Prof. Dr. Bernd Bischl

About

I am Professor of Computational Statistics at the department of statistics at LMU Munich. I like to work in data science, machine learning and everything computational in statistics.

Contact

Institut für Statistik

Ludwig-Maximilians-Universität München

Ludwigstraße 33

D-80539 München

Room 341, 3rd floor

Phone: +49 89 2180 3165

bernd.bischl [at] stat.uni-muenchen.de or

bernd_bischl@gmx.net

Research interest

You can find me on

Membership

Dissertation

Dissertation. It is cumulative, so I encourage you look at my published papers.

Journal Articles

[17] D. Horn, A. Demircioğlu, B. Bischl, T. Glasmachers, and C. Weihs. A comparative study on large scale kernelized support vector machines. Advances in Data Analysis and Classification, pages 1–17, 2016. [ bib | DOI | .pdf | http ]
[16] B. Bischl, M. Lang, L. Kotthoff, J. Schiffner, J. Richter, E. Studerus, G. Casalicchio, and Z. M. Jones. mlr: Machine learning in r. Journal of Machine Learning Research, 17(170):1–5, 2016. [ bib | .pdf | .html ]
[15] C. Weihs, D. Horn, and B. Bischl. Big data Classification: Aspects on Many Features and Many Observations, pages 113–122. Springer International Publishing, Cham, 2016. [ bib | DOI | .pdf | http ]
[14] B. Bischl, M. Lang, O. Mersmann, J. Rahnenführer, and C. Weihs. BatchJobs and BatchExperiments: Abstraction mechanisms for using R in batch environments. Journal of Statistical Software, 64(11):1–25, Mar. 2015. [ bib | http ]
[13] G. Casalicchio, B. Bischl, A.-L. Boulesteix, and M. Schmid. The residual-based predictiveness curve - a visual tool to assess the performance of prediction models (PREPARING SUBMISSION). Biometrics, 2015. [ bib ]
[12] M. Feilke, B. Bischl, V. Schmid, and J. Gertheiss. Boosting in non-linear regression models with an application to DCE-MRI data (PREPARING SUBMISSION). Methods of Information in Medicine, 2015. [ bib ]
[11] H. Kotthaus, I. Korb, M. Lang, B. Bischl, J. Rahnenführer, and P. Marwedel. Runtime and memory consumption analyses for machine learning R programs. Journal of Statistical Computation and Simulation, 85(1):14–29, 2015. [ bib | DOI | arXiv | .pdf | http ]
[10] M. Lang, H. Kotthaus, P. Marwedel, C. Weihs, J. Rahnenführer, and B. Bischl. Automatic model selection for high-dimensional survival analysis. Journal of Statistical Computation and Simulation, 85(1):62–76, 2015. [ bib | DOI | .pdf | http ]
[9] J. Vanschoren, J. N. van Rijn, B. Bischl, and L. Torgo. OpenML: Networked science in machine learning. SIGKDD Explor. Newsl., 15(2):49–60, June 2014. [ bib | DOI | .pdf | http ]
[8] B. Bischl, P. Kerschke, L. Kotthoff, M. Lindauer, Y. Malitsky, A. Frechétte, H. Hoos, F. Hutter, K. Leyton-Brown, K. Tierney, and J. Vanschoren. ASlib: A benchmark library for algorithm selection (SUBMITTED). Artificial Intelligence, 2014. [ bib ]
[7] O. Mersmann, M. Preuss, H. Trautmann, B. Bischl, and C. Weihs. Analyzing the BBOB results by means of benchmarking concepts. Evolutionary Computation Journal, 2014. [ bib | DOI | .pdf | http ]
[6] B. Bischl, J. Schiffner, and C. Weihs. Benchmarking local classification methods. Computational Statistics, 28(6):2599–2619, 2013. [ bib | DOI | .pdf | http ]
[5] O. Mersmann, B. Bischl, H. Trautmann, M. Wagner, J. Bossek, and F. Neumann. A novel feature-based approach to characterize algorithm performance for the traveling salesperson problem. Annals of Mathematics and Artificial Intelligence, March:1–32, 2013. [ bib | DOI | .pdf | http ]
[4] B. Bischl, O. Mersmann, H. Trautmann, and C. Weihs. Resampling methods for meta-model validation with recommendations for evolutionary computation. Evolutionary Computation, 20(2):249–275, 2012. [ bib | DOI | http ]
[3] P. Koch, B. Bischl, O. Flasch, T. Bartz-Beielstein, C. Weihs, and W. Konen. Tuning and evolution of support vector kernels. Evolutionary Intelligence, 5(3):153–170, 2012. [ bib | DOI | .pdf | http ]
[2] H. Blume, B. Bischl, M. Botteck, C. Igel, R. Martin, G. Roetter, G. Rudolph, W. Theimer, I. Vatolkin, and C. Weihs. Huge music archives on mobile devices. IEEE Signal Processing Magazine, 28(4):24–39, 2011. [ bib | DOI | http ]
[1] G. Szepannek, B. Bischl, and C. Weihs. On the combination of locally optimal pairwise classifiers. Journal of Engineering Applications of Artificial Intelligence, 22(1):79–85, 2008. [ bib | .pdf | http ]

