Publications

Here you can find all publications in which at least one member of the working group was involved. You can directly access the .bib file as well as a link to access the article.

  1. Guenther F, Bender A, Katz K, Kuechenhoff H, Hoehle M (2020) Nowcasting the COVID-19 Pandemic in Bavaria. medRxiv, 2020.06.26.20140210.
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  2. Sonabend R, Király FJ, Bender A, Bischl B, Lang M (2020) mlr3proba: Machine Learning Survival Analysis in R. arXiv:2008.08080 [cs, stat].
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  3. Bender A, Python A, Lindsay SW, Golding N, Moyes CL (2020) Modelling geospatial distributions of the triatomine vectors of Trypanosoma cruzi in Latin America. PLOS Neglected Tropical Diseases 14, e0008411.
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  4. Bender A, Rügamer D, Scheipl F, Bischl B (2020) A General Machine Learning Framework for Survival Analysis. arXiv:2006.15442 [cs, stat].
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  5. Python A, Bender A, Blangiardo M et al. (2020) A downscaling approach to compare COVID-19 count data from databases aggregated at different spatial scales. medRxiv, 2020.06.17.20133959.
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  6. Dorigatti E, Schubert B (2020) Joint epitope selection and spacer design for string-of-beads vaccines. https://doi.org/10.1101/2020.04.25.060988.
  7. Molnar C, Casalicchio G, Bischl B (2020) Quantifying Model Complexity via Functional Decomposition for Better Post-hoc Interpretability. In: In: Cellier P , In: Driessens K (eds) Machine Learning and Knowledge Discovery in Databases. ECML PKDD 2019, pp. 193–204. Springer International Publishing, Cham.
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  8. Scholbeck CA, Molnar C, Heumann C, Bischl B, Casalicchio G (2020) Sampling, Intervention, Prediction, Aggregation: A Generalized Framework for Model-Agnostic Interpretations. In: In: Cellier P , In: Driessens K (eds) Machine Learning and Knowledge Discovery in Databases. ECML PKDD 2019, pp. 205–216. Springer International Publishing, Cham.
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  9. Molnar C, König G, Bischl B, Casalicchio G (2020) Model-agnostic Feature Importance and Effects with Dependent Features–A Conditional Subgroup Approach. arXiv preprint arXiv:2006.04628.
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  10. Molnar C, König G, Herbinger J et al. (2020) Pitfalls to Avoid when Interpreting Machine Learning Models. arXiv preprint arXiv:2007.04131.
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  11. Stachl C, Au Q, Schoedel R et al. (2020) Predicting personality from patterns of behavior collected with smartphones. Proceedings of the National Academy of Sciences.
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  12. Selective background Monte Carlo simulation at Belle II (2020) 24th International Conference on Computing in High Energy & Nuclear Physics (CHEP), EPJ Web of Conferences.
  13. Bommert A, Sun X, Bischl B, Rahnenführer J, Lang M (2020) Benchmark for filter methods for feature selection in high-dimensional classification data. Computational Statistics & Data Analysis 143, 106839.
  14. Binder M, Moosbauer J, Thomas J, Bischl B (2020) Multi-Objective Hyperparameter Tuning and Feature Selection using Filter Ensembles. arXiv preprint arXiv:1912.12912.
  15. Rügamer D, Greven S (2020) Inference for L2-Boosting. Statistics and Computing 30, 279–289.
  16. Rügamer D, Kolb C, Klein N (2020) A Unifying Network Architecture for Semi-Structured Deep Distributional Learning. arXiv preprint arXiv:2002.05777.
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  17. Liew BXW, Rügamer D, Stöcker A, De Nunzio AM (2020) Classifying neck pain status using scalar and functional biomechanical variables – development of a method using functional data boosting. Gait & Posture 75, 146–150.
  18. Liew B, Rügamer D, De Nunzio A, Falla D (2020) Interpretable machine learning models for classifying low back pain status using functional physiological variables. European Spine Journal 29, 1845–1859.
