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A3  Methods for Efficient Resource Utilization in Machine Learning Algorithms


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Prof. Dr. Chen, Jian Jia
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Prof. Dr. Rahnenführer, Jörg

The goal of this project is the development of methods for algorithm selection and configuration under resource constraints. Especially of interest are scenarios where a single evaluation of an algorithm is expensive. We address the case where many competing candidate algorithms are available and each algorithm has specific hyperparameters that have to be tuned to obtain the best possible outcome. It is not possible to exhaustively search through this space, because the number of configurations that can be evaluated during the optimisation is heavily limited due to their long runtimes. In a typical situation the algorithm is a machine learning method that is applied on a regression or classification data set. The optimisation goal is to find a machine learning method from a set of candidates and configure its hyperparameters to achieve the best possible prediction quality.

Model-based optimisation (MBO) addresses this challenge. It uses a regression model as a surrogate to approximate the objective function. For example, the prediction quality of a machine learning algorithm on a given task is predicted by a Gaussian process regression. The predictions obtained from the surrogate model help to move quickly to regions in the search space with promising prediction quality. This reduces the number of expensive evaluations during the optimisation. Due to the enormous number of possible configurations, the overall MBO wall-clock runtime still can be unreasonably long. The use of parallel computing systems and efficient resource utilisation becomes essential.

To address these challenges, we have developed the framework resource-aware model-based optimization (RAMBO) with scheduling for heterogeneous runtimes. It extends MBO to work on parallel systems while maximising resource utilisation. Thus, methods applied for the optimisation to achieve high efficiency of embedded systems can be adapted for improving the parallel execution of machine learning tasks in modern computing systems. In the third phase, we want to investigate scenarios with additional challenges, such as time-varying objective functions, insufficient prediction quality due to small sample sizes, and data streams. Efficient solutions for these challenges can be obtained by extending the RAMBO framework and by further improving the scheduling strategies. Time-varying objective functions are common in the real world, i.e., the best-performing algorithm configuration changes over time, which is generally called concept drift. For small sample sizes, we will investigate the combination of real data and additional simulated data. For data streams the machine learning method will adapt to changes in order to optimise the prediction quality. These extensions of RAMBO make it usable for a wide range of applications, including data rate prediction for mobile phones as well as traffic analysis and prediction.

To evaluate the new methods, we use established benchmarks that provide a controlled environment to draw objective conclusions. In a second step we will also verify our proposed approach with real-world data. In addition to the developed methods themselves, a major outcome of this project are self-contained and well-documented open-source software packages, assuring the reproducibility of the experiments and future usability for other researchers around the world.

Project management:

Prof. Dr. Chen, Jian Jia
Prof. Dr. Rahnenführer, Jörg

Alumni project management:


marwedel.png
Prof. Dr. Marwedel, Peter

Alumni:

Dr. Bommert, Andrea
Dr. Cordes, Daniel
Dr. Kammers, Kai
Dr. Kleinsorge, Jan
Dr. Kotthaus, Helena
Dr. Lang, Michel
Dr.-Ing. Masoudinejad, Mojtaba
Moghaddas, Vahidreza
Dr. Neugebauer, Olaf
Dr. Plazar, Sascha
Dr. Rempel, Eugen
Dr. Richter, Jakob
Shi, Junjie
Stolte, Marieke

Software:

QCAPES-Framework
R packages BatchJobs and BatchExperiments
RAMBO: Resource-Aware Model-Based Optimization
TraceR : Performance Analysis for R Programs

Publications:

Bommert/etal/2022a Bommert, Andrea and Rahnenführer, Jörg and Lang, Michel. Employing an Adjusted Stability Measure for Multi-criteria Model Fitting on Data Sets with Similar Features. In Nicosia G. et al. (editors), Machine Learning, Optimization, and Data Science, pages 81-92, Springer, 2022. LaTeX Symbol


Richter/etal/2022a Richter, Jakob and Friede, Tim and Rahnenführer, Jörg. Improving adaptive seamless designs through Bayesian optimization. In Biometrical Journal, Vol. 64, No. 5, pages 948-963, 2022. LaTeX Symbol


Binder/etal/2021a Martin Binder and Florian Pfisterer and Michel Lang and Lennart Schneider and Lars Kotthoff and Bernd Bischl. mlr3pipelines - Flexible Machine Learning Pipelines in R. In Journal of Machine Learning Research, Vol. 22, No. 184, pages 1-7, 2021. LaTeX Symbol Green Arrow


Bischl/etal/2021a Bernd Bischl and Martin Binder and Michel Lang and Tobias Pielok and Jakob Richter and Stefan Coors and Janek Thomas and Theresa Ullmann and Marc Becker and Anne-Laure Boulesteix and Difan Deng and Marius Lindauer. Hyperparameter Optimization: Foundations, Algorithms, Best Practices and Open Challenges. 2021. LaTeX Symbol Green Arrow


Bischl/etal/2021b Bischl, Bernd and Casalicchio, Giuseppe and Feurer, Matthias and Gijsbers, Pieter and Hutter, Frank and Lang, Michel and Gomes Mantovani, Rafael and van Rijn, Jan and Vanschoren, Joaquin. OpenML Benchmarking Suites. In J. Vanschoren and S. Yeung (editors), Proceedings of the Neural Information Processing Systems Track on Datasets and Benchmarks, Vol. 1, 2021. LaTeX Symbol


Bommert/etal/2021a Bommert, Andrea and Welchowski, Thomas and Schmid, Matthias and Rahnenführer, Jörg. Benchmark of filter methods for feature selection in high-dimensional gene expression survival data. In Briefings in Bioinformatics, pages bbab354, 2021. LaTeX Symbol Green Arrow


Bommert/Lang/2021a Bommert, Andrea and Lang, Michel. stabm: Stability Measures for Feature Selection. In Journal of Open Source Software, Vol. 6, No. 59, pages 3010, 2021. LaTeX Symbol Green Arrow