Conference Articles (Peer Reviewed)

[28] B. Bischl, T. Kühn, and G. Szepannek. On class imbalance correction for classification algorithms in credit scoring. In Operations Research Proceedings 2014, pages 37–43. Springer International Publishing, 2016. [ bib | .pdf ]
[27] H. Degroote, B. Bischl, L. Kotthoff, and P. D. Causmaecker. Reinforcement learning for automatic online algorithm selection - an empirical study. In Proceedings of the 16th ITAT Conference Information Technologies - Applications and Theory, Tatranské Matliare, Slovakia, September 15-19, 2016., pages 93–101, 2016. [ bib | .pdf ]
[26] J. Richter, H. Kotthaus, B. Bischl, P. Marwedel, J. Rahnenführer, and M. Lang. Faster model-based optimization through resource-aware scheduling strategies (accepted for publication). In Proceedings of the 10th Learning and Intelligent OptimizatioN Conference (LION 10), Ischia Island (Napoli), Italy, 2016. [ bib | .pdf ]
[25] D. Brockhoff, B. Bischl, and T. Wagner. The Impact of Initial Designs on the Performance of MATSuMoTo on the Noiseless BBOB-2015 Testbed: A Preliminary Study. In GECCO '15 Companion, Madrid, Spain, July 2015. [ bib | DOI | http | .pdf ]
[24] N. Bauer, K. Friedrichs, B. Bischl, and C. Weihs. Fast model based optimization of tone onset detection by instance sampling (SUBMITTED). In Data Analysis, Machine Learning and Knowledge Discovery, Studies in Classification, Data Analysis, and Knowledge Organization, 2015. [ bib ]
[23] B. Bischl, T. Kühn, and G. Szepannek. On class imbalancy correction for classification algorithms in credit scoring (SUBMITTED). In Operations Research Proceedings 2014, Operations Research Proceedings. Springer, 2015. [ bib ]
[22] D. Horn, T. Wagner, D. Biermann, C. Weihs, and B. Bischl. Model-based multi-objective optimization: Taxonomy, multi-point proposal, toolbox and benchmark. In A. Gaspar-Cunha, C. Henggeler Antunes, and C. C. Coello, editors, Evolutionary Multi-Criterion Optimization (EMO), volume 9018 of Lecture Notes in Computer Science, pages 64–78. Springer, 2015. [ bib | DOI | .pdf | http ]
[21] B. Bischl, J. Schiffner, and C. Weihs. Benchmarking classification algorithms on high-performance computing clusters. In M. Spiliopoulou, L. Schmidt-Thieme, and R. Janning, editors, Data Analysis, Machine Learning and Knowledge Discovery, Studies in Classification, Data Analysis, and Knowledge Organization, pages 23–31. Springer, 2014. [ bib | DOI | .pdf | http ]
[20] B. Bischl, S. Wessing, N. Bauer, K. Friedrichs, and C. Weihs. MOI-MBO: Multiobjective infill for parallel model-based optimization. In P. M. Pardalos, M. G. Resende, C. Vogiatzis, and J. L. Walteros, editors, Learning and Intelligent Optimization, Lecture Notes in Computer Science, pages 173–186. Springer, 2014. [ bib | DOI | http ]
[19] P. Kerschke, M. Preuss, C. Hernández, O. Schütze, J.-Q. Sun, C. Grimme, G. Rudolph, B. Bischl, and H. Trautmann. Cell mapping techniques for exploratory landscape analysis. In Proceedings of the EVOLVE 2014: A Bridge between Probability, Set Oriented Numerics, and Evolutionary Computation, pages 115–131. Springer, 2014. [ bib | .pdf ]