  19. Liew BXW, Rügamer D, Abichandani D, De Nunzio AM (2020) Classifying individuals with and without patellofemoral pain syndrome using ground force profiles – Development of a method using functional data boosting. Gait & Posture 80, 90–95.
  20. Liew BXW, Peolsson A, Rügamer D et al. (2020) Clinical predictive modelling of post-surgical recovery in individuals with cervical radiculopathy – a machine learning approach. Scientific Reports.
  21. Berninger C, Stöcker A, Rügamer D (2020) A Bayesian Time-Varying Autoregressive Model for Improved Short- and Long-Term Prediction. arXiv preprint arXiv:2006.05750.
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  22. Rügamer D, Baumann PFM, Greven S (2020) Selective Inference for Additive and Linear Mixed Models. arXiv preprint arXiv:2007.07930.
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  23. König G, Molnar C, Bischl B, Grosse-Wentrup M (2020) Relative Feature Importance. arXiv preprint arXiv:2007.08283.
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  24. Dandl S, Molnar C, Binder M, Bischl B (2020) Multi-Objective Counterfactual Explanations. In: In: Bäck T , In: Preuss M , In: Deutz A et al. (eds) Parallel Problem Solving from Nature – PPSN XVI, pp. 448–469. Springer International Publishing, Cham.
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  25. Ellenbach N, Boulesteix A-L, Bischl B, Unger K, Hornung R (2020) Improved Outcome Prediction Across Data Sources Through Robust Parameter Tuning. Journal of Classification, 1–20.
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  26. Rügamer D, Pfisterer F, Bischl B (2020) Neural Mixture Distributional Regression.
  27. Brockhaus S, Rügamer D, Greven S (2020) Boosting Functional Regression Models with FDboost. Journal of Statistical Software 94, 1–50.
  28. Rügamer D, Baumann P, Greven S (2020) Selective Inference for Additive and Mixed Models. arXiv preprint arXiv:2007.07930.
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  29. Lang M, Binder M, Richter J et al. (2019) mlr3: A modern object-oriented machine learning framework in R. Journal of Open Source Software 4, 1903.
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  30. Dorigatti E, Schubert B (2019) Graph-Theoretical Formulation of the Generalized Epitope-based Vaccine Design Problem. https://doi.org/10.1101/845503.
  31. Au Q, Schalk D, Casalicchio G, Schoedel R, Stachl C, Bischl B (2019) Component-Wise Boosting of Targets for Multi-Output Prediction. arXiv preprint arXiv:1904.03943.
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  32. Casalicchio G, Molnar C, Bischl B (2019) Visualizing the Feature Importance for Black Box Models. In: In: Berlingerio M , In: Bonchi F , In: Gärtner T , In: Hurley N , In: Ifrim G (eds) Machine Learning and Knowledge Discovery in Databases. ECML PKDD 2018, pp. 655–670. Springer International Publishing, Cham.
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  33. Pfisterer F, Beggel L, Sun X, Scheipl F, Bischl B (2019) Benchmarking time series classification – Functional data vs machine learning approaches.
  34. Pfisterer F, Thomas J, Bischl B (2019) Towards Human Centered AutoML.
  35. Völkel ST, Schödel R, Buschek D et al. (2019) 2 Opportunities and challenges of utilizing personality traits for personalization in HCI. Personalized Human-Computer Interaction, 31.
  36. Sun X, Bommert A, Pfisterer F, Rähenfürher J, Lang M, Bischl B (2019) High dimensional restrictive federated model selection with multi-objective bayesian optimization over shifted distributions Proceedings of SAI Intelligent Systems Conference, pp. 629–647. Springer.
  37. Schmid M, Bischl B, Kestler HA (2019) Proceedings of Reisensburg 2016–2017.
  38. Beggel L, Kausler BX, Schiegg M, Pfeiffer M, Bischl B (2019) Time series anomaly detection based on shapelet learning. Computational Statistics 34, 945–976.