Chen/etal/2021a Chen, Jian-Jia and Huang, Wen-Hung and von der Brüggen, Georg and Ueter, Niklas. On the Formalism and Properties of Timing Analyses in Real-Time Embedded Systems. In A Journey of Embedded and Cyber-Physical Systems - Essays Dedicated to Peter Marwedel on the Occasion of His 70th Birthday, pages 37--55, 2021. LaTeX Symbol Green Arrow


Jagdhuber/Rahnenfuehrer/2021a Jagdhuber, Rudolf and Rahnenführer, Jörg. Implications on Feature Detection When Using the Benefit–Cost Ratio. In SN Computer Science, Vol. 2, No. 4, pages 316, 2021. LaTeX Symbol Green Arrow


Madjar/etal/2021a Madjar, Katrin and Zucknick, Manuela and Ickstadt, Katja and Rahnenführer, Jörg. Combining heterogeneous subgroups with graph-structured variable selection priors for Cox regression. In BMC Bioinform., Vol. 22, No. 1, pages 586, 2021. LaTeX Symbol Green Arrow


Marwedel/2021b Marwedel, Peter. Eingebettete Systeme - Grundlagen Eingebetteter Systeme in Cyber-Physikalischen Systemen. Springer, 2021. LaTeX Symbol Green Arrow


Shi/etal/2021a Shi, Junjie and Bian, Jiang and Richter, Jakob and Chen, Kuan-Hsun and Rahnenführer, Jörg and Xiong, Haoyi and Chen, Jian-Jia. MODES: model-based optimization on distributed embedded systems. In Machine Learning (Journal Track of ECML/PKDD), Vol. 110, No. 6, pages 1527--1547, 2021. LaTeX Symbol Green Arrow


Shi/etal/2021c Shi, Junjie and Ueter, Niklas and von der Brüggen, Georg and Chen, Jian-Jia. Graph-Based Optimizations for Multiprocessor Nested Resource Sharing. In Proceedings of the 27th IEEE International Conference on Embedded and Real-Time Computing Systems and Applications, RTCSA, 2021. LaTeX Symbol


Sonabend/etal/2021a Raphael Sonabend and Franz J Király and Andreas Bender and Bernd Bischl and Michel Lang. mlr3proba: An R Package for Machine Learning in Survival Analysis. In Bioinformatics, 2021. LaTeX Symbol


Bommert/etal/2020a Bommert, Andrea and Sun, Xudong and Bischl, Bernd and Rahnenführer, Jörg and Lang, Michel. Benchmark for Filter Methods for Feature Selection in High-Dimensional Classification Data. In Computational Statistics & Data Analysis, Vol. 143, pages 106839, 2020. PDF-Symbol LaTeX Symbol


Bommert/Rahnenfuehrer/2020a Bommert, Andrea and Rahnenführer, Jörg. Adjusted Measures for Feature Selection Stability for Data Sets with Similar Features. In Nicosia, Giuseppe and Ojha, Varun and La Malfa, Emanuele and Jansen, Giorgio and Sciacca, Vincenzo and Pardalos, Panos and Giuffrida, Giovanni and Umeton, Renato (editors), Machine Learning, Optimization, and Data Science, pages 203-214, Springer, 2020. LaTeX Symbol Green Arrow


Chen/etal/2020f Chen, Jian-Jia and Shi, Junjie and von der Brüggen, Georg and Ueter, Niklas. Scheduling of Real-Time Tasks with Multiple Critical Sections in Multiprocessor Systems. In IEEE Transactions on Computers, 2020. LaTeX Symbol


Jagdhuber/etal/2020a Jagdhuber, Rudolf and Lang, Michel and Stenzl, Arnulf and Neuhaus, Jochen and Rahnenführer, Jörg. Cost-Constrained feature selection in binary classification: adaptations for greedy forward selection and genetic algorithms. In BMC Bioinformatics, Vol. 21, No. 1, pages 26, 2020. LaTeX Symbol Green Arrow


Jagdhuber/etal/2020b Jagdhuber, Rudolf and Lang, Michel and Rahnenführer, Jörg. Feature Selection Methods for Cost-Constrained Classification in Random Forests. 2020. LaTeX Symbol Green Arrow


Marwedel/Mitra/2020a P. Marwedel and T. Mitra and M. E. Grimheden and H. A. Andrade. Survey on Education for Cyber-Physical Systems. In IEEE Design & Test, Vol. 37, No. 6, pages 56-70, 2020. LaTeX Symbol Green Arrow


Marwedel/Mitra/2020b P. Marwedel and T. Mitra and M. E. Grimheden and H. A. Andrade. Guest Editors’ Introduction: Guest Editors’ Introduction: Education for Cyber–Physical Systems. In IEEE Design & Test, Vol. 37, No. 6, pages 5-7, 2020. LaTeX Symbol Green Arrow


Nodoushan/Safaei/2020a Mostafa Jafari-Nodoushan and Bardia Safaei and Alireza Ejlali and Jian-Jia Chen. Leakage-Aware Battery Lifetime Analysis Using the Calculus of Variations. In IEEE Transactions on Circuits and Systems I: Regular Papers, Vol. 67, No. 12, pages 4829-4841, 2020. LaTeX Symbol Green Arrow


Richter/etal/2020a Richter, Jakob and Shi, Junjie and Chen, Jian-Jia and Rahnenführer, Jörg and Lang, Michel. Model-Based Optimization with Concept Drifts. In Proceedings of the 2020 Genetic and Evolutionary Computation Conference, pages 877?885, New York, NY, USA, Association for Computing Machinery, 2020. LaTeX Symbol Green Arrow


