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. Weerts H, Pfisterer F, Feurer M, Eggensperger K, Bergman E, Awad N, Vanschoren J, Pechenizkiy M, Bischl B, Hutter F (2024) Can Fairness be Automated? Guidelines and Opportunities for Fairness-aware AutoML. Journal of Artificial Intelligence Research 79, 639–677.
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  2. Liew BXW, Pfisterer F, Rügamer D, Zhai X (2024) Strategies to optimise machine learning classification performance when using biomechanical features. Journal of Biomechanics, 111998.
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  3. Sommer E, Wimmer L, Papamarkou T, Bothmann L, Bischl B, Rügamer D (2024) Connecting the Dots: Is Mode Connectedness the Key to Feasible Sample-Based Inference in Bayesian Neural Networks?
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  4. Papamarkou T, Skoularidou M, Palla K, Aitchison L, Arbel J, Dunson D, Filippone M, Fortuin V, Hennig P, Hubin A, Immer A, Karaletsos T, Khan ME, Kristiadi A, Li Y, Lobato JMH, Mandt S, Nemeth C, Osborne MA, Rudner TGJ, Rügamer D, Teh YW, Welling M, Wilson AG, Zhang R (2024) Position Paper: Bayesian Deep Learning in the Age of Large-Scale AI.
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  5. Dandl S, Haslinger C, Hothorn T, Seibold H, Sverdrup E, Wager S, Zeileis A (2024) What Makes Forest-Based Heterogeneous Treatment Effect Estimators Work? The Annals of Applied Statistics 18, 506–528.
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  6. Bothmann L, Peters K, Bischl B (2024) What Is Fairness? On the Role of Protected Attributes and Fictitious Worlds. arXiv:2205.09622 [cs, stat].
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  7. Rügamer D (2024) Scalable Higher-Order Tensor Product Spline Models Proceedings of The 27th International Conference on Artificial Intelligence and Statistics, PMLR.
  8. Dold D, Rügamer D, Sick B, Dürr O (2024) Semi-Structured Subspace Inference Proceedings of The 27th International Conference on Artificial Intelligence and Statistics, PMLR.
  9. Schalk D, Bischl B, Rügamer D (2024) Privacy-Preserving and Lossless Distributed Estimation of High-Dimensional Generalized Additive Mixed Models. Statistics & Computing 34.
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  10. Weber T, Ingrisch M, Bischl B, Rügamer D (2024) Constrained Probabilistic Mask Learning for Task-specific Undersampled MRI Reconstruction Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV),
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  11. Gruber C, Hechinger K, Aßenmacher M, Kauermann G, Plank B (2024) More Labels or Cases? Assessing Label Variation in Natural Language Inference The Third Workshop on Understanding Implicit and Underspecified Language,
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  12. Herbinger J, Dandl S, Ewald FK, Loibl S, Casalicchio G (2024) Leveraging Model-Based Trees as Interpretable Surrogate Models for Model Distillation. In: In: Nowaczyk S , In: Biecek P , In: Chung NC , In: Vallati M , In: Skruch P , In: Jaworek-Korjakowska J , In: Parkinson S , In: Nikitas A , In: Atzmüller M , In: Kliegr T , In: Schmid U , In: Bobek S , In: Lavrac N , In: Peeters M , In: Dierendonck R van , In: Robben S , In: Mercier-Laurent E , In: Kayakutlu G , In: Owoc ML , In: Mason K , In: Wahid A , In: Bruno P , In: Calimeri F , In: Cauteruccio F , In: Terracina G , In: Wolter D , In: Leidner JL , In: Kohlhase M , In: Dimitrova V (eds) Artificial Intelligence. ECAI 2023 International Workshops, pp. 232–249. Springer Nature Switzerland, Cham.
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  13. Liew BXW, Rügamer D, Birn-Jeffery A (2023) Neuromechanical stabilisation of the centre of mass during running. Gait & Posture.
  14. Weber T, Ingrisch M, Bischl B, Rügamer D (2023) Unreading Race: Purging Protected Features from Chest X-ray Embeddings. arXiv:2311.01349.
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  15. Bothmann L, Dandl S, Schomaker M (2023) Causal Fair Machine Learning via Rank-Preserving Interventional Distributions Proceedings of the 1st Workshop on Fairness and Bias in AI co-located with 26th European Conference on Artificial Intelligence (ECAI 2023), CEUR Workshop Proceedings.
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  16. Rügamer D, Pfisterer F, Bischl B, Grün B (2023) Mixture of Experts Distributional Regression: Implementation Using Robust Estimation with Adaptive First-order Methods. AStA Advances in Statistical Analysis.
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  17. Hornung R, Nalenz M, Schneider L, Bender A, Bothmann L, Bischl B, Augustin T, Boulesteix A-L (2023) Evaluating Machine Learning Models in Non-Standard Settings: An Overview and New Findings. arXiv:2310.15108 [cs, stat].
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  18. Zhang Z, Yang H, Ma B, Rügamer D, Nie E (2023) Baby’s CoThought: Leveraging Large Language Models for Enhanced Reasoning in Compact Models.
  19. Jeblick K, Schachtner B, Dexl J, Mittermeier A, Stüber AT, Topalis J, Weber T, Wesp P, Sabel B, Ricke J, Ingrisch M (2023) ChatGPT Makes Medicine Easy to Swallow: An Exploratory Case Study on Simplified Radiology Reports. European Radiology.