  39. Pfisterer F, Coors S, Thomas J, Bischl B (2019) Multi-Objective Automatic Machine Learning with AutoxgboostMC.
  40. Sun X, Bischl B (2019) Tutorial and Survey on Probabilistic Graphical Model and Variational Inference in Deep Reinforcement Learning.
  41. Gijsbers P, LeDell E, Thomas J, Poirier S, Bischl B, Vanschoren J (2019) An Open Source AutoML Benchmark. CoRR abs/1907.00909.
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  42. Sun X, Wang Y, Gossmann A, Bischl B (2019) Resampling-based Assessment of Robustness to Distribution Shift for Deep Neural Networks. CoRR abs/1906.02972.
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  43. Pfister FMJ, Schumann A von, Bemetz J et al. (2019) Recognition of subjects with early-stage Parkinson from free-living unilateral wrist-sensor data using a hierarchical machine learning model JOURNAL OF NEURAL TRANSMISSION, pp. 663–663. SPRINGER WIEN SACHSENPLATZ 4-6, PO BOX 89, A-1201 WIEN, AUSTRIA.
  44. Goschenhofer J, Pfister FMJ, Yuksel KA, Bischl B, Fietzek U, Thomas J (2019) Wearable-based Parkinson’s Disease Severity Monitoring using Deep Learning. CoRR abs/1904.10829.
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  45. Sun X, Lin J, Bischl B (2019) ReinBo: Machine Learning pipeline search and configuration with Bayesian Optimization embedded Reinforcement Learning. CoRR abs/1904.05381.
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  46. Schüller N, Boulesteix A-L, Bischl B, Unger K, Hornung R (2019) Improved outcome prediction across data sources through robust parameter tuning. 221.
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  47. Carrara N, Leurent E, Laroche R, Urvoy T, Maillard O, Pietquin O (2019) Scaling up budgeted reinforcement learning. arXiv preprint arXiv:1903.01004.
  48. Pargent F, Bischl B, Thomas J (2019) A Benchmark Experiment on How to Encode Categorical Features in Predictive Modeling.
  49. Beggel L, Pfeiffer M, Bischl B (2019) Robust Anomaly Detection in Images using Adversarial Autoencoders. CoRR abs/1901.06355.
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  50. Stachl C, Au Q, Schoedel R et al. (2019) Behavioral Patterns in Smartphone Usage Predict Big Five Personality Traits.
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  51. Schuwerk T, Kaltefleiter LJ, Au J-Q, Hoesl A, Stachl C (2019) Enter the Wild: Autistic Traits and Their Relationship to Mentalizing and Social Interaction in Everyday Life. Journal of Autism and Developmental Disorders.
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  52. König G, Grosse-Wentrup M (2019) A Causal Perspective on Challenges for AI in Precision Medicine.
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  53. Molnar C, Casalicchio G, Bischl B (2018) iml: An R package for Interpretable Machine Learning. The Journal of Open Source Software 3, 786.
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  54. Thomas J, Hepp T, Mayr A, Bischl B (2018) Probing for sparse and fast variable selection with model-based boosting.
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  55. Thomas J, Mayr A, Bischl B, Schmid M, Smith A, Hofner B (2018) Gradient boosting for distributional regression: faster tuning and improved variable selection via noncyclical updates. Statistics and Computing 28, 673–687.
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  56. Kühn D, Probst P, Thomas J, Bischl B (2018) Automatic Exploration of Machine Learning Experiments on OpenML. arXiv preprint arXiv:1806.10961.
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  57. Thomas J, Coors S, Bischl B (2018) Automatic Gradient Boosting. ICML AutoML Workshop.
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  58. Schalk D, Thomas J, Bischl B (2018) compboost: Modular Framework for Component-Wise Boosting. The Journal of Open Source Software, 967.
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  59. Schoedel R, Au Q, Völkel ST et al. (2018) Digital Footprints of Sensation Seeking. Zeitschrift für Psychologie 226, 232–245.