Richter/Shi/2020a Richter, Jakob and Shi, Junjie and Chen, Jian-Jia and Rahnenführer, Jörg and Lang, Michel. Model-based Optimization with Concept Drifts. In The Genetic and Evolutionary Computation Conference (GECCO), 2020. LaTeX Symbol


Schoenberger/etal/2020a Schönberger, Lea and von der Brüggen, Georg and Chen, Kuan-Hsun and Sliwa, Benjamin and Youssef, Hazem and Ramachandran, Aswin and Wietfeld, Christian and ten Hompel, Michael and Chen, Jian-Jia. Offloading Safety- and Mission-Critical Tasks via Unreliable Connections. In 32nd Euromicro Conference on Real-Time Systems (ECRTS), 2020. LaTeX Symbol


Schwitalla/Schoenberger/2020a Schwitalla, Sebastian and Schoenberger, Lea and Chen, Jian-Jia. Priority-Preserving Optimization of Status Quo ID-Assignments in Controller Area Network. In Design, Automation and Test in Europe Conference (DATE), IEEE, 2020. LaTeX Symbol


Kotthaus/etal/2019a Tözün, Pinar and Kotthaus, Helena. Scheduling Data-Intensive Tasks on Heterogeneous Many Cores. In IEEE Data Engineering Bulletin, Vol. 42, No. 1, pages 61-72, 2019. PDF-Symbol LaTeX Symbol Green Arrow


Kotthaus/etal/2019b Kotthaus, Helena and Schönberger, Lea and Lang, Andreas and Chen, Jian-Jia and Marwedel, Peter. Can Flexible Multi-Core Scheduling Help to Execute Machine Learning Algorithms Resource-Efficiently?. In 22nd International Workshop on Software and Compilers for Embedded Systems, pages 59-62, ACM, 2019. LaTeX Symbol Green Arrow


Kotthaus/Vitek/2019c Kotthaus, Helena and Vitek, Jan. Typical Mistakes in Data Science: Should you Trust my Model?. In Abstract Booklet of the International R User Conference (UseR!), Toulouse, France, 2019. LaTeX Symbol


Lang/etal/2019a Lang, Michel and Binder, Martin and Richter, Jakob and Schratz, Patrick and Pfisterer, Florian and Coors, Stefan and Au, Quay and Casalicchio, Giuseppe and Kotthoff, Lars and Bischl, Bernd. Mlr3: A Modern Object-Oriented Machine Learning Framework in R. In Journal of Open Source Software, Vol. 4, No. 44, pages 1903, 2019. LaTeX Symbol Green Arrow


Richter/etal/2019a Richter, Jakob and Madjar, Katrin and Rahnenführer, Jörg. Model-based optimization of subgroup weights for survival analysis. In Bioinformatics, Vol. 35, No. 14, pages i484-i491, 2019. LaTeX Symbol Green Arrow


Shi/etal/2019a Shi, Junjie and Ueter, Niklas and von der Brüggen, Georg and Chen, Jian-jia. Multiprocessor Synchronization of Periodic Real-Time Tasks Using Dependency Graphs. In 2019 IEEE Real-Time and Embedded Technology and Applications Symposium (RTAS), pages 279--292, IEEE, 2019. PDF-Symbol LaTeX Symbol


Shi/etal/2019b Shi, Junjie and Ueter, Niklas and von der Brüggen, Georg and Chen, Jian-Jia. Partitioned Scheduling for Dependency Graphs in Multiprocessor Real-Time Systems. In Proceedings of the 25th IEEE International Conference on Embedded and Real-Time Computing Systems and Applications, RTCSA, IEEE, 2019. PDF-Symbol LaTeX Symbol


Chen/etal/2018f Chen, Jian-Jia and von der Brueggen, Georg and Shi, Junjie and Ueter, Niklas. Dependency Graph Approach for Multiprocessor Real-Time Synchronization. In Real-Time Systems Symposium (RTSS), 2018. LaTeX Symbol


Kotthaus/etal/2018a Kotthaus, Helena and Lang, Andreas and Marwedel, Peter. Optimizing Parallel R Programs via Dynamic Scheduling Strategies. In Abstract Booklet of the International R User Conference (UseR!), Brisbane, Australia, 2018. LaTeX Symbol


Marwedel/2018a Marwedel, P.. Design of Cyber - Physical Systems. Global Initiative of Academic Networks (GIAN), Course 270, Ministryof Human Resource Development, Government of India, Indian Institute of Technology Delhi, 2018. LaTeX Symbol


Marwedel/2018b Marwedel, Peter. Embedded System Design - Embedded Systems Foundations of Cyber-Physical Systems, and the Internet of Things. Springer, 2018. LaTeX Symbol


Richter/etal/2018a Jakob Richter, Katrin Madjar, Jörg Rahnenführer. Model-Based Optimization of Subgroup Weights for Survival Analysis. No. 3, Faculty of Statistics, TU Dortmund University, 2018. PDF-Symbol LaTeX Symbol


Bischl/etal/2017a Bischl, Bernd and Richter, Jakob and Bossek, Jakob and Horn, Daniel and Thomas, Janek and Lang, Michel. mlrMBO: A Modular Framework for Model-Based Optimization of Expensive Black-Box Functions. In arXiv preprint arXiv:1703.03373, 2017. LaTeX Symbol Green Arrow


Bommert/etal/2017a Andrea Bommert and Jörg Rahnenführer and Michel Lang. A multi-criteria approach to find predictive and sparse models with stable feature selection for high-dimensional data. In Computational and Mathematical Methods in Medicine, Vol. 2017, pages 1--18, 2017. LaTeX Symbol Green Arrow


Kotthaus/2017b Kotthaus, Helena and Lang, Andreas and Neugebauer, Olaf and Marwedel, Peter. R goes Mobile: Efficient Scheduling for Parallel R Programs on Heterogeneous Embedded Systems. In Abstract Booklet of the International R User Conference (UseR!), pages 74, Brussels, Belgium, 2017. LaTeX Symbol Green Arrow


