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ORCIDBernd Bischl
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- affiliation: LMU Munich, Department of Statistics, Germany
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2020 – today
- 2022
[c53]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- [i66]Christian A. Scholbeck, Giuseppe Casalicchio, Christoph Molnar, Bernd Bischl, Christian Heumann:
Marginal Effects for Non-Linear Prediction Functions. CoRR abs/2201.08837 (2022) - [i65]Emilio Dorigatti, Jann Goschenhofer, Benjamin Schubert, Mina Rezaei, Bernd Bischl:
Positive-Unlabeled Learning with Uncertainty-aware Pseudo-label Selection. CoRR abs/2201.13192 (2022) - [i64]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) - [i63]Julia Herbinger, Bernd Bischl, Giuseppe Casalicchio:
REPID: Regional Effect Plots with implicit Interaction Detection. CoRR abs/2202.07254 (2022) - [i62]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) - [i61]Felix Ott, David Rügamer, Lucas Heublein, Bernd Bischl, Christopher Mutschler:
Cross-Modal Common Representation Learning with Triplet Loss Functions. CoRR abs/2202.07901 (2022) - [i60]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) - [i59]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) - [i58]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) - [i57]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) - [i56]Difan Deng, Florian Karl, Frank Hutter, Bernd Bischl, Marius Lindauer:
Efficient Automated Deep Learning for Time Series Forecasting. CoRR abs/2205.05511 (2022) - 2021
- [j26]Ilias Gerostathopoulos
, Frantisek Plasil, Christian Prehofer, Janek Thomas, Bernd Bischl:
Automated Online Experiment-Driven Adaptation-Mechanics and Cost Aspects. IEEE Access 9: 58079-58087 (2021) - [j25]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) - [j24]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) - [j23]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) - [d5]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) - [j22]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) - [c52]Pieter Gijsbers, Florian Pfisterer, Jan N. van Rijn, Bernd Bischl, Joaquin Vanschoren:
Meta-learning for symbolic hyperparameter defaults. GECCO Companion 2021: 151-152 - [c51]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 - [c50]Jann Goschenhofer, Rasmus Hvingelby, David Rügamer, Janek Thomas, Moritz Wagner, Bernd Bischl:
Deep Semi-supervised Learning for Time Series Classification. ICMLA 2021: 422-428 - [c49]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 - [c48]Julia Moosbauer, Julia Herbinger, Giuseppe Casalicchio, Marius Lindauer, Bernd Bischl:
Explaining Hyperparameter Optimization via Partial Dependence Plots. NeurIPS 2021: 2280-2291 - [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
- [j21]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 - [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
- [j20]Matthias Schmid, Bernd Bischl, Hans A. Kestler:
Proceedings of Reisensburg 2016-2017. Comput. Stat. 34(3): 943-944 (2019) - [j19]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) - [j18]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) - [j17]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) - [d4]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
- [j16]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) - [j15]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) - [j14]Hans A. Kestler, Bernd Bischl, Matthias Schmid:
Proceedings of Reisensburg 2014-2015. Comput. Stat. 33(3): 1125-1126 (2018) - [d3]Christoph Molnar, Giuseppe Casalicchio, Bernd Bischl:
iml: An R package for Interpretable Machine Learning. J. Open Source Softw. 3(26): 786 (2018) - [d2]Daniel Schalk, Janek Thomas, Bernd Bischl:
compboost: Modular Framework for Component-Wise Boosting. J. Open Source Softw. 3(30): 967 (2018) - [j13]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
- [j12]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) - [d1]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]