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  60. Völkel ST, Graefe J, Schödel R et al. (2018) I Drive My Car and My States Drive Me Proceedings of the 10th International Conference on Automotive User Interfaces and Interactive Vehicular Applications - AutomotiveUI ’18, pp. 198–203. ACM Press, New York, New York, USA.
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  61. Goerigk S, Hilbert S, Jobst A et al. (2018) Predicting instructed simulation and dissimulation when screening for depressive symptoms. European Archives of Psychiatry and Clinical Neuroscience. https://doi.org/10.1007/s00406-018-0967-2.
  62. Pfisterer F, Rijn JN van, Probst P, Müller A, Bischl B (2018) Learning Multiple Defaults for Machine Learning Algorithms. stat 1050, 23.
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  63. Bischl B, Casalicchio G, Feurer M et al. (2019) OpenML benchmarking suites and the OpenML100. arXiv preprint arXiv:1708.03731.
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  64. Casalicchio G, Lesaffre E, Küchenhoff H, Bruyneel L (2017) Nonlinear Analysis to Detect if Excellent Nursing Work Environments Have Highest Well-Being. Journal of Nursing Scholarship 49, 537–547.
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  65. Casalicchio G, Bossek J, Lang M et al. (2017) OpenML: An R package to connect to the machine learning platform OpenML. Computational Statistics, 1–15.
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  66. Probst P, Au Q, Casalicchio G, Stachl C, Bischl B (2017) Multilabel Classification with R Package mlr. The R Journal 9, 352–369.
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  67. Horn D, Dagge M, Sun X, Bischl B (2017) First Investigations on Noisy Model-Based Multi-objective Optimization. In: In: Trautmann H , In: Rudolph G , In: Klamroth K et al. (eds) Evolutionary Multi-Criterion Optimization: 9th International Conference, EMO 2017, Münster, Germany, March 19-22, 2017, Proceedings, pp. 298–313. Springer International Publishing, Cham.
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  68. Bischl B, Richter J, Bossek J, Horn D, Thomas J, Lang M (2017) mlrMBO: A Modular Framework for Model-Based Optimization of Expensive Black-Box Functions. arXiv preprint arXiv:1703.03373.
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  69. Kotthaus H, Richter J, Lang A et al. (2017) RAMBO: Resource-Aware Model-Based Optimization with Scheduling for Heterogeneous Runtimes and a Comparison with Asynchronous Model-Based Optimization International Conference on Learning and Intelligent Optimization, pp. 180–195. Springer.
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  70. Lang M, Bischl B, Surmann D (2017) batchtools: Tools for R to work on batch systems. The Journal of Open Source Software 2.
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  71. Thomas J, Hepp T, Mayr A, Bischl B (2017) Probing for sparse and fast variable selection with model-based boosting.
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  72. Stachl C, Hilbert S, Au Q et al. (2017) Personality Traits Predict Smartphone Usage (C Wrzus, Ed.). European Journal of Personality 31, 701–722.
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  73. Bischl B, Lang M, Kotthoff L et al. (2016) mlr: Machine Learning in R. The Journal of Machine Learning Research 17, 5938–5942.
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  74. Casalicchio G, Bischl B, Boulesteix A-L, Schmid M (2015) The residual-based predictiveness curve: A visual tool to assess the performance of prediction models. Biometrics 72, 392–401.
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  75. Rietzler M, Geiselhart F, Thomas J, Rukzio E (2016) FusionKit: a generic toolkit for skeleton, marker and rigid-body tracking Proceedings of the 8th ACM SIGCHI Symposium on Engineering Interactive Computing Systems, pp. 73–84. ACM.
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  76. Schiffner J, Bischl B, Lang M et al. (2016) mlr Tutorial.
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  77. Bischl B, Kühn T, Szepannek G (2016) On Class Imbalance Correction for Classification Algorithms in Credit Scoring Operations Research Proceedings 2014, pp. 37–43. Springer International Publishing.
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  78. Degroote H, Bischl B, Kotthoff L, Causmaecker PD (2016) Reinforcement Learning for Automatic Online Algorithm Selection - an Empirical Study Proceedings of the 16th ITAT Conference Information Technologies - Applications and Theory, Tatranské Matliare, Slovakia, September 15-19, 2016., pp. 93–101.