[18] O. Meyer, B. Bischl, and C. Weihs. Support vector machines on large data sets: Simple parallel approaches. In M. Spiliopoulou, L. Schmidt-Thieme, and R. Janning, editors, Data Analysis, Machine Learning and Knowledge Discovery, Studies in Classification, Data Analysis, and Knowledge Organization, pages 87–95. Springer, 2014. [ bib | DOI | .pdf | http ]
[17] I. Vatolkin, B. Bischl, G. Rudolph, and C. Weihs. Statistical comparison of classifiers for multi-objective feature selection in instrument recognition. In M. Spiliopoulou, L. Schmidt-Thieme, and R. Janning, editors, Data Analysis, Machine Learning and Knowledge Discovery, Studies in Classification, Data Analysis, and Knowledge Organization, pages 171–178. Springer, 2014. [ bib | DOI | .pdf | http ]
[16] S. Hess, T. Wagner, and B. Bischl. PROGRESS: Progressive reinforcement-learning-based surrogate selection. In G. Nicosia and P. Pardalos, editors, Learning and Intelligent Optimization, Lecture Notes in Computer Science, pages 110–124. Springer, 2013. [ bib | DOI | .pdf | http ]
[15] S. Nallaperuma, M. Wagner, F. Neumann, B. Bischl, O. Mersmann, and H. Trautmann. A feature-based comparison of local search and the christofides algorithm for the travelling salesperson problem. In Foundations of Genetic Algorithms (FOGA), 2013. [ bib | DOI | .pdf | http ]
[14] J. van Rijn, B. Bischl, L. Torgo, G. Gao, V. Umaashankar, S. Fischer, P. Winter, B. Wiswedel, M. Berthold, and J. Vanschoren. OpenML: A collaborative science platform. In ECML/PKDD (3), pages 645–649, 2013. [ bib | DOI | .pdf ]
[13] J. van Rijn, V. Umaashankar, S. Fischer, B. Bischl, L. Torgo, B. Gao, P. Winter, B. Wiswedel, M. R. Berthold, and J. Vanschoren. A RapidMiner extension for Open Machine Learning. In RapidMiner Community Meeting and Conference (RCOMM), pages 59–70, 2013. [ bib | .pdf ]
[12] B. Bischl, O. Mersmann, H. Trautmann, and M. Preuss. Algorithm selection based on exploratory landscape analysis and cost-sensitive learning. In Genetic and Evolutionary Computation Conference (GECCO), 2012. [ bib | DOI | .pdf | http ]
[11] O. Mersmann, B. Bischl, J. Bossek, H. Trautmann, W. M., and F. Neumann. Local search and the traveling salesman problem: A feature-based characterization of problem hardness. In Learning and Intelligent Optimization Conference (LION), 2012. [ bib | DOI | .pdf ]
[10] J. Schiffner, B. Bischl, and C. Weihs. Bias-variance analysis of local classification methods. In W. Gaul, A. Geyer-Schulz, L. Schmidt-Thieme, and J. Kunze, editors, Challenges at the Interface of Data Analysis, Computer Science, and Optimization, Studies in Classification, Data Analysis, and Knowledge Organization, pages 49–57, Berlin Heidelberg, 2012. Springer. [ bib | DOI | .pdf | http ]
[9] C. Weihs, O. Mersmann, B. Bischl, A. Fritsch, H. Trautmann, T. Karbach, and B. Spaan. A case study on the use of statistical classification methods in particle physics. In W. Gaul, A. Geyer-Schulz, L. Schmidt-Thieme, and J. Kunze, editors, Challenges at the Interface of Data Analysis, Computer Science, and Optimization, Studies in Classification, Data Analysis, and Knowledge Organization, pages 69–77, Berlin Heidelberg, 2012. Springer. [ bib | DOI | http ]