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  20. Liew BXW, Kovacs FM, Rügamer D, Royuela A (2023) Automatic Variable Selection Algorithms in Prognostic Factor Research in Neck Pain. Journal of Clinical Medicine 12.
  21. Ott F, Rügamer D, Heublein L, Bischl B, Mutschler C (2023) Auxiliary Cross-Modal Representation Learning With Triplet Loss Functions for Online Handwriting Recognition. IEEE Access 11, 94148–94172.
  22. Bothmann L, Wimmer L, Charrakh O, Weber T, Edelhoff H, Peters W, Nguyen H, Benjamin C, Menzel A (2023) Automated wildlife image classification: An active learning tool for ecological applications. Ecological Informatics 77.
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  23. Kolb C, Müller CL, Bischl B, Rügamer D (2023) Smoothing the Edges: A General Framework for Smooth Optimization in Sparse Regularization using Hadamard Overparametrization. arXiv preprint arXiv:2307.03571.
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  24. Liew BXW, Rügamer D, Mei Q, Altai Z, Zhu X, Zhai X, Cortes N (2023) Smooth and accurate predictions of joint contact force timeseries in gait using overparameterised deep neural networks. Frontiers in Bioengineering and Biotechnology: Biomechanics.
  25. Kolb C, Bischl B, Müller CL, Rügamer D (2023) Sparse Modality Regression Proceedings of the 37th International Workshop on Statistical Modelling, IWSM 2023,
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  26. Wiese JG, Wimmer L, Papamarkou T, Bischl B, Günnemann S, Rügamer D (2023) Towards Efficient Posterior Sampling in Deep Neural Networks via Symmetry Removal Machine Learning and Knowledge Discovery in Databases (ECML-PKDD), Springer International Publishing.
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  27. Rügamer D (2023) A New PHO-rmula for Improved Performance of Semi-Structured Networks. ICML 2023.
  28. Ott F, Heublein L, Rügamer D, Bischl B, Mutschler C (2023) Fusing Structure from Motion and Simulation-Augmented Pose Regression from Optical Flow for Challenging Indoor Environments. arXiv:2304.07250.
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  29. Rath K, Rügamer D, Bischl B, Toussaint U von, Albert C (2023) Dependent state space Student-t processes for imputation and data augmentation in plasma diagnostics. Contributions to Plasma Physics.
  30. Weber T, Ingrisch M, Bischl B, Rügamer D (2023) Cascaded Latent Diffusion Models for High-Resolution Chest X-ray Synthesis Advances in Knowledge Discovery and Data Mining: 27th Pacific-Asia Conference, PAKDD 2023,
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  31. Ott F, Raichur NL, Rügamer D, Feigl T, Neumann H, Bischl B, Mutschler C (2023) Benchmarking Visual-Inertial Deep Multimodal Fusion for Relative Pose Regression and Odometry-aided Absolute Pose Regression. arXiv:2208.00919.
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  32. Weber T, Ingrisch M, Bischl B, Rügamer D (2023) Implicit Embeddings via GAN Inversion for High Resolution Chest Radiographs. MICCAI Workshop on Medical Applications with Disentanglements 2022.
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  33. Dorigatti E, Bischl B, Rügamer D (2023) Frequentist Uncertainty Quantification in Semi-Structured Neural Networks International Conference on Artificial Intelligence and Statistics, PMLR.
  34. Pielok T, Bischl B, Rügamer D (2023) Approximate Bayesian Inference with Stein Functional Variational Gradient Descent International Conference on Learning Representations,
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  35. Wimmer L, Sale Y, Hofman P, Bischl B, Hüllermeier E (2023) Quantifying Aleatoric and Epistemic Uncertainty in Machine Learning: Are Conditional Entropy and Mutual Information Appropriate Measures? 39th Conference on Uncertainty in Artificial Intelligence (UAI 2023),
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  36. Gertheiss J, Rügamer D, Liew B, Greven S (2023) Functional Data Analysis: An Introduction and Recent Developments.
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  37. Hartl WH, Kopper P, Xu L, Heller L, Mironov M, Wang R, Day AG, Elke G, Küchenhoff H, Bender A (2023) Relevance of Protein Intake for Weaning in the Mechanically Ventilated Critically Ill: Analysis of a Large International Database. Critical Care Medicine.
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  38. Hendrix P, Sun CC, Brighton H, Bender A (2023) On the Connection Between Language Change and Language Processing. Cognitive Science 47, e13384.
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  39. Coens F, Knops N, Tieken I, Vogelaar S, Bender A, Kim JJ, Krupka K, Pape L, Raes A, Tönshoff B, Prytula A, Registry C (2023) Time-Varying Determinants of Graft Failure in Pediatric Kidney Transplantation in Europe. Clinical Journal of the American Society of Nephrology.
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  40. Wiegrebe S, Kopper P, Sonabend R, Bischl B, Bender A (2023) Deep Learning for Survival Analysis: A Review.
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  41. Garces Arias E, Pai V, Schöffel M, Heumann C, Aßenmacher M (2023) Automatic Transcription of Handwritten Old Occitan Language Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing, pp. 15416–15439. Association for Computational Linguistics, Singapore.