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  79. Horn D, Bischl B (2016) Multi-objective Parameter Configuration of Machine Learning Algorithms using Model-Based Optimization Computational Intelligence (SSCI), 2016 IEEE Symposium Series on, pp. 1–8. IEEE.
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  80. Richter J, Kotthaus H, Bischl B, Marwedel P, Rahnenführer J, Lang M (2016) Faster Model-Based Optimization through Resource-Aware Scheduling Strategies Proceedings of the 10th Learning and Intelligent OptimizatioN Conference (LION 10), Ischia Island (Napoli), Italy.
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  81. Horn D, Demircioğlu A, Bischl B, Glasmachers T, Weihs C (2016) A Comparative Study on Large Scale Kernelized Support Vector Machines. Advances in Data Analysis and Classification, 1–17.
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  82. Weihs C, Horn D, Bischl B (2016) Big data Classification: Aspects on Many Features and Many Observations. In: In: Wilhelm AFX , In: Kestler HA (eds) Analysis of Large and Complex Data, pp. 113–122. Springer International Publishing, Cham.
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  83. Demirciğlu A, Horn D, Glasmachers T, Bischl B, Weihs C (2016) Fast model selection by limiting SVM training times. arxiv.org
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  84. Casalicchio G, Tutz G, Schauberger G (2015) Subject-specific Bradley–Terry–Luce models with implicit variable selection. Statistical Modelling 15, 526–547.
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  85. Bauer N, Friedrichs K, Bischl B, Weihs C (2015) Fast Model Based Optimization of Tone Onset Detection by Instance Sampling Data Analysis, Machine Learning and Knowledge Discovery,
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  86. Bischl B, Kühn T, Szepannek G (2015) On Class Imbalancy Correction for Classification Algorithms in Credit Scoring Operations Research Proceedings 2014, Springer.
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  87. Bossek J, Bischl B, Wagner T, Rudolph G (2015) Learning feature-parameter mappings for parameter tuning via the profile expected improvement Proceedings of the 2015 Annual Conference on Genetic and Evolutionary Computation, pp. 1319–1326. ACM.
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  88. Brockhoff D, Bischl B, Wagner T (2015) The Impact of Initial Designs on the Performance of MATSuMoTo on the Noiseless BBOB-2015 Testbed: A Preliminary Study GECCO ’15 Companion, Madrid, Spain.
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  89. Horn D, Wagner T, Biermann D, Weihs C, Bischl B (2015) Model-Based Multi-Objective Optimization: Taxonomy, Multi-Point Proposal, Toolbox and Benchmark. In: In: Gaspar-Cunha A , In: Henggeler Antunes C , In: Coello CC (eds) Evolutionary Multi-Criterion Optimization (EMO), pp. 64–78. Springer.
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  90. Bischl B, Lang M, Mersmann O, Rahnenführer J, Weihs C (2015) BatchJobs and BatchExperiments: Abstraction Mechanisms for Using R in Batch Environments. Journal of Statistical Software 64, 1–25.
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  91. Feilke M, Bischl B, Schmid VJ, Gertheiss J (2015) Boosting in non-linear regression models with an application to DCE-MRI data. Methods of Information in Medicine.
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  92. Kotthaus H, Korb I, Lang M, Bischl B, Rahnenführer J, Marwedel P (2015) Runtime and memory consumption analyses for machine learning R programs. Journal of Statistical Computation and Simulation 85, 14–29.
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  93. Lang M, Kotthaus H, Marwedel P, Weihs C, Rahnenführer J, Bischl B (2015) Automatic model selection for high-dimensional survival analysis. Journal of Statistical Computation and Simulation 85, 62–76.
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  94. Bischl B, Schiffner J, Weihs C (2014) Benchmarking Classification Algorithms on High-Performance Computing Clusters. In: In: Spiliopoulou M , In: Schmidt-Thieme L , In: Janning R (eds) Data Analysis, Machine Learning and Knowledge Discovery, pp. 23–31. Springer.
