Research

Research groups

Conferences

We maintain a list of conferences on WikiCFP that interests us: Click here

Publications

A full list of publications in BibTex format is available here

[2021, 2020, 2019, 2018, 2017, 2016, 2015, 2014, 2013, 2012, 2011, 2010, 2009, 2008, 2007]

2021

  1. Liew BXW, Rügamer D, Zhai XJ, Morris S, Netto K (2021) Comparing machine, deep, and transfer learning in predicting joint moments in running. Journal of Biomechanics.
  2. Schalk D, Bischl B, Rügamer D (2021) Accelerated Componentwise Gradient Boosting using Efficient Data Representation and Momentum-based Optimization.
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  3. Ott F, Rügamer D, Heublein L, Bischl B, Mutschler C (2021) Joint Classification and Trajectory Regression of Online Handwriting using a Multi-Task Learning Approach Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV),
  4. Goschenhofer J, Hvingelby R, Rügamer D, Thomas J, Wagner M, Bischl B (2021) Deep Semi-Supervised Learning for Time Series Classification 20th IEEE International Conference on Machine Learning and Applications (ICMLA),
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  5. Python A, Bender A, Blangiardo M et al. (2021) A Downscaling Approach to Compare COVID-19 Count Data from Databases Aggregated at Different Spatial Scales. Journal of the Royal Statistical Society: Series A (Statistics in Society).
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  6. Rügamer D, Baumann P, Greven S (2021) Selective Inference for Additive and Mixed Models. Computational Statistics and Data Analysis.
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  7. Bauer A, Klima A, Gauß J, Kümpel H, Bender A, Küchenhoff H (2021) Mundus Vult Decipi, Ergo Decipiatur: Visual Communication of Uncertainty in Election Polls. PS: Political Science & Politics, 1–7.
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  8. Fabritius MP, Seidensticker M, Rueckel J et al. (2021) Bi-Centric Independent Validation of Outcome Prediction after Radioembolization of Primary and Secondary Liver Cancer. Journal of Clinical Medicine 10, 3668.
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  9. Pfisterer F, Kern C, Dandl S, Sun M, Kim MP, Bischl B (2021) mcboost: Multi-Calibration Boosting for R. Journal of Open Source Software 6, 3453.
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  10. Bothmann L, Strickroth S, Casalicchio G et al. (2021) Developing Open Source Educational Resources for Machine Learning and Data Science. arXiv:2107.14330 [cs, stat].
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  11. Falla D, Devecchi V, Jimenez-Grande D, Rügamer D, Liew B (2021) Modern Machine Learning Approaches Applied in Spinal Pain Research. Journal of Electromyography and Kinesiology.
  12. *Coors S, *Schalk D, Bischl B, Rügamer D (2021) Automatic Componentwise Boosting: An Interpretable AutoML System. ECML-PKDD Workshop on Automating Data Science.
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  13. Bischl B, Binder M, Lang M et al. (2021) Hyperparameter Optimization: Foundations, Algorithms, Best Practices and Open Challenges. arXiv preprint arXiv:2107.05847.
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  14. Berninger C, Stöcker A, Rügamer D (2021) A Bayesian Time-Varying Autoregressive Model for Improved Short- and Long-Term Prediction. Journal of Forecasting.
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  15. Python A, Bender A, Nandi AK et al. (2021) Predicting non-state terrorism worldwide. Science Advances 7, eabg4778.
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  16. Baumann PFM, Hothorn T, Rügamer D (2021) Deep Conditional Transformation Models. Accepted at ECML-PKDD 2021.
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  17. König G, Freiesleben T, Bischl B, Casalicchio G, Grosse-Wentrup M (2021) Decomposition of Global Feature Importance into Direct and Associative Components (DEDACT).
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  18. Ramjith J, Bender A, Roes KCB, Jonker MA (2021) Recurrent Events Analysis with Piece-wise exponential Additive Mixed Models. Research Square.
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  19. Pfisterer F, Rijn JN van, Probst P, Müller A, Bischl B (2021) Learning Multiple Defaults for Machine Learning Algorithms. 2021 Genetic and Evolutionary Computation Conference Companion (GECCO ’21 Companion).