Kotthaus/etal/2017a Kotthaus, Helena and Richter, Jakob and Lang, Andreas and Thomas, Janek and Bischl, Bernd and Marwedel, Peter and Rahnenführer, Jörg and Lang, Michel. RAMBO: Resource-Aware Model-Based Optimization with Scheduling for Heterogeneous Runtimes and a Comparison with Asynchronous Model-Based Optimization. In Procs. of the 11th LION, pages 180-195, 2017. LaTeX Symbol Green Arrow


Marwedel/etal/2017a Marwedel, Peter and Falk, Heiko and Neugebauer, Olaf. Memory-Aware Optimization of Embedded Software for Multiple Objectives. Vol. Handbook of Hardware/Software Codesign, pages 1-37, Springer, 2017. LaTeX Symbol


Neugebauer/etal/2017a Neugebauer, Olaf and Marwedel, Peter and Kühn, Roland and Engel, Michael. Quality Evaluation Strategies for Approximate Computing in Embedded Systems. In Technological Innovation for Smart Systems: 8th IFIP WG 5.5/SOCOLNET Advanced Doctoral Conference on Computing, Electrical and Industrial Systems, DoCEIS 2017, pages 203--210, Costa de Caparica, Portugal, 2017. LaTeX Symbol Green Arrow


Richter/2017b Richter, Jakob and Rahnenführer, Jörg and Lang, Michel. mlrHyperopt: Effortless and collaborative hyperparameter optimization experiments. In Abstract Booklet of the International R User Conference (UseR!), pages 78, Brussels, Belgium, 2017. LaTeX Symbol Green Arrow


Bischl/etal/2016a Bernd Bischl and Michel Lang and Lars Kotthoff and Julia Schiffner and Jakob Richter and Erich Studerus and Giuseppe Casalicchio and Zachary M. Jones. mlr: Machine Learning in R. In Journal of Machine Learning Research, Vol. 17, No. 170, pages 1-5, 2016. PDF-Symbol LaTeX Symbol Green Arrow


Kotthaus/etal/2016a Kotthaus, Helena and Richter, Jakob and Lang, Andreas and Lang, Michel and Marwedel, Peter. Resource-Aware Scheduling Strategies for Parallel Machine Learning R Programs through RAMBO. In Abstract Booklet of the International R User Conference (UseR!), pages 195, Stanford University, Palo Alto, California, 2016. LaTeX Symbol Green Arrow


Richter/etal/2016a Richter, Jakob and Lang, Michel and Bischl, Bernd. mlrMBO: A Toolbox for Model-Based Optimization of Expensive Black-Box Functions. In Abstract Booklet of the International R User Conference (UseR!), pages 202, Stanford University, Palo Alto, California, 2016. PDF-Symbol LaTeX Symbol Green Arrow


Richter/etal/2016b Richter, Jakob and Kotthaus, Helena and Bischl, Bernd and Marwedel, Peter and Rahnenführer, Jörg and Lang, Michel. Faster Model-Based Optimization through Resource-Aware Scheduling Strategies. In Proceedings of the 10th International Conference: Learning and Intelligent Optimization (LION 10), Vol. 10079, pages 267--273, Springer, 2016. LaTeX Symbol Green Arrow


Bischl/etal/2015a Bischl, Bernd and Lang, Michel and Mersmann, Olaf and Rahnenführer, Jörg and Weihs, Claus. BatchJobs and BatchExperiments: Abstraction Mechanisms for Using R in Batch Environments. In Journal of Statistical Software, Vol. 64, No. 11, pages 1--235, 2015. LaTeX Symbol Green Arrow


Kotthaus/2015a Kotthaus, Helena and Korb, Ingo and Marwedel, Peter. Performance Analysis for Parallel R Programs: Towards Efficient Resource Utilization. No. 1, Department of Computer Science 12, TU Dortmund University, 2015. PDF-Symbol LaTeX Symbol Green Arrow


Kotthaus/etal/2015a Kotthaus, Helena and Korb, Ingo and Marwedel, Peter. Distributed Performance Analysis for R. In R Implementation, Optimization and Tooling Workshop (RIOT), Prag, Czech, 2015. LaTeX Symbol


Kotthaus/etal/2014a Kotthaus, Helena and Korb, Ingo and Lang, Michel and Bischl, Bernd and Rahnenführer, Jörg and Marwedel, Peter. Runtime and Memory Consumption Analyses for Machine Learning R Programs. In Journal of Statistical Computation and Simulation, Vol. 85, No. 1, pages 14-29, 2014. LaTeX Symbol Green Arrow


Kotthaus/etal/2014b Kotthaus, Helena and Korb, Ingo and Engel, Michael and Marwedel, Peter. Dynamic Page Sharing Optimization for the R Language. In Proceedings of the 10th Symposium on Dynamic Languages, pages 79-90, Portland, Oregon, USA, ACM, 2014. LaTeX Symbol Green Arrow


Kotthaus/etal/2014c Kotthaus, Helena and Korb, Ingo and Künne, Markus and Marwedel, Peter. Performance Analysis for R: Towards a Faster R Interpreter. In Abstract Booklet of the International R User Conference (UseR!), pages 104, Los Angeles, USA, 2014. PDF-Symbol LaTeX Symbol


Lang/etal/2014a Lang, Michel and Kotthaus, Helena and Marwedel, Peter and Weihs, Claus and Rahnenführer, Jörg and Bischl, Bernd. Automatic Model Selection for High-Dimensional Survival Analysis. In Journal of Statistical Computation and Simulation, Vol. 85, No. 1, pages 62--76, 2014. LaTeX Symbol Green Arrow