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  95. Bischl B, Wessing S, Bauer N, Friedrichs K, Weihs C (2014) MOI-MBO: Multiobjective Infill for Parallel Model-Based Optimization. In: In: Pardalos PM , In: Resende MGC , In: Vogiatzis C , In: Walteros JL (eds) Learning and Intelligent Optimization, pp. 173–186. Springer.
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  96. Kerschke P, Preuss M, Hernández C et al. (2014) Cell Mapping Techniques for Exploratory Landscape Analysis Proceedings of the EVOLVE 2014: A Bridge between Probability, Set Oriented Numerics, and Evolutionary Computation, pp. 115–131. Springer.
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  97. Meyer O, Bischl B, Weihs C (2014) Support Vector Machines on Large Data Sets: Simple Parallel Approaches. In: In: Spiliopoulou M , In: Schmidt-Thieme L , In: Janning R (eds) Data Analysis, Machine Learning and Knowledge Discovery, pp. 87–95. Springer.
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  98. Vatolkin I, Bischl B, Rudolph G, Weihs C (2014) Statistical Comparison of Classifiers for Multi-objective Feature Selection in Instrument Recognition. In: In: Spiliopoulou M , In: Schmidt-Thieme L , In: Janning R (eds) Data Analysis, Machine Learning and Knowledge Discovery, pp. 171–178. Springer.
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  99. Bischl B, Kerschke P, Kotthoff L et al. (2014) ASlib: A Benchmark Library for Algorithm Selection. Artificial Intelligence.
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  100. Mersmann O, Preuss M, Trautmann H, Bischl B, Weihs C (2014) Analyzing the BBOB Results by Means of Benchmarking Concepts. Evolutionary Computation Journal.
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  101. Vanschoren J, Rijn JN van, Bischl B, Torgo L (2014) OpenML: Networked Science in Machine Learning. SIGKDD Explor. Newsl. 15, 49–60.
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  102. Bergmann S, Ziegler N, Bartels T et al. (2013) Prevalence and severity of foot pad alterations in German turkey poults during the early rearing phase. Poultry science 92, 1171–1176.
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  103. Ziegler N, Bergmann S, Huebei J et al. (2013) Climate parameters and the influence on the foot pad health status of fattening turkeys BUT 6 during the early rearing phase. BERLINER UND MUNCHENER TIERARZTLICHE WOCHENSCHRIFT 126, 181–188.
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  104. Hess S, Wagner T, Bischl B (2013) PROGRESS: Progressive Reinforcement-Learning-Based Surrogate Selection. In: In: Nicosia G , In: Pardalos P (eds) Learning and Intelligent Optimization, pp. 110–124. Springer.
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  105. Nallaperuma S, Wagner M, Neumann F, Bischl B, Mersmann O, Trautmann H (2013) A Feature-Based Comparison of Local Search and the Christofides Algorithm for the Travelling Salesperson Problem Foundations of Genetic Algorithms (FOGA),
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  106. Rijn J van, Bischl B, Torgo L et al. (2013) OpenML: A Collaborative Science Platform ECML/PKDD (3), pp. 645–649.
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  107. Rijn J van, Umaashankar V, Fischer S et al. (2013) A RapidMiner extension for Open Machine Learning RapidMiner Community Meeting and Conference (RCOMM), pp. 59–70.
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  108. Bischl B, Schiffner J, Weihs C (2013) Benchmarking local classification methods. Computational Statistics 28, 2599–2619.
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  109. Mersmann O, Bischl B, Trautmann H, Wagner M, Bossek J, Neumann F (2013) A novel feature-based approach to characterize algorithm performance for the traveling salesperson problem. Annals of Mathematics and Artificial Intelligence March, 1–32.
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  110. Mersmann O, Bischl B, Trautmann H, Wagner M, Bossek J, Neumann F (2013) A novel feature-based approach to characterize algorithm performance for the traveling salesperson problem. Annals of Mathematics and Artificial Intelligence March, 1–32.