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  20. Gijsbers P, Pfisterer F, Rijn JN van, Bischl B, Vanschoren J (2021) Meta-Learning for Symbolic Hyperparameter Defaults. 2021 Genetic and Evolutionary Computation Conference Companion (GECCO ’21 Companion).
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  21. Rath K, Albert CG, Bischl B, Toussaint U von (2021) Symplectic Gaussian process regression of maps in Hamiltonian systems. Chaos: An Interdisciplinary Journal of Nonlinear Science 31, 053121.
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  22. Kopper P, Pölsterl S, Wachinger C, Bischl B, Bender A, Rügamer D (2021) Semi-Structured Deep Piecewise Exponential Models. In: In: Greiner R , In: Kumar N , In: Gerds TA , In: Schaar M van der (eds) Proceedings of AAAI Spring Symposium on Survival Prediction - Algorithms, Challenges, and Applications 2021, pp. 40–53. PMLR.
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  23. König G, Molnar C, Bischl B, Grosse-Wentrup M (2021) Relative Feature Importance 2020 25th International Conference on Pattern Recognition (ICPR), pp. 9318–9325.
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  24. Gerostathopoulos I, Plášil F, Prehofer C, Thomas J, Bischl B (2021) Automated Online Experiment-Driven Adaptation–Mechanics and Cost Aspects. IEEE Access 9, 58079–58087.
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  25. Rügamer D, Shen R, Bukas C et al. (2021) deepregression: a Flexible Neural Network Framework for Semi-Structured Deep Distributional Regression. arXiv:2104.02705 [stat].
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  26. Pargent F, Pfisterer F, Thomas J, Bischl B (2021) Regularized target encoding outperforms traditional methods in supervised machine learning with high cardinality features. arXiv preprint arXiv:2104.00629.
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  27. Hilbert S, Coors S, Kraus EB et al. (2021) Machine Learning for the Educational Sciences.
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  28. Liew B, Lee HY, Rügamer D et al. (2021) A novel metric of reliability in pressure pain threshold measurement. Scientific Reports (Nature).
  29. Küchenhoff H, Günther F, Höhle M, Bender A (2021) Analysis of the early COVID-19 epidemic curve in Germany by regression models with change points. Epidemiology & Infection, 1–17.
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  30. Bender A, Rügamer D, Scheipl F, Bischl B (2021) A General Machine Learning Framework for Survival Analysis. In: In: Hutter F , In: Kersting K , In: Lijffijt J , In: Valera I (eds) Machine Learning and Knowledge Discovery in Databases, pp. 158–173. Springer International Publishing.
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  31. Sonabend R, Király FJ, Bender A, Bischl B, Lang M (2021) mlr3proba: An R Package for Machine Learning in Survival Analysis. Bioinformatics.
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  32. Agrawal A, Pfisterer F, Bischl B et al. (2021) Debiasing classifiers: is reality at variance with expectation? Available at SSRN 3711681.
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  33. Fritz C, Dorigatti E, Rügamer D (2021) Combining Graph Neural Networks and Spatio-temporal Disease Models to Predict COVID-19 Cases in Germany. arXiv:2101.00661 [cs, stat].
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  34. Pfisterer F, Schneider L, Moosbauer J, Binder M, Bischl B (2021) YAHPO Gym - Design Criteria and a new Multifidelity Benchmark for Hyperparameter Optimization. arXiv:2109.03670 [cs, stat].
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  35. Binder M, Pfisterer F, Lang M, Schneider L, Kotthoff L, Bischl B (2021) mlr3pipelines - Flexible Machine Learning Pipelines in R. Journal of Machine Learning Research 22, 1–7.
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  36. Schneider L, Pfisterer F, Binder M, Bischl B (2021) Mutation is all you need 8th ICML Workshop on Automated Machine Learning,
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  37. Becker M, Binder M, Bischl B et al. (2021) mlr3 book.
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  38. Au Q, Herbinger J, Stachl C, Bischl B, Casalicchio G (2021) Grouped Feature Importance and Combined Features Effect Plot. arXiv preprint arXiv:2104.11688.
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2020