Lee/etal/2014a Sangkyun Lee and Jörg Rahnenführer and Michel Lang and Katleen de Preter and Pieter Mestdagh and Jan Koster and Rogier Versteeg and Raymond Stallings and Luigi Varesio and Shahab Asgharzadeh and Johannes Schulte and Kathrin Fielitz and Melanie Heilmann and Katharina Morik and Alexander Schramm. Robust Selection of Cancer Survival Signatures from High-Throughput Genomic Data Using Two-Fold Subsampling. In PLoS ONE, Vol. 9, pages e108818, 2014. PDF-Symbol LaTeX Symbol


Ahrens/etal/2013a Maike Ahrens and Michael Turewicz and Swaantje Casjens and Caroline May and Beate Pesch and Christian Stephan and Dirk Woitalla and Ralf Gold and Thomas Brüning and Helmut E. Meyer and Jörg Rahnenführer and Martin Eisenacher. Detection of Patient Subgroups with Differential Expression in Omics Data: A Comprehensive Comparison of Univariate Measures. In PLoS ONE, Vol. 8, No. 11, pages e79380, 2013. LaTeX Symbol


Cordes/etal/2013a Cordes, Daniel and Engel, Michael and Neugebauer, Olaf and Marwedel, Peter. Automatic Extraction of Pipeline Parallelism for Embedded Heterogeneous Multi-Core Platforms. In Proceedings of the Sixteenth International Conference on Compilers, Architectures, and Synthesis for Embedded Systems (CASES 2013), Montreal, Canada, 2013. LaTeX Symbol


Cordes/etal/2013b Cordes, Daniel and Engel, Michael and Neugebauer, Olaf and Marwedel, Peter. Automatic Extraction of Multi-Objective Aware Parallelism for Heterogeneous MPSoCs. In Proceedings of the Sixth International Workshop on Multi-/Many-core Computing Systems (MuCoCoS 2013), Edinburgh, Scotland, UK, 2013. LaTeX Symbol


Cordes/etal/2013c Cordes, Daniel and Engel, Michael and Neugebauer, Olaf and Marwedel, Peter. Automatic Extraction of Task-Level Parallelism for Heterogeneous MPSoC. In Proceedings of the Fourth International Workshop on Parallel Software Tools and Tool Infrastructures (PSTI 2013), Lyon, France, 2013. LaTeX Symbol


Kotthaus/etal/2013a Kotthaus, Helena and Lang, Michel and Rahnenführer, Jörg and Marwedel, Peter. Runtime and Memory Consumption Analyses for Machine Learning R Programs. In Abstracts 45. Arbeitstagung, Ulmer Informatik-Berichte, No. 2013-07, pages 3--4, Ulmer Informatik-Berichte, 2013. PDF-Symbol LaTeX Symbol


Lang/etal/2013a Lang, Michel and Bischl, Bernd and Weihs, Claus and Rahnenführer, Jörg. Automatic model selection for high-dimensional survival analysis. In Abstracts der 45. Arbeitstagung, No. 2013-07, pages 32--33, Ulmer Informatik-Berichte, 2013. PDF-Symbol LaTeX Symbol


Bischl/etal/2012a Bischl, Bernd and Lang, Michel and Mersmann, Olaf and Rahnenführer, Jörg and Weihs, Claus. Computing on High Performance Clusters with R: Packages BatchJobs and BatchExperiments. No. 1, Fakultät Statistik, TU Dortmund, 2012. PDF-Symbol LaTeX Symbol


Kotthaus/etal/2012a Kotthaus, Helena and Plazar, Sascha and Marwedel, Peter. A JVM-based Compiler Strategy for the R Language. In Abstract Booklet of the 8th International R User Conference (UseR!), pages 68, Nashville, Tennessee, USA, 2012. LaTeX Symbol


Lohr/etal/2012a Lohr, M. and Köllmann, C. and Freis, E. and Hellwig, B. and Hengstler, J. G. and Ickstadt, K. and Rahnenführer, J.. Optimal strategies for sequential validation of significant features from high-dimensional genomic data. In Journal of Toxicology and Environmental Health, Part A, Vol. 75, No. 8-10, pages 447-460, 2012. LaTeX Symbol


Plazar/etal/2012a Plazar, Sascha and Falk, Heiko and Marwedel, Peter. WCET-aware Static Locking of Instruction Caches. In Proceedings of the International Symposium on Code Generation and Optimization (CGO), San Jose, CA, USA, 2012. PDF-Symbol LaTeX Symbol


Falk/etal/2011a Falk, Heiko and Kotthaus, Helena. WCET-driven Cache-aware Code Positioning. In Proceedings of the International Conference on Compilers, Architectures and Synthesis for Embedded Systems (CASES), pages 145-154, Taipei, Taiwan, 2011. LaTeX Symbol Green Arrow


Kammers/etal/2011a Kammers, K. and Lang, M. and Hengstler, J. G. and Schmidt, M. and Rahnenführer, J.. Survival Models with Preclustered Gene Groups as Covariates. In BMC Bioinformatics, Vol. 12, No. 1, pages 478, 2011. PDF-Symbol LaTeX Symbol Green Arrow


Plazar/etal/2011a Plazar, Sascha and Kleinsorge, Jan C. and Falk, Heiko and Marwedel, Peter. WCET-driven Branch Prediction aware Code Positioning. In Proceedings of the International Conference on Compilers, Architectures and Synthesis for Embedded Systems (CASES), pages 165-174, Taipei, Taiwan, 2011. LaTeX Symbol