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  42. Öztürk IT, Nedelchev R, Heumann C, Garces Arias E, Roger M, Bischl B, Aßenmacher M (2023) How Different Is Stereotypical Bias Across Languages? 3rd Workshop on Bias and Fairness in AI (co-located with ECML-PKDD 2023),
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  43. Witte M, Schwenzow J, Heitmann M, Reisenbichler M, Aßenmacher M (2023) Potential for Decision Aids based on Natural Language Processing Proceedings of the European Marketing Academy, 52nd, (114322),
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  44. Aßenmacher M, Rauch L, Goschenhofer J, Stephan A, Bischl B, Roth B, Sick B (2023) Towards Enhancing Deep Active Learning with Weak Supervision and Constrained Clustering Proceedings of the 7th Workshop on Interactive Adaptive Learning (co-located with ECML-PKDD 2023),
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  45. Aßenmacher M, Sauter N, Heumann C (2023) Classifying multilingual party manifestos: Domain transfer across country, time, and genre. arXiv preprint arXiv:2307.16511.
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  46. Akkus C, Chu L, Djakovic V, Jauch-Walser S, Koch P, Loss G, Marquardt C, Moldovan M, Sauter N, Schneider M, Schulte R, Urbanczyk K, Goschenhofer J, Heumann C, Hvingelby R, Schalk D, Aßenmacher M (2023) Multimodal Deep Learning. arXiv preprint arXiv:2301.04856.
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  47. Bischl B, Binder M, Lang M, Pielok T, Richter J, Coors S, Thomas J, Ullmann T, Becker M, Boulesteix A-L, Deng D, Lindauer M (2023) Hyperparameter Optimization: Foundations, Algorithms, Best Practices, and Open Challenges. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, e1484.
  48. Deiseroth B, Meuer M, Gritsch N, Eichenberg C, Schramowski P, Aßenmacher M, Kersting K (2023) Divergent Token Metrics: Measuring degradation to prune away LLM components – and optimize quantization. arXiv preprint arXiv:2311.01544.
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  49. Gündüz HA, Binder M, To X-Y, Mreches R, Bischl B, McHardy AC, Münch PC, Rezaei M (2023) A self-supervised deep learning method for data-efficient training in genomics. Communications Biology 6, 928.
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  50. Garces Arias E, Pai V, Schöffel M, Heumann C, Aßenmacher M (2023) Automatic transcription of handwritten Old Occitan language Accepted at EMNLP 2023,
  51. König G, Freiesleben T, Grosse-Wentrup M (2023) Improvement-focused Causal Recourse (ICR) 37th AAAI Conference,
  52. Koch P, Nuñez GV, Garces Arias E, Heumann C, Schöffel M, Häberlin A, Aßenmacher M (2023) A tailored Handwritten-Text-Recognition System for Medieval Latin First Workshop on Ancient Language Processing (ALP 2023),
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  53. Luther C, König G, Grosse-Wentrup M (2023) Efficient SAGE Estimation via Causal Structure Learning AISTATS,
  54. Münch P, Mreches R, To X-Y, Gündüz HA, Moosbauer J, Klawitter S, Deng Z-L, Robertson G, Rezaei M, Asgari E, Franzosa E, Huttenhower C, Bischl B, McHardy A, Binder M (2023) A platform for deep learning on (meta)genomic sequences (preprint).
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  55. Feurer M, Eggensperger K, Bergman E, Pfisterer F, Bischl B, Hutter F (2023) Mind the Gap: Measuring Generalization Performance Across Multiple Objectives. In: In: Crémilleux B , In: Hess S , In: Nijssen S (eds) Advances in Intelligent Data Analysis XXI. IDA 2023., pp. 130–142. Springer, Cham.
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  56. Prager RP, Dietrich K, Schneider L, Schäpermeier L, Bischl B, Kerschke P, Trautmann H, Mersmann O (2023) Neural Networks as Black-Box Benchmark Functions Optimized for Exploratory Landscape Features Proceedings of the 17th ACM/SIGEVO Conference on Foundations of Genetic Algorithms, pp. 129–139.
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  57. Purucker L, Schneider L, Anastacio M, Beel J, Bischl B, Hoos H (2023) Q(D)O-ES: Population-based Quality (Diversity) Optimisation for Post Hoc Ensemble Selection in AutoML AutoML Conference 2023,
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  58. Rauch L, Aßenmacher M, Huseljic D, Wirth M, Bischl B, Sick B (2023) ActiveGLAE: A Benchmark for Deep Active Learning with Transformers ECML-PKDD 2023,
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  59. Scheppach A, Gündüz HA, Dorigatti E, Münch PC, McHardy AC, Bischl B, Rezaei M, Binder M (2023) Neural Architecture Search for Genomic Sequence Data 2023 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB), pp. 1–10.
  60. Schneider L, Bischl B, Thomas J (2023) Multi-Objective Optimization of Performance and Interpretability of Tabular Supervised Machine Learning Models Proceedings of the Genetic and Evolutionary Computation Conference, pp. 538–547.
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  61. Schulze P, Wiegrebe S, Thurner PW, Heumann C, Aßenmacher M, Wankmüller S (2023) Exploring Topic-Metadata Relationships with the STM: A Bayesian Approach. Accepted at Advances in Statistical Analysis (AStA).