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  111. Bischl B, Mersmann O, Trautmann H, Preuss M (2012) Algorithm Selection Based on Exploratory Landscape Analysis and Cost-Sensitive Learning Genetic and Evolutionary Computation Conference (GECCO),
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  112. Mersmann O, Bischl B, Bossek J, Trautmann H, M. W, Neumann F (2012) Local Search and the Traveling Salesman Problem: A Feature-Based Characterization of Problem Hardness Learning and Intelligent Optimization Conference (LION),
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  113. Schiffner J, Bischl B, Weihs C (2012) Bias-variance analysis of local classification methods. In: In: Gaul W , In: Geyer-Schulz A , In: Schmidt-Thieme L , In: Kunze J (eds) Challenges at the Interface of Data Analysis, Computer Science, and Optimization, pp. 49–57. Springer, Berlin Heidelberg.
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  114. Bischl B, Mersmann O, Trautmann H, Weihs C (2012) Resampling Methods for Meta-Model Validation with Recommendations for Evolutionary Computation. Evolutionary Computation 20, 249–275.
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  115. Koch P, Bischl B, Flasch O, Bartz-Beielstein T, Weihs C, Konen W (2012) Tuning and evolution of support vector kernels. Evolutionary Intelligence 5, 153–170.
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  116. Bischl B, Lang M, Mersmann O, Rahnenfuehrer J, Weihs C (2012) Computing on high performance clusters with R: Packages BatchJobs and BatchExperiments. SFB 876, TU Dortmund University
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  117. Mersmann O, Bischl B, Trautmann H, Preuss M, Weihs C, Rudolph G (2011) Exploratory Landscape Analysis. In: In: Krasnogor N (ed) Proceedings of the 13th annual conference on genetic and evolutionary computation (GECCO ’11), pp. 829–836. ACM, New York, NY, USA.
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  118. Weihs C, Friedrichs K, Bischl B (2011) Statistics for hearing aids: Auralization Second Bilateral German-Polish Symposium on Data Analysis and its Applications (GPSDAA),
  119. Blume H, Bischl B, Botteck M et al. (2011) Huge Music Archives on Mobile Devices. IEEE Signal Processing Magazine 28, 24–39.
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  120. Koch P, Bischl B, Flasch O, Bartz-Beielstein T, Konen W (2011) On the Tuning and Evolution of Support Vector Kernels. Research Center CIOP (Computational Intelligence, Optimization and Data Mining), Cologne University of Applied Science, Faculty of Computer Science and Engineering Science
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  121. Bischl B, Mersmann O, Trautmann H (2010) Resampling Methods in Model Validation. In: In: Bartz-Beielstein T , In: Chiarandini M , In: Paquete L , In: Preuss M (eds) 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.
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  122. Bischl B, Eichhoff M, Weihs C (2010) Selecting Groups of Audio Features by Statistical Tests and the Group Lasso 9. ITG Fachtagung Sprachkommunikation, VDE Verlag, Berlin, Offenbach.
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  123. Bischl B, Vatolkin I, Preuss M (2010) Selecting Small Audio Feature Sets in Music Classification by Means of Asymmetric Mutation PPSN XI: Proceedings of the 11th International Conference on Parallel Problem Solving from Nature, pp. 314–323. Springer.
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  124. Szepannek G, Gruhne M, Bischl B et al. (2010) Perceptually Based Phoneme Recognition in Popular Music. In: In: Locarek-Junge H , In: Weihs C (eds) Classification as a Tool for Research, pp. 751–758. Springer, Berlin Heidelberg.
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  125. Weihs C, O. M, Bischl B et al. (2010) A Case Study on the Use of Statistical Classification Methods in Particle Physics MSDM2010, Tunis,
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  126. Bischl B, Ligges U, Weihs C (2009) Frequency estimation by DFT interpolation: A comparison of methods. SFB 475, Faculty of Statistics, TU Dortmund, Germany
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