  • Bommert/etal/2022a - Employing an Adjusted Stability Measure for Multi-criteria Model Fitting on Data Sets with Similar Features
  • Richter/etal/2022a - Improving adaptive seamless designs through Bayesian optimization
  • Binder/etal/2021a - mlr3pipelines - Flexible Machine Learning Pipelines in R
  • Bischl/etal/2021a - Hyperparameter Optimization: Foundations, Algorithms, Best Practices and Open Challenges
  • Bischl/etal/2021b - OpenML Benchmarking Suites
  • Bommert/etal/2021a - Benchmark of filter methods for feature selection in high-dimensional gene expression survival data
  • Bommert/Lang/2021a - stabm: Stability Measures for Feature Selection
  • Chen/etal/2021a - On the Formalism and Properties of Timing Analyses in Real-Time Embedded Systems
  • Jagdhuber/Rahnenfuehrer/2021a - Implications on Feature Detection When Using the Benefit–Cost Ratio
  • Madjar/etal/2021a - Combining heterogeneous subgroups with graph-structured variable selection priors for Cox regression
  • Marwedel/2021b - Eingebettete Systeme - Grundlagen Eingebetteter Systeme in Cyber-Physikalischen Systemen
  • Shi/etal/2021a - MODES: model-based optimization on distributed embedded systems
  • Shi/etal/2021c - Graph-Based Optimizations for Multiprocessor Nested Resource Sharing
  • Sonabend/etal/2021a - mlr3proba: An R Package for Machine Learning in Survival Analysis
  • Bommert/etal/2020a - Benchmark for Filter Methods for Feature Selection in High-Dimensional Classification Data
  • Bommert/Rahnenfuehrer/2020a - Adjusted Measures for Feature Selection Stability for Data Sets with Similar Features
  • Chen/etal/2020f - Scheduling of Real-Time Tasks with Multiple Critical Sections in Multiprocessor Systems
  • Jagdhuber/etal/2020a - Cost-Constrained feature selection in binary classification: adaptations for greedy forward selection and genetic algorithms
  • Jagdhuber/etal/2020b - Feature Selection Methods for Cost-Constrained Classification in Random Forests
  • Marwedel/Mitra/2020a - Survey on Education for Cyber-Physical Systems
  • Marwedel/Mitra/2020b - Guest Editors’ Introduction: Guest Editors’ Introduction: Education for Cyber–Physical Systems
  • Nodoushan/Safaei/2020a - Leakage-Aware Battery Lifetime Analysis Using the Calculus of Variations
  • Richter/etal/2020a - Model-Based Optimization with Concept Drifts
  • Richter/Shi/2020a - Model-based Optimization with Concept Drifts
  • Schoenberger/etal/2020a - Offloading Safety- and Mission-Critical Tasks via Unreliable Connections
  • Schwitalla/Schoenberger/2020a - Priority-Preserving Optimization of Status Quo ID-Assignments in Controller Area Network
  • Kotthaus/etal/2019a - Scheduling Data-Intensive Tasks on Heterogeneous Many Cores
  • Kotthaus/etal/2019b - Can Flexible Multi-Core Scheduling Help to Execute Machine Learning Algorithms Resource-Efficiently?
  • Kotthaus/Vitek/2019c - Typical Mistakes in Data Science: Should you Trust my Model?
  • Lang/etal/2019a - Mlr3: A Modern Object-Oriented Machine Learning Framework in R
  • Richter/etal/2019a - Model-based optimization of subgroup weights for survival analysis
  • Shi/etal/2019a - Multiprocessor Synchronization of Periodic Real-Time Tasks Using Dependency Graphs
  • Shi/etal/2019b - Partitioned Scheduling for Dependency Graphs in Multiprocessor Real-Time Systems
  • Chen/etal/2018f - Dependency Graph Approach for Multiprocessor Real-Time Synchronization
  • Kotthaus/etal/2018a - Optimizing Parallel R Programs via Dynamic Scheduling Strategies
  • Marwedel/2018a - Design of Cyber - Physical Systems
  • Marwedel/2018b - Embedded System Design - Embedded Systems Foundations of Cyber-Physical Systems, and the Internet of Things
  • Richter/etal/2018a - Model-Based Optimization of Subgroup Weights for Survival Analysis
  • Bischl/etal/2017a - mlrMBO: A Modular Framework for Model-Based Optimization of Expensive Black-Box Functions
  • Bommert/etal/2017a - A multi-criteria approach to find predictive and sparse models with stable feature selection for high-dimensional data
  • Kotthaus/2017b - R goes Mobile: Efficient Scheduling for Parallel R Programs on Heterogeneous Embedded Systems
  • Kotthaus/etal/2017a - RAMBO: Resource-Aware Model-Based Optimization with Scheduling for Heterogeneous Runtimes and a Comparison with Asynchronous Model-Based Optimization
  • Marwedel/etal/2017a - Memory-Aware Optimization of Embedded Software for Multiple Objectives
  • Neugebauer/etal/2017a - Quality Evaluation Strategies for Approximate Computing in Embedded Systems
  • Richter/2017b - mlrHyperopt: Effortless and collaborative hyperparameter optimization experiments
  • Bischl/etal/2016a - mlr: Machine Learning in R
  • Kotthaus/etal/2016a - Resource-Aware Scheduling Strategies for Parallel Machine Learning R Programs through RAMBO
  • Richter/etal/2016a - mlrMBO: A Toolbox for Model-Based Optimization of Expensive Black-Box Functions
  • Richter/etal/2016b - Faster Model-Based Optimization through Resource-Aware Scheduling Strategies
  • Bischl/etal/2015a - BatchJobs and BatchExperiments: Abstraction Mechanisms for Using R in Batch Environments
  • Kotthaus/2015a - Performance Analysis for Parallel R Programs: Towards Efficient Resource Utilization
  • Kotthaus/etal/2015a - Distributed Performance Analysis for R
  • Kotthaus/etal/2014a - Runtime and Memory Consumption Analyses for Machine Learning R Programs
  • Kotthaus/etal/2014b - Dynamic Page Sharing Optimization for the R Language
  • Kotthaus/etal/2014c - Performance Analysis for R: Towards a Faster R Interpreter
  • Lang/etal/2014a - Automatic Model Selection for High-Dimensional Survival Analysis
  • Lee/etal/2014a - Robust Selection of Cancer Survival Signatures from High-Throughput Genomic Data Using Two-Fold Subsampling
  • Ahrens/etal/2013a - Detection of Patient Subgroups with Differential Expression in Omics Data: A Comprehensive Comparison of Univariate Measures
  • Cordes/etal/2013a - Automatic Extraction of Pipeline Parallelism for Embedded Heterogeneous Multi-Core Platforms
  • Cordes/etal/2013b - Automatic Extraction of Multi-Objective Aware Parallelism for Heterogeneous MPSoCs
  • Cordes/etal/2013c - Automatic Extraction of Task-Level Parallelism for Heterogeneous MPSoC
  • Kotthaus/etal/2013a - Runtime and Memory Consumption Analyses for Machine Learning R Programs
  • Lang/etal/2013a - Automatic model selection for high-dimensional survival analysis
  • Bischl/etal/2012a - Computing on High Performance Clusters with R: Packages BatchJobs and BatchExperiments
  • Kotthaus/etal/2012a - A JVM-based Compiler Strategy for the R Language
  • Lohr/etal/2012a - Optimal strategies for sequential validation of significant features from high-dimensional genomic data
  • Plazar/etal/2012a - WCET-aware Static Locking of Instruction Caches
  • Falk/etal/2011a - WCET-driven Cache-aware Code Positioning
  • Kammers/etal/2011a - Survival Models with Preclustered Gene Groups as Covariates
  • Plazar/etal/2011a - WCET-driven Branch Prediction aware Code Positioning