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  62. Fischer S, Harutyunyan L, Feurer M, Bischl B (2023) OpenML-CTR23 – A curated tabular regression benchmarking suite AutoML Conference 2023 (Workshop),
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  63. Urchs S, Thurner V, Aßenmacher M, Heumann C, Thiemichen S (2023) How Prevalent is Gender Bias in ChatGPT? - Exploring German and English ChatGPT Responses 1st Workshop on Biased Data in Conversational Agents (co-located with ECML-PKDD 2023),
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  64. Vahidi A, Wimmer L, Gündüz HA, Bischl B, Hüllermeier E, Rezaei M (2023) Diversified Ensemble of Independent Sub-Networks for Robust Self-Supervised Representation Learning. arXiv preprint arXiv:2308.14705.
  65. Vogel M, Aßenmacher M, Gubler A, Attin T, Schmidlin PR (2023) Cleaning potential of interdental brushes around orthodontic brackets-an in vitro investigation. Swiss Dental Journal 133.
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  66. Karl F, Pielok T, Moosbauer J, Pfisterer F, Coors S, Binder M, Schneider L, Thomas J, Richter J, Lang M, Garrido-Merchán EC, Branke J, Bischl B (2023) Multi-Objective Hyperparameter Optimization in Machine Learning – An Overview. ACM Transactions on Evolutionary Learning and Optimization 3, 1–50.
  67. Dandl S, Casalicchio G, Bischl B, Bothmann L (2023) Interpretable Regional Descriptors: Hyperbox-Based Local Explanations. In: In: Koutra D , In: Plant C , In: Gomez Rodriguez M , In: Baralis E , In: Bonchi F (eds) ECML PKDD 2023: Machine Learning and Knowledge Discovery in Databases: Research Track, pp. 479–495. Springer Nature Switzerland, Cham.
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  68. Dandl S, Hofheinz A, Binder M, Bischl B, Casalicchio G (2023) counterfactuals: An R Package for Counterfactual Explanation Methods.
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  69. Molnar C, König G, Bischl B, Casalicchio G (2023) Model-agnostic Feature Importance and Effects with Dependent Features–A Conditional Subgroup Approach. Data Mining and Knowledge Discovery.
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  70. Scholbeck CA, Funk H, Casalicchio G (2023) Algorithm-Agnostic Feature Attributions for Clustering. In: In: Longo L (ed) Explainable Artificial Intelligence, pp. 217–240. Springer Nature Switzerland, Cham.
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  71. Molnar C, Freiesleben T, König G, Herbinger J, Reisinger T, Casalicchio G, Wright MN, Bischl B (2023) Relating the Partial Dependence Plot and Permutation Feature Importance to the Data Generating Process. In: In: Longo L (ed) Explainable Artificial Intelligence, pp. 456–479. Springer Nature Switzerland, Cham.
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  72. Herbinger J, Bischl B, Casalicchio G (2023) Decomposing Global Feature Effects Based on Feature Interactions. arXiv preprint arXiv:2306.00541.
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  73. Löwe H, Scholbeck CA, Heumann C, Bischl B, Casalicchio G (2023) fmeffects: An R Package for Forward Marginal Effects. arXiv preprint arXiv:2310.02008.
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  74. Scholbeck CA, Moosbauer J, Casalicchio G, Gupta H, Bischl B, Heumann C (2023) Position Paper: Bridging the Gap Between Machine Learning and Sensitivity Analysis. arXiv preprint arXiv:2312.13234.
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  75. Dorigatti E, Bischl B, Schubert B (2022) Improved proteasomal cleavage prediction with positive-unlabeled learning. Extended Abstract presented at Machine Learning for Health (ML4H) symposium 2022, November 28th, 2022, New Orleans, United States & Virtual.
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  76. Kook L, Baumann PFM, Dürr O, Sick B, Rügamer D (2022) Estimating Conditional Distributions with Neural Networks using R package deeptrafo. arXiv preprint arXiv:2211.13665.
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  77. Rügamer D, Baumann PFM, Kneib T, Hothorn T (2022) Probabilistic Time Series Forecasts with Autoregressive Transformation Models. Statistics & Computing.
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  78. Ziegler I, Ma B, Nie E, Bischl B, Rügamer D, Schubert B, Dorigatti E (2022) What cleaves? Is proteasomal cleavage prediction reaching a ceiling? Extended Abstract presented at the NeurIPS Learning Meaningful Representations of Life (LMRL) workshop 2022.
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  79. Ott F, Rügamer D, Heublein L, Bischl B, Mutschler C (2022) Representation Learning for Tablet and Paper Domain Adaptation in favor of Online Handwriting Recognition MPRSS 2022,
  80. Ziegler I, Ma B, Nie E, Bischl B, Rügamer D, Schubert B, Dorigatti E (2022) What cleaves? Is proteasomal cleavage prediction reaching a ceiling? NeurIPS 2022 Workshop on Learning Meaningful Representations of Life (LMRL),
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  81. Kaiser P, Rügamer D, Kern C (2022) Uncertainty as a key to fair data-driven decision making NeurIPS 2022 Workshop on Trustworthy and Socially Responsible Machine Learning (TSRML),
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  82. Rezaei M, Dorigatti E, Rügamer D, Bischl B (2022) Joint Debiased Representation Learning and Imbalanced Data Clustering arXiv preprint arXiv:2109.05232,
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  83. Bothmann L (2022) Künstliche Intelligenz in der Strafverfolgung. In: In: Peters K (ed) Cyberkriminalität, LMU Munich, Munich.