[8] O. Mersmann, B. Bischl, H. Trautmann, M. Preuss, C. Weihs, and G. Rudolph. Exploratory landscape analysis. In N. Krasnogor, editor, Proceedings of the 13th annual conference on genetic and evolutionary computation (GECCO 11), pages 829–836, New York, NY, USA, 2011. ACM. [ bib | DOI | .pdf | http ]
[7] C. Weihs, K. Friedrichs, and B. Bischl. Statistics for hearing aids: Auralization. In Second Bilateral German-Polish Symposium on Data Analysis and its Applications (GPSDAA), 2011. [ bib | .pdf ]
[6] B. Bischl, O. Mersmann, and H. Trautmann. Resampling methods in model validation. In T. Bartz-Beielstein, M. Chiarandini, L. Paquete, and M. Preuss, editors, WEMACS – Proceedings of the Workshop on Experimental Methods for the Assessment of Computational Systems, Technical Report TR 10-2-007. Department of Computer Science, TU Dortmund University, 2010. [ bib | .pdf ]
[5] B. Bischl, M. Eichhoff, and C. Weihs. Selecting groups of audio features by statistical tests and the group lasso. In 9. ITG Fachtagung Sprachkommunikation, Berlin, Offenbach, 2010. VDE Verlag. [ bib | .pdf | .html ]
[4] B. Bischl, I. Vatolkin, and M. Preuss. Selecting small audio feature sets in music classification by means of asymmetric mutation. In PPSN XI: Proceedings of the 11th International Conference on Parallel Problem Solving from Nature, volume 6238 of Lecture Notes in Computer Science, pages 314–323. Springer, 2010. [ bib | DOI | .pdf | http ]
[3] G. Szepannek, M. Gruhne, B. Bischl, S. Krey, T. Harczos, F. Klefenz, C. Dittmar, and C. Weihs. Perceptually based phoneme recognition in popular music. In H. Locarek-Junge and C. Weihs, editors, Classification as a Tool for Research, volume 40 of Studies in Classification, Data Analysis, and Knowledge Organization, pages 751–758, Berlin Heidelberg, 2010. Springer. [ bib | DOI | .pdf | http ]
[2] C. Weihs, M. O., B. Bischl, A. Fritsch, H. Trautmann, T.-M. Karbach, and B. Spaan. A case study on the use of statistical classification methods in particle physics. In MSDM2010, Tunis, 2010. [ bib | .pdf ]
[1] G. Szepannek, B. Bischl, and C. Weihs. On the combination of locally optimal pairwise classifiers. In Machine Learning and Data Mining in Pattern Recognition, volume 4571 of Lecture Notes in Computer Science, pages 104–116, Berlin Heidelberg, 2007. Springer. [ bib | DOI | .pdf | http ]

Technical Reports (Not Peer Reviewed)

[4] A. Demirciğlu, D. Horn, T. Glasmachers, B. Bischl, and C. Weihs. Fast model selection by limiting svm training times. Technical Report arxiv:1302.1602.03368v1, arxiv.org, 2016. [ bib | .pdf ]
[3] B. Bischl, M. Lang, O. Mersmann, J. Rahnenfuehrer, and C. Weihs. Computing on high performance clusters with R: Packages BatchJobs and BatchExperiments. Technical report, TU Dortmund, 2012. [ bib | .pdf ]
[2] P. Koch, B. Bischl, O. Flasch, T. Bartz-Beielstein, and W. Konen. On the tuning and evolution of support vector kernels. Technical report, Cologne University of Applied Science, Cologne University of Applied Science, Faculty of Computer Science and Engineering Science, 2011. [ bib | .pdf ]
[1] B. Bischl, U. Ligges, and C. Weihs. Frequency estimation by DFT interpolation: A comparison of methods. Technical report, TU Dortmund, 2009. [ bib | .pdf ]