  1. Günther F, Bender A, Katz K, Küchenhoff H, Höhle M (2020) Nowcasting the COVID-19 pandemic in Bavaria. Biometrical Journal.
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  2. 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.
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  3. Rügamer D, Pfisterer F, Bischl B (2020) Neural Mixture Distributional Regression. arXiv:2010.06889 [cs, stat].
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  4. Guenther F, Bender A, Höhle M, Wildner M, Küchenhoff H (2020) Analysis of the COVID-19 pandemic in Bavaria: adjusting for misclassification. medRxiv, 2020.09.29.20203877.
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  5. 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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  6. Schratz P, Muenchow J, Iturritxa E, Cortés J, Bischl B, Brenning A (2020) Monitoring forest health using hyperspectral imagery: Does feature selection improve the performance of machine-learning techniques?
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  7. 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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  8. Binder M, Pfisterer F, Bischl B (2020) Collecting Empirical Data About Hyperparameters for Data Driven AutoML AutoML Workshop at ICML 2020,
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  9. Binder M, Moosbauer J, Thomas J, Bischl B (2020) Multi-Objective Hyperparameter Tuning and Feature Selection Using Filter Ensembles Proceedings of the 2020 Genetic and Evolutionary Computation Conference, pp. 471–479. Association for Computing Machinery, New York, NY, USA.
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  10. Beggel L, Pfeiffer M, Bischl B (2020) Robust Anomaly Detection in Images using Adversarial Autoencoders Joint European Conference on Machine Learning and Knowledge Discovery in Databases, pp. 206–222. Springer.
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  11. Dorigatti E, Schubert B (2020) Joint epitope selection and spacer design for string-of-beads vaccines. bioRxiv.
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  12. Pfister FMJ, Um TT, Pichler DC et al. (2020) High-Resolution Motor State Detection in parkinson’s Disease Using convolutional neural networks. Scientific reports 10, 1–11.
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  13. 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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  14. Goerigk S, Hilbert S, Jobst A et al. (2020) Predicting instructed simulation and dissimulation when screening for depressive symptoms. European Archives of Psychiatry and Clinical Neuroscience 270, 153–168.
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  15. Rügamer D, Kolb C, Klein N (2020) A Unified Network Architecture for Semi-Structured Deep Distributional Regression. arXiv:2002.05777 [cs, stat].
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  16. 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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  17. Molnar C, König G, Herbinger J et al. (2020) Pitfalls to Avoid when Interpreting Machine Learning Models. ICML workshop XXAI: Extending Explainable AI Beyond Deep Models and Classifiers.
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  18. 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, pp. 193–204. Springer International Publishing, Cham. link | pdf.
  19. Molnar C, Casalicchio G, Bischl B (2020) Interpretable Machine Learning–A Brief History, State-of-the-Art and Challenges. arXiv preprint arXiv:2010.09337.
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  20. 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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  21. 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.
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  22. Sun X, Bommert A, Pfisterer F, Rähenfürher J, Lang M, Bischl B (2020) High Dimensional Restrictive Federated Model Selection with Multi-objective Bayesian Optimization over Shifted Distributions. In: In: Bi Y , In: Bhatia R , In: Kapoor S (eds) Intelligent Systems and Applications, pp. 629–647. Springer International Publishing, Cham.
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  23. Rügamer D, Greven S (2020) Inference for L2-Boosting. Statistics and Computing 30, 279–289.
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  24. 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.
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  25. 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.
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  26. 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.
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  27. 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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  28. Brockhaus S, Rügamer D, Greven S (2020) Boosting Functional Regression Models with FDboost. Journal of Statistical Software 94, 1–50.
  29. Dorigatti E, Schubert B (2020) Graph-theoretical formulation of the generalized epitope-based vaccine design problem (RD Kouyos, Ed.). PLOS Computational Biology 16, e1008237.
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2019