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  84. Ghada W, Casellas E, Herbinger J, Garcia-Benadí A, Bothmann L, Estrella N, Bech J, Menzel A (2022) Stratiform and Convective Rain Classification Using Machine Learning Models and Micro Rain Radar. Remote Sensing 14.
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  85. Ramjith J, Bender A, Roes KCB, Jonker MA (2022) Recurrent Events Analysis with Piece-Wise Exponential Additive Mixed Models. Statistical Modelling, 1471082X221117612.
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  86. Ott F, Rügamer D, Heublein L, Hamann T, Barth J, Bischl B, Mutschler C (2022) Benchmarking Online Sequence-to-Sequence and Character-based Handwriting Recognition from IMU-Enhanced Pens. International Journal on Document Analysis and Recognition (IJDAR).
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  87. Schiele P, Berninger C, Rügamer D (2022) ARMA Cell: A Modular and Effective Approach for Neural Autoregressive Modeling. arXiv preprint arXiv:2208.14919.
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  88. Schalk D, Bischl B, Rügamer D (2022) Accelerated Componentwise Gradient Boosting using Efficient Data Representation and Momentum-based Optimization. Journal of Computational and Graphical Statistics.
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  89. Rath K, Rügamer D, Bischl B, Toussaint U von, Rea C, Maris A, Granetz R, Albert C (2022) Data augmentation for disruption prediction via robust surrogate models. Journal of Plasma Physics.
  90. Dandl S, Pfisterer F, Bischl B (2022) Multi-Objective Counterfactual Fairness Proceedings of the Genetic and Evolutionary Computation Conference Companion, pp. 328–331. Association for Computing Machinery, New York, NY, USA.
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  91. Mittermeier M, Weigert M, Rügamer D, Küchenhoff H, Ludwig R (2022) A Deep Learning Version of Hess & Brezowskys Classification of Großwetterlagen over Europe: Projection of Future Changes in a CMIP6 Large Ensemble. Environmental Research Letters.
  92. Ott F, Rügamer D, Heublein L, Bischl B, Mutschler C (2022) Domain Adaptation for Time-Series Classification to Mitigate Covariate Shift ACM Multimedia,
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  93. Rügamer D, Bender A, Wiegrebe S, Racek D, Bischl B, Müller C, Stachl C (2022) Factorized Structured Regression for Large-Scale Varying Coefficient Models Machine Learning and Knowledge Discovery in Databases (ECML-PKDD), Springer International Publishing.
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  94. Beaudry G, Drouin O, Gravel J, Smyrnova A, Bender A, Orri M, Geoffroy M-C, Chadi N (2022) A Comparative Analysis of Pediatric Mental Health-Related Emergency Department Utilization in Montréal, Canada, before and during the COVID-19 Pandemic. Annals of General Psychiatry 21, 17.
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  95. Dandl S, Bender A, Hothorn T (2022) Heterogeneous Treatment Effect Estimation for Observational Data using Model-based Forests.
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  96. Klaß A, Lorenz S, Lauer-Schmaltz M, Rügamer D, Bischl B, Mutschler C, Ott F (2022) Uncertainty-aware Evaluation of Time-Series Classification for Online Handwriting Recognition with Domain Shift IJCAI-ECAI 2022, 1st International Workshop on Spatio-Temporal Reasoning and Learning,
  97. Fritz C, Nicola GD, Günther F, Rügamer D, Rave M, Schneble M, Bender A, Weigert M, Brinks R, Hoyer A, Berger U, Küchenhoff H, Kauermann G (2022) Challenges in Interpreting Epidemiological Surveillance Data - Experiences from Germany. Journal of Computational & Graphical Statistics.
  98. Rügamer D (2022) Additive Higher-Order Factorization Machines. arXiv preprint arXiv:2205.14515.
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  99. Rügamer D, Kolb C, Fritz C, Pfisterer F, Kopper P, Bischl B, Shen R, Bukas C, Sousa LB de Andrade e, Thalmeier D, Baumann P, Kook L, Klein N, Müller CL (2022) deepregression: a Flexible Neural Network Framework for Semi-Structured Deep Distributional Regression. Journal of Statistical Software (provisionally accepted).
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  100. Schalk D, Hoffmann V, Bischl B, Mansmann U (2022) Distributed non-disclosive validation of predictive models by a modified ROC-GLM. arXiv preprint arXiv:2202.10828.
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  101. Liew BXW, Kovacs FM, Rügamer D, Royuela A (2022) Machine learning for prognostic modelling in individuals with non-specific neck pain. European Spine Journal.
  102. Fritz C, Dorigatti E, Rügamer D (2022) Combining Graph Neural Networks and Spatio-temporal Disease Models to Predict COVID-19 Cases in Germany. Scientific Reports 12, 2045–2322.
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  103. Rügamer D, Baumann P, Greven S (2022) Selective Inference for Additive and Mixed Models. Computational Statistics and Data Analysis 167, 107350.
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  104. Ott F, Rügamer D, Heublein L, Bischl B, Mutschler C (2022) Cross-Modal Common Representation Learning with Triplet Loss Functions. arXiv preprint arXiv:2202.07901.
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  105. Dorigatti E, Goschenhofer J, Schubert B, Rezaei M, Bischl B (2022) Positive-Unlabeled Learning with Uncertainty-aware Pseudo-label Selection. arXiv preprint arXiv:2109.05232.
