Sebastian Buschjäger

I am a postdoctoral researcher and the Coordinator for Resource-Aware Machine Learning at the Lamarr Institute. Before that, I was a Ph.D. student in the artificial intelligence group at TU Dortmund University in Germany. There, I participated in the Collaborative Research Center SFB 876, project A1 in which I studied novel machine learning methods for embedded systems and small devices. I received my Ph.D. in 2022 with distinction (summa cum laude) titled “Ensemble learning with discrete classifiers on small devices.” Apart from research, I play badminton, run, and watch too many movies.

My formal CV is available in German or English.

research topics

Machine Learning Ensemble learning, model application, submodular functions
Computer Architecture Architecture-dependent optimizations and code generation, custom architectures/FPGAs, embedded devices

selected publications

ECML

Joint leaf-refinement and ensemble pruning through L\(_\mbox1\)regularization

Sebastian Buschjäger, and Katharina Morik

Data Min. Knowl. Discov., 2023

PAPER Code Poster Slides Bibtex

@article{Buschjaeger/Morik/2023,
  author = {Buschj{\"{a}}ger, Sebastian and Morik, Katharina},
  title = {Joint leaf-refinement and ensemble pruning through L\({}_{\mbox{1}}\) regularization},
  number = {3},
  pages = {1230--1261},
  volume = {37},
  bibsource = {dblp computer science bibliography, https://dblp.org},
  biburl = {https://dblp.org/rec/journals/datamine/BuschjagerM23.bib},
  year = {2023},
  doi = {10.1007/s10618-023-00921-z},
  journal = {Data Min. Knowl. Discov.},
  timestamp = {Sat, 13 May 2023 01:07:00 +0200},
  url = {https://doi.org/10.1007/s10618-023-00921-z},
}

PhD Thesis

Ensemble learning with discrete classifiers on small devices

Sebastian Buschjäger

Technical University of Dortmund, Germany, 2022

PAPER Bibtex

@phdthesis{Buschjaeger/2022,
  author = {Buschj{\"{a}}ger, Sebastian},
  title = {Ensemble learning with discrete classifiers on small devices},
  url = {http://hdl.handle.net/2003/41132},
  bibsource = {dblp computer science bibliography, https://dblp.org},
  biburl = {https://dblp.org/rec/phd/dnb/Buschjager22.bib},
  school = {Technical University of Dortmund, Germany},
  timestamp = {Fri, 20 Jan 2023 21:27:23 +0100},
  urn = {urn:nbn:de:101:1-2022111802423335923878},
  year = {2022},
}

AAAI

Shrub Ensembles for Online Classification

Sebastian Buschjäger, Sibylle Hess, and Katharina Morik

In Proceedings of the Thirty-Sixth AAAI Conference on Artificial Intelligence (AAAI-22), 2022

arXiv PAPER Code Poster Slides Bibtex

@inproceedings{Buschjaeger/etal/2022,
  author = {Buschjäger, Sebastian and Hess, Sibylle and Morik, Katharina},
  booktitle = {Proceedings of the Thirty-Sixth {AAAI} Conference on Artificial Intelligence (AAAI-22)},
  title = {Shrub Ensembles for Online Classification},
  year = {2022},
  publisher = {{AAAI} Press},
}

TECS
Efficient Realization of Decision Trees for Real-Time Inference

Kuan-Hsun Chen, ChiaHui Su, Christian Hakert, and 5 more authors

ACM Transactions on Embedded Computing Systems, Dec 2021

Abs PAPER Bibtex

For timing-sensitive edge applications, the demand for efficient lightweight machine learning solutions has increased recently. Tree ensembles are among the state-of-the-art in many machine learning applications. While single decision trees are comparably small, an ensemble of trees can have a significant memory footprint leading to cache locality issues, which are crucial to performance in terms of execution time. In this work, we analyze memory-locality issues of the two most common realizations of decision trees, i.e. native and if-else trees. We highlight, that both realizations demand a more careful memory layout to improve caching behavior and maximize performance. We adopt a probabilistic model of decision tree inference to find the best memory layout for each tree at the application layer. Further, we present an efficient heuristic to take architecture-dependent information into account thereby optimizing the given ensemble for a target computer architecture. Our code-generation framework, which is freely available on an open-source repository, produces optimized code sessions while preserving the structure and accuracy of the trees. With several real-world data sets, we evaluate the elapsed time of various tree realizations on server hardware as well as embedded systems for Intel and ARM processors. Our optimized memory layout achieves a reduction in execution time up to 75 % execution for server-class systems, and up to 70 % for embedded systems, respectively.
@article{Chen/etal/2021, author = {Chen, Kuan-Hsun and Su, ChiaHui and Hakert, Christian and Buschj\"{a}ger, Sebastian and Lee, Chao-Lin and Lee, Jenq-Kuen and Morik, Katharina and Chen, Jian-Jia}, title = {Efficient Realization of Decision Trees for Real-Time Inference}, doi = {10.1145/3508019}, issn = {1539-9087}, url = {https://doi.org/10.1145/3508019}, address = {New York, NY, USA}, journal = {{ACM} Transactions on Embedded Computing Systems}, keywords = {Random Forest, Optimized Memory Layout, Real-Time Inference, Cache-Aware Optimization, Architecture-Aware Realization}, month = dec, publisher = {Association for Computing Machinery}, year = {2021}, }

arXiv

There is no Double-Descent in Random Forests

Sebastian Buschjäger, and Katharina Morik

Dec 2021

arXiv PAPER Bibtex

@misc{Buschjaeger/morik/2021c,
  title = {There is no Double-Descent in Random Forests},
  author = {Buschjäger, Sebastian and Morik, Katharina},
  year = {2021},
  eprint = {2111.04409},
  archiveprefix = {arXiv},
  primaryclass = {cs.LG},
}