  1. 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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  2. Sun X, Bischl B (2019) Tutorial and Survey on Probabilistic Graphical Model and Variational Inference in Deep Reinforcement Learning 2019 IEEE Symposium Series on Computational Intelligence (SSCI),
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  3. Pfisterer F, Thomas J, Bischl B (2019) Towards Human Centered AutoML. arXiv preprint arXiv:1911.02391.
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  4. Pfisterer F, Beggel L, Sun X, Scheipl F, Bischl B (2019) Benchmarking time series classification – Functional data vs machine learning approaches. arXiv preprint arXiv:1911.07511.
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  5. Schmid M, Bischl B, Kestler HA (2019) Proceedings of Reisensburg 2016–2017.
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  6. Beggel L, Kausler BX, Schiegg M, Pfeiffer M, Bischl B (2019) Time series anomaly detection based on shapelet learning. Computational Statistics 34, 945–976.
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  7. Pfisterer F, Coors S, Thomas J, Bischl B (2019) Multi-Objective Automatic Machine Learning with AutoxgboostMC. arXiv preprint arXiv:1908.10796.
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  8. 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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  9. 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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  10. Probst P, Boulesteix A-L, Bischl B (2019) Tunability: Importance of Hyperparameters of Machine Learning Algorithms. Journal of Machine Learning Research 20, 1–32.
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  11. 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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  12. Völkel ST, Schödel R, Buschek D et al. (2019) Opportunities and challenges of utilizing personality traits for personalization in HCI. Personalized Human-Computer Interaction, 31–65.
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  13. 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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  14. 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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  15. 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.
  16. 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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  17. 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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  18. Pargent F, Bischl B, Thomas J (2019) A Benchmark Experiment on How to Encode Categorical Features in Predictive Modeling.
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  19. Stachl C, Au Q, Schoedel R et al. (2019) Behavioral Patterns in Smartphone Usage Predict Big Five Personality Traits.
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  20. 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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  21. König G, Grosse-Wentrup M (2019) A Causal Perspective on Challenges for AI in Precision Medicine.
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  22. Goschenhofer J, Pfister FMJ, Yuksel KA, Bischl B, Fietzek U, Thomas J (2019) Wearable-based Parkinson’s Disease Severity Monitoring using Deep Learning. Machine Learning and Knowledge Discovery in Databases, ECML PKDD 2019, 400–415.
  23. Sun X, Gossmann A, Wang Y, Bischt B (2019) Variational Resampling Based Assessment of Deep Neural Networks under Distribution Shift 2019 IEEE Symposium Series on Computational Intelligence (SSCI), pp. 1344–1353.
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2018

  1. Arenas D, Barp E, Bohner G et al. (2018) Workshop contribution MLJ.
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  2. Molnar C, Casalicchio G, Bischl B (2018) iml: An R package for Interpretable Machine Learning. The Journal of Open Source Software 3, 786. link | pdf.
  3. Kestler HA, Bischl B, Schmid M (2018) Proceedings of Reisensburg 2014–2015.
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  4. Fossati M, Dorigatti E, Giuliano C (2018) N-ary relation extraction for simultaneous T-Box and A-Box knowledge base augmentation (PE Cimiano, Ed.). Semantic Web 9, 413–439.
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  5. Thomas J, Hepp T, Mayr A, Bischl B (2018) Probing for sparse and fast variable selection with model-based boosting.
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  6. 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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  7. 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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  8. Thomas J, Coors S, Bischl B (2018) Automatic Gradient Boosting. ICML AutoML Workshop.
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  9. Schalk D, Thomas J, Bischl B (2018) compboost: Modular Framework for Component-Wise Boosting. JOSS 3, 967.
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  10. Horn D, Demircioğlu A, Bischl B, Glasmachers T, Weihs C (2018) A Comparative Study on Large Scale Kernelized Support Vector Machines. Advances in Data Analysis and Classification, 1–17.
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  11. Schoedel R, Au Q, Völkel ST et al. (2018) Digital Footprints of Sensation Seeking. Zeitschrift für Psychologie 226, 232–245.
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  12. 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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  13. Rügamer D, Greven S (2018) Selective inference after likelihood-or test-based model selection in linear models. Statistics & Probability Letters 140, 7–12.
  14. Rijn JN van, Pfisterer F, Thomas J, Bischl B, Vanschoren J (2018) Meta Learning for Defaults–Symbolic Defaults Neurips 2018 Workshop on Meta Learning,
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  15. Burdukiewicz M, Karas M, Jessen LE, Kosinski M, Bischl B, Rödiger S (2018) Conference Report: Why R? 2018. The R Journal 10, 572–578.
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2017