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  106. Kopper P, Wiegrebe S, Bischl B, Bender A, Rügamer D (2022) DeepPAMM: Deep Piecewise Exponential Additive Mixed Models for Complex Hazard Structures in Survival Analysis Advances in Knowledge Discovery and Data Mining, pp. 249–261. Springer International Publishing.
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  107. Hartl WH, Kopper P, Bender A, Scheipl F, Day AG, Elke G, Küchenhoff H (2022) Protein intake and outcome of critically ill patients: analysis of a large international database using piece-wise exponential additive mixed models. Critical Care 26, 7.
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  115. Hurmer N, To X-Y, Binder M, Gündüz HA, Münch PC, Mreches R, McHardy AC, Bischl B, Rezaei M (2022) Transformer Model for Genome Sequence Analysis LMRL Workshop - NeurIPS 2022,
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  120. Pargent F, Pfisterer F, Thomas J, Bischl B (2022) Regularized target encoding outperforms traditional methods in supervised machine learning with high cardinality features. Computational Statistics, 1–22.
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  122. Schneider L, Pfisterer F, Kent P, Branke J, Bischl B, Thomas J (2022) Tackling Neural Architecture Search With Quality Diversity Optimization International Conference on Automated Machine Learning, pp. 9–1. PMLR.
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  123. Schneider L, Pfisterer F, Thomas J, Bischl B (2022) A Collection of Quality Diversity Optimization Problems Derived from Hyperparameter Optimization of Machine Learning Models Proceedings of the Genetic and Evolutionary Computation Conference Companion, pp. 2136–2142.
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  124. Schneider L, Schäpermeier L, Prager RP, Bischl B, Trautmann H, Kerschke P (2022) HPO X ELA: Investigating Hyperparameter Optimization Landscapes by Means of Exploratory Landscape Analysis Parallel Problem Solving from Nature – PPSN XVII, pp. 575–589.
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  125. Sonabend R, Bender A, Vollmer S (2022) Avoiding C-hacking When Evaluating Survival Distribution Predictions with Discrimination Measures. Bioinformatics 38, 4178–4184.
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  126. Turkoglu MO, Becker A, Gündüz HA, Rezaei M, Bischl B, Daudt RC, D’Aronco S, Wegner JD, Schindler K (2022) FiLM-Ensemble: Probabilistic Deep Learning via Feature-wise Linear Modulation. Advances in Neural Information Processing Systems (NeurIPS 2022).
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  127. Au Q, Herbinger J, Stachl C, Bischl B, Casalicchio G (2022) Grouped Feature Importance and Combined Features Effect Plot. Data Mining and Knowledge Discovery 36, 1401–1450.
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  128. Bothmann L, Strickroth S, Casalicchio G, Rügamer D, Lindauer M, Scheipl F, Bischl B (2022) Developing Open Source Educational Resources for Machine Learning and Data Science. In: In: Kinnaird KM , In: Steinbach P , In: Guhr O (eds) Proceedings of the Third Teaching Machine Learning and Artificial Intelligence Workshop, pp. 1–6. PMLR.
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  129. Herbinger J, Bischl B, Casalicchio G (2022) REPID: Regional Effect Plots with implicit Interaction Detection. International Conference on Artificial Intelligence and Statistics (AISTATS) 25.
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  130. Moosbauer J, Casalicchio G, Lindauer M, Bischl B (2022) Enhancing Explainability of Hyperparameter Optimization via Bayesian Algorithm Execution. arXiv:2111.14756 [cs.LG].
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  131. Nießl C, Herrmann M, Wiedemann C, Casalicchio G, Boulesteix A-L (2022) Over-optimism in benchmark studies and the multiplicity of design and analysis options when interpreting their results. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery 12, e1441.
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  132. Scholbeck CA, Casalicchio G, Molnar C, Bischl B, Heumann C (2022) Marginal Effects for Non-Linear Prediction Functions. To Appear in Data Mining and Knowledge Discovery.
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  133. Molnar C, König G, Herbinger J, Freiesleben T, Dandl S, Scholbeck CA, Casalicchio G, Grosse-Wentrup M, Bischl B (2022) General Pitfalls of Model-Agnostic Interpretation Methods for Machine Learning Models. In: In: Holzinger A , In: Goebel R , In: Fong R , In: Moon T , In: Müller K-R , In: Samek W (eds) xxAI - Beyond Explainable AI: International Workshop, Held in Conjunction with ICML 2020, July 18, 2020, Vienna, Austria, Revised and Extended Papers, pp. 39–68. Springer International Publishing, Cham.
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  134. Hilbert S, Coors S, Kraus E, Bischl B, Lindl A, Frei M, Wild J, Krauss S, Goretzko D, Stachl C (2021) Machine learning for the educational sciences. Review of Education 9, e3310.
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  135. Liew BXW, Rügamer D, Duffy K, Taylor M, Jackson J (2021) The mechanical energetics of walking across the adult lifespan. PloS one 16, e0259817.
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  136. Mittermeier M, Weigert M, Rügamer D (2021) Identifying the atmospheric drivers of drought and heat using a smoothed deep learning approach. NeurIPS 2021, Tackling Climate Change with Machine Learning.
