoff, Julia Schiffner, Jakob Richter, Erich Studerus, Giuseppe Casalicchio, Zachary M. Jones:
mlr: Machine Learning in R. J. Mach. Learn. Res. 17: 170:1-170:5 (2016) - [c28]Hans Degroote, Bernd Bischl, Lars Kotthoff, Patrick De Causmaecker:
Reinforcement Learning for Automatic Online Algorithm Selection - an Empirical Study. ITAT 2016: 93-101 - [c27]Jakob Richter, Helena Kotthaus, Bernd Bischl, Peter Marwedel, Jörg Rahnenführer, Michel Lang:
Faster Model-Based Optimization Through Resource-Aware Scheduling Strategies. LION 2016: 267-273 - [c26]Daniel Horn, Bernd Bischl:
Multi-objective parameter configuration of machine learning algorithms using model-based optimization. SSCI 2016: 1-8 - [i5]Aydin Demircioglu, Daniel Horn, Tobias Glasmachers, Bernd Bischl, Claus Weihs:
Fast model selection by limiting SVM training times. CoRR abs/1602.03368 (2016) - [i4]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:
mlr Tutorial. CoRR abs/1609.06146 (2016) - 2015
- [j8]Olaf Mersmann, Mike Preuss, Heike Trautmann, Bernd Bischl, Claus Weihs:
Analyzing the BBOB Results by Means of Benchmarking Concepts. Evol. Comput. 23(1): 161-185 (2015) - [c25]Daniel Horn, Tobias Wagner, Dirk Biermann, Claus Weihs, Bernd Bischl:
Model-Based Multi-objective Optimization: Taxonomy, Multi-Point Proposal, Toolbox and Benchmark. EMO (1) 2015: 64-78 - [c24]Dimo Brockhoff, Bernd Bischl, Tobias Wagner:
The Impact of Initial Designs on the Performance of MATSuMoTo on the Noiseless BBOB-2015 Testbed: A Preliminary Study. GECCO (Companion) 2015: 1159-1166 - [c23]Jakob Bossek, Bernd Bischl, Tobias Wagner, Günter Rudolph:
Learning Feature-Parameter Mappings for Parameter Tuning via the Profile Expected Improvement. GECCO 2015: 1319-1326 - [c22]Rafael Gomes Mantovani, André Luis Debiaso Rossi, Joaquin Vanschoren, Bernd Bischl, André C. P. L. F. de Carvalho:
To tune or not to tune: Recommending when to adjust SVM hyper-parameters via meta-learning. IJCNN 2015: 1-8 - [c21]Rafael Gomes Mantovani, André Luis Debiaso Rossi, Joaquin Vanschoren, Bernd Bischl, André C. P. L. F. de Carvalho:
Effectiveness of Random Search in SVM hyper-parameter tuning. IJCNN 2015: 1-8 - [c20]Joaquin Vanschoren, Jan N. van Rijn, Bernd Bischl:
Taking machine learning research online with OpenML. BigMine 2015: 1-4 - [c19]Bernd Bischl:
Applying Model-Based Optimization to Hyperparameter Optimization in Machine Learning. MetaSel@PKDD/ECML 2015: 1 - [i3]Bernd Bischl, Pascal Kerschke, Lars Kotthoff, Marius Lindauer, Yuri Malitsky, Alexandre Fréchette, Holger H. Hoos, Frank Hutter, Kevin Leyton-Brown, Kevin Tierney, Joaquin Vanschoren:
ASlib: A Benchmark Library for Algorithm Selection. CoRR abs/1506.02465 (2015) - 2014
- [b1]Bernd Bischl:
Model and Algorithm Selection in Statistical Learning and Optimization. Technical University Dortmund, Germany, 2014 - [c18]Claus Weihs, Daniel Horn, Bernd Bischl:
Big Data Classification : Aspects on Many Features and Many Observations. ECDA 2014: 113-122 - [c17]Nadja Bauer, Klaus Friedrichs, Bernd Bischl, Claus Weihs:
Fast Model Based Optimization of Tone Onset Detection by Instance Sampling. ECDA 2014: 461-472 - [c16]Bernd Bischl, Simon Wessing, Nadja Bauer, Klaus Friedrichs, Claus Weihs:
MOI-MBO: Multiobjective Infill for Parallel Model-Based Optimization. LION 2014: 173-186 - [c15]Bernd Bischl, Tobias Kühn, Gero Szepannek:
On Class Imbalance Correction for Classification Algorithms in Credit Scoring. OR 2014: 37-43 - [i2]Joaquin Vanschoren, Jan N. van Rijn, Bernd Bischl, Luís Torgo:
OpenML: networked science in machine learning. CoRR abs/1407.7722 (2014) - 2013
- [j7]Olaf Mersmann, Bernd Bischl, Heike Trautmann, Markus Wagner, Jakob Bossek, Frank Neumann:
A novel feature-based approach to characterize algorithm performance for the traveling salesperson problem. Ann. Math. Artif. Intell. 69(2): 151-182 (2013) - [j6]Bernd Bischl, Julia Schiffner, Claus Weihs:
Benchmarking local classification methods. Comput. Stat. 28(6): 2599-2619 (2013) - [j5]Joaquin Vanschoren, Jan N. van Rijn, Bernd Bischl, Luís Torgo:
OpenML: networked science in machine learning. SIGKDD Explor. 15(2): 49-60 (2013) - [c14]Samadhi Nallaperuma, Markus Wagner, Frank Neumann, Bernd Bischl, Olaf Mersmann, Heike Trautmann:
A feature-based comparison of local search and the christofides algorithm for the travelling salesperson problem. FOGA 2013: 147-160 - [c13]Stefan Hess, Tobias Wagner, Bernd Bischl:
PROGRESS: Progressive Reinforcement-Learning-Based Surrogate Selection. LION 2013: 110-124 - [c12]Jan N. van Rijn, Bernd Bischl, Luís Torgo, Bo Gao, Venkatesh Umaashankar, Simon Fischer, Patrick Winter, Bernd Wiswedel, Michael R. Berthold, Joaquin Vanschoren:
OpenML: A Collaborative Science Platform. ECML/PKDD (3) 2013: 645-649 - 2012
- [j4]Bernd Bischl, Olaf Mersmann, Heike Trautmann, Claus Weihs:
Resampling Methods for Meta-Model Validation with Recommendations for Evolutionary Computation. Evol. Comput. 20(2): 249-275 (2012) - [j3]Patrick Koch, Bernd Bischl, Oliver Flasch, Thomas Bartz-Beielstein, Claus Weihs, Wolfgang Konen:
Tuning and evolution of support vector kernels. Evol. Intell. 5(3): 153-170 (2012) - [c11]Bernd Bischl, Olaf Mersmann, Heike Trautmann, Mike Preuß:
Algorithm selection based on exploratory landscape analysis and cost-sensitive learning. GECCO 2012: 313-320 - [c10]Bernd Bischl, Julia Schiffner, Claus Weihs:
Benchmarking Classification Algorithms on High-Performance Computing Clusters. GfKl 2012: 23-31 - [c9]Oliver Meyer, Bernd Bischl, Claus Weihs:
Support Vector Machines on Large Data Sets: Simple Parallel Approaches. GfKl 2012: 87-95 - [c8]Igor Vatolkin, Bernd Bischl, Günter Rudolph, Claus Weihs:
Statistical Comparison of Classifiers for Multi-objective Feature Selection in Instrument Recognition. GfKl 2012: 171-178 - [c7]Olaf Mersmann, Bernd Bischl, Jakob Bossek, Heike Trautmann, Markus Wagner, Frank Neumann:
Local Search and the Traveling Salesman Problem: A Feature-Based Characterization of Problem Hardness. LION 2012: 115-129 - [i1]Olaf Mersmann, Bernd Bischl, Heike Trautmann, Markus Wagner, Frank Neumann:
A Novel Feature-Based Approach to Characterize Algorithm Performance for the Traveling Salesman Problem. CoRR abs/1208.2318 (2012) - 2011
- [j2]Holger Blume, Bernd Bischl, Martin Botteck, Christian Igel, Rainer Martin, Günther Rötter, Günter Rudolph, Wolfgang M. Theimer, Igor Vatolkin, Claus Weihs:
Huge Music Archives on Mobile Devices. IEEE Signal Process. Mag. 28(4): 24-39 (2011) - [c6]Olaf Mersmann, Bernd Bischl, Heike Trautmann, Mike Preuss, Claus Weihs, Günter Rudolph:
Exploratory landscape analysis. GECCO 2011: 829-836 - 2010
- [c5]Bernd Bischl, Markus Eichhoff, Claus Weihs:
Selecting Groups of Audio Features by Statistical Tests and the Group Lasso. Sprachkommunikation 2010: 1-4 - [c4]Julia Schiffner, Bernd Bischl, Claus Weihs:
Bias-Variance Analysis of Local Classification Methods. GfKl 2010: 49-57 - [c3]Claus Weihs, Olaf Mersmann, Bernd Bischl, Arno Fritsch, Heike Trautmann, Till Moritz Karbach, Bernhard Spaan:
A Case Study on the Use of Statistical Classification Methods in Particle Physics. GfKl 2010: 69-77 - [c2]Bernd Bischl, Igor Vatolkin, Mike Preuss:
Selecting Small Audio Feature Sets in Music Classification by Means of Asymmetric Mutation. PPSN (1) 2010: 314-323
2000 – 2009
- 2009
- [j1]Gero Szepannek, Bernd Bischl, Claus Weihs:
On the combination of locally optimal pairwise classifiers. Eng. Appl. Artif. Intell. 22(1): 79-85 (2009) - 2007
- [c1]Gero Szepannek, Bernd Bischl, Claus Weihs:
On the Combination of Locally Optimal Pairwise Classifiers. MLDM 2007: 104-116
Coauthor Index
[j45] [c92] [i108] [i106] [j44] [j41] [j39] [c90] [c82] [c78] [c76] [i105] [i103] [i102] [i100] [i97] [i94] [i84] [j33] [c67] [c66] [c65] [c63] [c61] [c59] [c58] [c55] [c54] [i79] [i77] [i76] [i73] [i69] [i65] [i61] [i57] [c51] [c48] [i55] [i53] [i47] [i44] [i43] [i40] [i39] [i38] [c44] [i33] [i29] [i26]
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