Disserations:

Richter/2020b Richter, Jakob. Extending Model-Based Optimization with Resource-Aware Parallelization and for Dynamic Optimization Problems. pages 151, Technische Universität Dortmund, 2020. PDF-Symbol LaTeX Symbol Green Arrow


Kotthaus/2018a Kotthaus, Helena. Methods for Efficient Resource Utilization in Statistical Machine Learning Algorithms. TU Dortmund University, Dortmund, Department of Computer Science, 2018. LaTeX Symbol Green Arrow


Neugebauer/2018a Neugebauer, Olaf. Efficient Implementation of Resource-Constrained Cyber-Physical Systems Using Multi-Core Parallelism. TU Dortmund, Dortmund, Germany, Department of Computer Science, 2018. LaTeX Symbol Green Arrow


Holzkamp/2017a Holzkamp, Olivera. Memory-Aware Mapping Strategies for Heterogeneous MPSoC Systems. TU Dortmund, 2017. LaTeX Symbol Green Arrow


Lang/2015a Lang, Michel. Automatische Modellselektion in der Überlebenszeitanalyse. TU Dortmund, Dortmund, 2015. LaTeX Symbol Green Arrow


Cordes/2013d Cordes, Daniel A.. Automatic Parallelization for Embedded Multi-Core Systems using High-Level Cost Models. TU Dortmund, Department of Computer Science, 2013. LaTeX Symbol


Netzer/2013a Christian Netzer. Vorhersage der Überlebenswahrscheinlichkeit für Patientenuntergruppen mit hochdimensionalen Daten am Beispiel zweier Lungenkrebskohorten. TU Dortmund, 2013. LaTeX Symbol


Kammers/2012a Kammers, Kai. Survival models with gene groups as covariates. TU Dortmund, 2012. LaTeX Symbol Green Arrow


Plazar/2012b Plazar, Sascha. Memory-based Optimization Techniques for Real-Time Systems. TU Dortmund, Department of Computer Science, Dortmund, Germany, 2012. LaTeX Symbol


  • Richter/2020b - Extending Model-Based Optimization with Resource-Aware Parallelization and for Dynamic Optimization Problems
  • Kotthaus/2018a - Methods for Efficient Resource Utilization in Statistical Machine Learning Algorithms
  • Neugebauer/2018a - Efficient Implementation of Resource-Constrained Cyber-Physical Systems Using Multi-Core Parallelism
  • Holzkamp/2017a - Memory-Aware Mapping Strategies for Heterogeneous MPSoC Systems
  • Lang/2015a - Automatische Modellselektion in der Überlebenszeitanalyse
  • Cordes/2013d - Automatic Parallelization for Embedded Multi-Core Systems using High-Level Cost Models
  • Netzer/2013a - Vorhersage der Überlebenswahrscheinlichkeit für Patientenuntergruppen mit hochdimensionalen Daten am Beispiel zweier Lungenkrebskohorten
  • Kammers/2012a - Survival models with gene groups as covariates
  • Plazar/2012b - Memory-based Optimization Techniques for Real-Time Systems

Final Theses:

  • Lang/2019a - Ablaufplanungsverfahren zur effizienten Ressourcennutzung bei der Algorithmenkonfiguration von maschinellen Lernverfahren
  • Bommert/2016a - Stabile Variablenselektion in der Klassifikation
  • Lang/2016a - Ressourcengewahre Ablaufplanungsverfahren für modellbasierte Optimierungen
  • richter/2015a - Modellbasierte Hyperparameteroptimierung für maschinelle Lernverfahren auf großen Daten

Preliminary Work:

Kammers/Rahnenfuehrer/2010a Kammers, K. and Rahnenführer, J.. Improved Interpretability of Survival Models with Gene Groups as Covariates. TU Dortmund, Department of Statistics, 2010. LaTeX Symbol


Lohr/etal/2010a Miriam Lohr AND Patricio Godoy AND Jan G Hengstler AND Jörg Rahnenführer AND Marco Grzegorczyk. Extracting differential regulatory sub-networks from genome-wide microarray expression data. In Proceedings of the Seventh International Workshop on Computational Systems Biology (WCSB 2010), Luxembourg, 2010. LaTeX Symbol


Plazar/etal/2010a Plazar, Sascha and Marwedel, Peter and Rahnenführer, Jörg. Optimizing Execution Runtimes of R Programs. In Book of Abstracts of ISBIS-2010 (International Symposium on Business and Industrial Statistics), Portorose, Slovenia, 2010. LaTeX Symbol