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  137. Weber T, Ingrisch M, Bischl B, Rügamer D (2021) Towards modelling hazard factors in unstructured data spaces using gradient-based latent interpolation. NeurIPS 2021 Workshops, Deep Generative Models and Downstream Applications.
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  138. Weber T, Ingrisch M, Fabritius M, Bischl B, Rügamer D (2021) Survival-oriented embeddings for improving accessibility to complex data structures. NeurIPS 2021 Workshops, Bridging the Gap: From Machine Learning Research to Clinical Practice.
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  139. 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.
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  142. Python A, Bender A, Blangiardo M, Illian JB, Lin Y, Liu B, Lucas TCD, Tan S, Wen Y, Svanidze D, Yin J (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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  143. 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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  144. Rezaei M, Soleymani F, Bischl B, Azizi S (2021) Deep Bregman Divergence for Contrastive Learning of Visual Representations. arXiv preprint arXiv:2109.07455.
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  146. Fabritius MP, Seidensticker M, Rueckel J, Heinze C, Pech M, Paprottka KJ, Paprottka PM, Topalis J, Bender A, Ricke J, Mittermeier A, Ingrisch M (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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  148. 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.
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  155. 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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  158. Liew B, Lee HY, Rügamer D, Nunzio AMD, Heneghan NR, Falla D, Evans DW (2021) A novel metric of reliability in pressure pain threshold measurement. Scientific Reports (Nature).
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  214. Pfister FMJ, Schumann A von, Bemetz J, Thomas J, Ceballos-Baumann A, Bischl B, Fietzek U (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.
  215. Gijsbers P, LeDell E, Thomas J, Poirier S, Bischl B, Vanschoren J (2019) An Open Source AutoML Benchmark. CoRR.
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  216. 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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  217. 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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  218. Völkel ST, Schödel R, Buschek D, Stachl C, Au Q, Bischl B, Bühner M, Hussmann H (2019) Opportunities and challenges of utilizing personality traits for personalization in HCI. Personalized Human-Computer Interaction, 31–65.
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  219. Sun X, Wang Y, Gossmann A, Bischl B (2019) Resampling-based Assessment of Robustness to Distribution Shift for Deep Neural Networks. CoRR.
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  220. Lang M, Binder M, Richter J, Schratz P, Pfisterer F, Coors S, Au Q, Casalicchio G, Kotthoff L, Bischl B (2019) mlr3: A modern object-oriented machine learning framework in R. Journal of Open Source Software 4, 1903.
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  221. 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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  222. Arenas D, Barp E, Bohner G, Churvay V, Kiraly F, Lienart T, Vollmer S, Innes M, Bischl B (2018) Workshop contribution MLJ.
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  223. 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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  224. Kestler HA, Bischl B, Schmid M (2018) Proceedings of Reisensburg 2014–2015.
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  225. Bender A, Scheipl F (2018) pammtools: Piece-wise exponential Additive Mixed Modeling tools. arXiv:1806.01042 [stat].
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  226. 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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  227. 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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  228. 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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  229. Rügamer D, Greven S (2018) Selective inference after likelihood-or test-based model selection in linear models. Statistics & Probability Letters 140, 7–12.
  230. Schoedel R, Au Q, Völkel ST, Lehmann F, Becker D, Bühner M, Bischl B, Hussmann H, Stachl C (2018) Digital Footprints of Sensation Seeking. Zeitschrift für Psychologie 226, 232–245.
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  231. Schalk D, Thomas J, Bischl B (2018) compboost: Modular Framework for Component-Wise Boosting. JOSS 3, 967.
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  232. Thomas J, Coors S, Bischl B (2018) Automatic Gradient Boosting. ICML AutoML Workshop.
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  233. 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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  234. Völkel ST, Graefe J, Schödel R, Häuslschmid R, Stachl C, Au Q, Hussmann H (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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  235. Stachl C, Hilbert S, Au Q, Buschek D, De Luca A, Bischl B, Hussmann H, Bühner M (2017) Personality Traits Predict Smartphone Usage (C Wrzus, Ed.). European Journal of Personality 31, 701–722.
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  236. 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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  237. 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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  238. 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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  239. 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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  240. Beggel L, Sun X, Bischl B (2017) mlrFDA: an R toolbox for functional data analysis. Ulmer Informatik-Berichte, 15.
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  241. 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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  242. Thomas J, Hepp T, Mayr A, Bischl B (2017) Probing for sparse and fast variable selection with model-based boosting. Computational and mathematical methods in medicine 2017.
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  243. Kotthaus H, Richter J, Lang A, Thomas J, Bischl B, Marwedel P, Rahnenführer J, Lang M (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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  244. 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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  245. 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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  246. Casalicchio G, Bossek J, Lang M, Kirchhoff D, Kerschke P, Hofner B, Seibold H, Vanschoren J, Bischl B (2017) OpenML: An R package to connect to the machine learning platform OpenML. Computational Statistics, 977–991.
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  247. 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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  248. Bischl B, Lang M, Kotthoff L, Schiffner J, Richter J, Studerus E, Casalicchio G, Jones ZM (2016) mlr: Machine Learning in R. The Journal of Machine Learning Research 17, 5938–5942.
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  249. 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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  250. 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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  251. Bischl B, Kerschke P, Kotthoff L, Lindauer M, Malitsky Y, Frechétte A, Hoos H, Hutter F, Leyton-Brown K, Tierney K, Vanschoren J (2016) ASlib: A Benchmark Library for Algorithm Selection. Artificial Intelligence 237, 41–58.