  1. Bischl B, Casalicchio G, Feurer M et al. (2019) OpenML benchmarking suites and the OpenML100. arXiv preprint arXiv:1708.03731.
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  2. 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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  3. Cáceres LP, Bischl B, Stützle T (2017) Evaluating Random Forest Models for Irace Proceedings of the Genetic and Evolutionary Computation Conference Companion, pp. 1146–1153. Association for Computing Machinery.
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  4. 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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  5. 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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  6. 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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  7. 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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  8. Horn D, Dagge M, Sun X, Bischl B (2017) First Investigations on Noisy Model-Based Multi-objective Optimization 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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  9. Beggel L, Sun X, Bischl B (2017) mlrFDA: an R toolbox for functional data analysis. Ulmer Informatik-Berichte, 15.
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  10. Horn D, Bischl B, Demircioglu A, Glasmachers T, Wagner T, Weihs C (2017) Multi-objective selection of algorithm portfolios. Archives of Data Science.
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  11. 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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  12. 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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  13. Thomas J, Hepp T, Mayr A, Bischl B (2017) Probing for sparse and fast variable selection with model-based boosting.
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2016

  1. Richter J, Kotthaus H, Bischl B, Marwedel P, Rahnenführer J, Lang M (2019) 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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  2. Horn D, Bischl B (2016) Multi-objective Parameter Configuration of Machine Learning Algorithms using Model-Based Optimization 2016 IEEE Symposium Series on Computational Intelligence (SSCI), pp. 1–8. IEEE.
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  3. 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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  4. Bauer N, Friedrichs K, Bischl B, Weihs C (2016) Fast Model Based Optimization of Tone Onset Detection by Instance Sampling Data Analysis, Machine Learning and Knowledge Discovery,
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  5. 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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  6. Bischl B, Kerschke P, Kotthoff L et al. (2016) ASlib: A Benchmark Library for Algorithm Selection. Artificial Intelligence 237, 41–58.
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  7. 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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  8. Demircioglu A, Horn D, Glasmachers T, Bischl B, Weihs C (2016) Fast model selection by limiting SVM training times.
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  9. 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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  10. Degroote H, Bischl B, Kotthoff L, De Causmaecker P (2016) Reinforcement Learning for Automatic Online Algorithm Selection - an Empirical Study ITAT 2016 Proceedings, pp. 93–101. CEUR-WS.org.
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  11. Feilke M, Bischl B, Schmid VJ, Gertheiss J (2016) Boosting in nonlinear regression models with an application to DCE-MRI data. Methods of information in medicine 55, 31–41.
  12. Beggel L, Kausler BX, Schiegg M, Bischl B (2016) Anomaly Detection with Shapelet-Based Feature Learning for Time Series. Ulmer Informatik-Berichte, 25.
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  13. 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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  14. Schiffner J, Bischl B, Lang M et al. (2016) mlr Tutorial.
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  15. 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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  16. Feilke M, Bischl B, Schmid VJ, Gertheiss J (2016) Boosting in non-linear regression models with an application to DCE-MRI data. Methods of Information in Medicine.
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2015