Alexa/2009a Adrian Alexa. topGO: Enrichment analysis for Gene Ontology. In \urlhttp://www.bioconductor.org/packages/release/bioc/html/topGO.html, 2009. LaTeX Symbol


Lokuciejewski/etal/2009a Lokuciejewski, Paul and Gedikli, Fatih and Marwedel, Peter and Morik, Katharina. Automatic WCET Reduction by Machine Learning Based Heuristics for Function Inlining. In SMART '09: Proc. of the 3rd Workshop on Statistical and Machine Learning Approaches to Architecture and Compilation, pages 1--15, Paphos / Cyprus, 2009. LaTeX Symbol


Podwojski/etal/2009a Katharina Podwojski and Arno Fritsch and Daniel C Chamrad and Wolfgang Paul and Barbara Sitek and Kai Stühler and Petra Mutzel and Christian Stephan and Helmut E Meyer and Wolfgang Urfer and Katja Ickstadt and Jörg Rahnenführer. Retention time alignment algorithms for LC/MS data must consider non-linear shifts. In Bioinformatics, Vol. 25, No. 6, pages 758--764, Fakultät Statistik, Technische Universität Dortmund, 44221 Dortmund, Germany. katharina.podwojski@tu-dortmund.de, 2009. LaTeX Symbol Green Arrow


Bogojeska/etal/2008a Jasmina Bogojeska and Adrian Alexa and André Altmann and Thomas Lengauer and Jörg Rahnenführer. Rtreemix: an R package for estimating evolutionary pathways and genetic progression scores. In Bioinformatics, Vol. 24, No. 20, pages 2391--2392, Max Planck Institute for Informatics, Saarbrücken, Germany. jasmina@mpi-inf.mpg.de, 2008. LaTeX Symbol Green Arrow


Plazar/etal/2008a Plazar, Sascha and Lokuciejewski, Paul and Marwedel, Peter. A Retargetable Framework for Multi-objective WCET-aware High-level Compiler Optimizations. In Proceedings of The 29th IEEE Real-Time Systems Symposium (RTSS) WiP, pages 49--52, Barcelona / Spain, 2008. LaTeX Symbol


Alexa/etal/2006a Adrian Alexa and Jörg Rahnenführer and Thomas Lengauer. Improved scoring of functional groups from gene expression data by decorrelating GO graph structure. In Bioinformatics, Vol. 22, No. 13, pages 1600--1607, Max-Planck-Institute for Informatics Stuhlsatzenhausweg 85, D-66123 Saarbrücken, Germany. alexa@mpi-sb.mpg.de, 2006. LaTeX Symbol Green Arrow


Falk/etal/2006b Falk, Heiko and Wagner, Jens and Schaefer, André. Use of a Bit-true Data Flow Analysis for Processor-Specific Source Code Optimization. In 4th IEEE Workshop on Embedded Systems for Real-Time Multimedia (ESTIMedia), pages 133--138, Seoul/Korea, 2006. LaTeX Symbol


Falk/Schwarzer/2006a Falk, Heiko and Schwarzer, Martin. Loop Nest Splitting for WCET-Optimization and Predictability Improvement. In 4th IEEE Workshop on Embedded Systems for Real-Time Multimedia (ESTIMedia), pages 115--120, Seoul/Korea, 2006. LaTeX Symbol


Yin/etal/2006a Junming Yin and Niko Beerenwinkel and Jörg Rahnenführer and Thomas Lengauer. Model selection for mixtures of mutagenetic trees. In Stat Appl Genet Mol Biol, Vol. 5, pages Article17, Department of EECS, University of California, Berkeley, CA, USA. junming@cs.berkeley.edu, 2006. LaTeX Symbol Green Arrow


Rahnenfuehrer/etal/2005a Jörg Rahnenführer and Niko Beerenwinkel and Wolfgang A Schulz and Christian Hartmann and Andreas von Deimling and Bernd Wullich and Thomas Lengauer. Estimating cancer survival and clinical outcome based on genetic tumor progression scores. In Bioinformatics, Vol. 21, No. 10, pages 2438--2446, Max-Planck Institute for Informatics, Stuhlsatzenhausweg 85, D-66123 Saarbrücken, Germany. rahnenfj@mpi-sb.mpg.de, 2005. LaTeX Symbol Green Arrow


  • Kammers/Rahnenfuehrer/2010a - Improved Interpretability of Survival Models with Gene Groups as Covariates
  • Lohr/etal/2010a - Extracting differential regulatory sub-networks from genome-wide microarray expression data
  • Plazar/etal/2010a - Optimizing Execution Runtimes of R Programs
  • Alexa/2009a - topGO: Enrichment analysis for Gene Ontology
  • Lokuciejewski/etal/2009a - Automatic WCET Reduction by Machine Learning Based Heuristics for Function Inlining
  • Podwojski/etal/2009a - Retention time alignment algorithms for LC/MS data must consider non-linear shifts
  • Bogojeska/etal/2008a - Rtreemix: an R package for estimating evolutionary pathways and genetic progression scores
  • Plazar/etal/2008a - A Retargetable Framework for Multi-objective WCET-aware High-level Compiler Optimizations
  • Alexa/etal/2006a - Improved scoring of functional groups from gene expression data by decorrelating GO graph structure
  • Falk/etal/2006b - Use of a Bit-true Data Flow Analysis for Processor-Specific Source Code Optimization
  • Falk/Schwarzer/2006a - Loop Nest Splitting for WCET-Optimization and Predictability Improvement
  • Yin/etal/2006a - Model selection for mixtures of mutagenetic trees
  • Rahnenfuehrer/etal/2005a - Estimating cancer survival and clinical outcome based on genetic tumor progression scores