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  252. Bischl B, Kühn T, Szepannek G (2016) On Class Imbalance Correction for Classification Algorithms in Credit Scoring. In: In: Lübbecke M , In: Koster A , In: Letmathe P , In: Madlener R , In: Peis B , In: Walther G (eds) Operations Research Proceedings 2014, pp. 37–43. Springer International Publishing.
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  253. Demircioglu A, Horn D, Glasmachers T, Bischl B, Weihs C (2016) Fast model selection by limiting SVM training times.
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  254. 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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  255. 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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  256. 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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  257. 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.
  258. 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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  259. Schiffner J, Bischl B, Lang M, Richter J, Jones ZM, Probst P, Pfisterer F, Gallo M, Kirchhoff D, Kühn T, Thomas J, Kotthoff L (2016) mlr Tutorial.
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  260. 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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  261. 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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  262. 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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  263. 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.
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  264. 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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  265. 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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  266. 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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  267. 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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  268. 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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  269. 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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  270. 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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  271. 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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  272. 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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  273. Vanschoren J, Rijn JN van, Bischl B, Casalicchio G, Feurer M (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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  274. 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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  275. 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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  276. Kerschke P, Preuss M, Hernández C, Schütze O, Sun J-Q, Grimme C, Rudolph G, Bischl B, Trautmann H (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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  277. 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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  278. Vanschoren J, Rijn JN van, Bischl B, Torgo L (2014) OpenML: Networked Science in Machine Learning. SIGKDD Explorations Newsletter 15, 49–60.
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  279. 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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  280. 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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  281. 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 69, 151–182.
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  282. Rijn J van, Bischl B, Torgo L, Gao G, Umaashankar V, Fischer S, Winter P, Wiswedel B, Berthold MR, Vanschoren J (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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  283. Bischl B, Schiffner J, Weihs C (2013) Benchmarking local classification methods. Computational Statistics 28, 2599–2619.
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  284. Bergmann S, Ziegler N, Bartels T, Hübel J, Schumacher C, Rauch E, Brandl S, Bender A, Casalicchio G, Krautwald-Junghanns M-E, others (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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  285. 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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  286. Rijn J van, Umaashankar V, Fischer S, Bischl B, Torgo L, Gao B, Winter P, Wiswedel B, Berthold MR, Vanschoren J (2013) A RapidMiner extension for Open Machine Learning RapidMiner Community Meeting and Conference (RCOMM), pp. 59–70.
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  287. Ziegler N, Bergmann S, Huebei J, Bartels T, Schumacher C, Bender A, Casalicchio G, Kuechenhoff H, Krautwald-Junghanns M-E, Erhard M (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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  288. 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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  289. 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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  290. 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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  291. 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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  292. 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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  293. Weihs C, O. M, Bischl B, Fritsch A, Trautmann H, Karbach T-M, Spaan B (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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  294. 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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  295. 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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  296. 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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  297. Blume H, Bischl B, Botteck M, Igel C, Martin R, Roetter G, Rudolph G, Theimer W, Vatolkin I, Weihs C (2011) Huge Music Archives on Mobile Devices. IEEE Signal Processing Magazine 28, 24–39.
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  298. 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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  299. Weihs C, Friedrichs K, Bischl B (2011) Statistics for hearing aids: Auralization Second Bilateral German-Polish Symposium on Data Analysis and its Applications (GPSDAA),
  300. 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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  301. Szepannek G, Gruhne M, Bischl B, Krey S, Harczos T, Klefenz F, Dittmar C, Weihs C (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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  302. 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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  303. 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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  304. 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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  305. 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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  306. 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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  307. Stüber AT, Coors S, Schachtner B, Weber T, Rügamer D, Bender A, Mittermeier A, Öcal O, Seidensticker M, Ricke J, others (2023) A Comprehensive Machine Learning Benchmark Study for Radiomics-Based Survival Analysis of CT Imaging Data in Patients With Hepatic Metastases of CRC. Investigative Radiology, 10–1097.
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  308. Rügamer D, Kolb C, Klein N (2023) Semi-Structured Distributional Regression. The American Statistician.
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  309. Goschenhofer J, Ragupathy P, Heumann C, Bischl B, Aßenmacher M (2022) CC-Top: Constrained Clustering for Dynamic Topic Discovery Workshop on Ever Evolving NLP (EvoNLP), Association for Computational Linguistics, Abu Dhabi, United Arab Emirates.
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  310. Dexl J, Benz M, Kuritcyn P, Wittenberg T, Bruns V, Geppert C, Hartmann A, Bischl B, Goschenhofer J (2022) Robust Colon Tissue Cartography with Semi-Supervision. Current Directions in Biomedical Engineering 8, 344–347.
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  311. Rueger S, Goschenhofer J, Nath A, Firsching M, Ennen A, Bischl B (2022) Deep-Learning-based Aluminum Sorting on Dual Energy X-Ray Transmission Data. Sensor-based Sorting and Control.
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  312. 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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  313. 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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  314. Vanschoren J, Bischl B, Hutter F, Sebag M, Kegl B, Schmid M, Napolitano G, Wolstencroft K (2015) Towards a data science collaboratory. Lecture Notes in Computer Science (IDA 2015) 9385.
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