  1. Vanschoren J, Rijn JN, Bischl B (2015) Taking machine learning research online with OpenML. In: In: Fan W , In: Bifet A , In: Yang Q , In: Yu PS (eds) Proceedings of the 4th International Workshop on Big Data, Streams and Heterogeneous Source Mining: Algorithms, Systems, Programming Models and Applications, pp. 1–4. PMLR.
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  2. Mantovani RG, Rossi ALD, Vanschoren J, Bischl B, Carvalho ACPLF (2015) To tune or not to tune: Recommending when to adjust SVM hyper-parameters via meta-learning 2015 International Joint Conference on Neural Networks (IJCNN), pp. 1–8.
    link|<a href"https://repositorio.unesp.br/bitstream/handle/11449/161236/WOS000370730602079.pdf?sequence=1">pdf</a>
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  3. Mantovani RG, Rossi ALD, Vanschoren J, Bischl B, Carvalho ACPLF de (2015) Effectiveness of Random Search in SVM hyper-parameter tuning 2015 International Joint Conference on Neural Networks (IJCNN), pp. 1–8.
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  4. 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. Association for Computing Machinery.
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  5. 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 Proceedings of the Companion Publication of the 2015 Annual Conference on Genetic and Evolutionary Computation, pp. 1159–1166. Association for Computing Machinery, Madrid, Spain.
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  6. 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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  7. 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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  8. 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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  9. 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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  10. Bischl B (2015) Applying Model-Based Optimization to Hyperparameter Optimization in Machine Learning Proceedings of the 2015 International Conference on Meta-Learning and Algorithm Selection - Volume 1455, p. 1. CEUR-WS.org, Aachen, DEU.
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  11. Vanschoren J, Rijn JN van, Bischl B, Casalicchio G (2015) OpenML: a networked science platform for machine learning 2015 ICML Workshop on Machine Learning Open Source Software (MLOSS 2015), pp. 1–3.
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  12. 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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  13. 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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  14. Mersmann O, Preuss M, Trautmann H, Bischl B, Weihs C (2015) Analyzing the BBOB Results by Means of Benchmarking Concepts. Evolutionary Computation Journal 23, 161–185.
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  15. Vanschoren J, Bischl B, Hutter F et al. (2015) Towards a data science collaboratory. Lecture Notes in Computer Science (IDA 2015) 9385.
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2014

  1. 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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  2. 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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  3. 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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  4. 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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  5. 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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  6. 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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2013

  1. 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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  2. 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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  3. Rijn J van, Bischl B, Torgo L et al. (2013) OpenML: A Collaborative Science Platform Machine Learning and Knowledge Discovery in Databases, pp. 645–649. Springer Berlin Heidelberg, Berlin, Heidelberg.
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  4. Bischl B, Schiffner J, Weihs C (2013) Benchmarking local classification methods. Computational Statistics 28, 2599–2619.
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  5. 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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  6. 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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  7. 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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  8. 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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2012

  1. Nallaperuma S, Wagner M, Neumann F, Bischl B, Mersmann O, Trautmann H (2012) Features of Easy and Hard Instances for Approximation Algorithms and the Traveling Salesperson Problem.
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  2. Bischl B, Mersmann O, Trautmann H, Preuss M (2012) Algorithm Selection Based on Exploratory Landscape Analysis and Cost-Sensitive Learning Proceedings of the 14th Annual Conference on Genetic and Evolutionary Computation, pp. 313–320.
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  3. 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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  4. 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), pp. 115–129. Springer Berlin Heidelberg, Berlin, Heidelberg.
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  5. 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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  6. Weihs C, O. M, Bischl B et al. (2012) A Case Study on the Use of Statistical Classification Methods in Particle Physics Challenges at the Interface of Data Analysis, Computer Science, and Optimization, pp. 69–77. Springer Berlin Heidelberg, Berlin, Heidelberg.
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  7. 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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  8. 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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2011

  1. 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. Association for Computing Machinery, New York, NY, USA.
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  2. Blume H, Bischl B, Botteck M et al. (2011) . IEEE Signal Processing Magazine 28, 24–39.
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  3. Weihs C, Friedrichs K, Bischl B (2011) Statistics for hearing aids: Auralization Second Bilateral German-Polish Symposium on Data Analysis and its Applications (GPSDAA),
  4. 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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2010

  1. Bischl B, Vatolkin I, Preuss M (2010) Selecting Small Audio Feature Sets in Music Classification by Means of Asymmetric Mutation Parallel Problem Solving from Nature, PPSN XI, pp. 314–323. Springer.
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  2. Szepannek G, Gruhne M, Bischl B et al. (201AD) 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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  3. 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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  4. 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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2009

  1. Bischl B, Ligges U, Weihs C (6AD) Frequency estimation by DFT interpolation: A comparison of methods. SFB 475, Faculty of Statistics, TU Dortmund, Germany
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  2. Szepannek G, Bischl B, Weihs C (2009) On the combination of locally optimal pairwise classifiers. Engineering Applications of Artificial Intelligence 22, 79–85.
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2008

  1. Szepannek G, Bischl B, Weihs C (2008) On the Combination of Locally Optimal Pairwise Classifiers. Journal of Engineering Applications of Artificial Intelligence 22, 79–85.
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2007