Pieter Gijsbers, MSc

Research output

Content available in repository 2017 2019 2021 2022 2024

• 5 Conference contribution
• 3 Article
• 1 Paper
• 1 Other contribution
• 1 More
  • 1 Phd Thesis 1 (Research TU/e / Graduation TU/e)

Content available in repository

Content available in repository

Search results

• 2024

  Croissant: A Metadata Format for ML-Ready Datasets.

  Akhtar, M., Benjelloun, O., Conforti, C., Gijsbers, P., Giner-Miguelez, J., Jain, N., Kuchnik, M., Lhoest, Q., Marcenac, P., Maskey, M., Mattson, P., Oala, L., Ruyssen, P., Shinde, R., Simperl, E., Thomas, G., Tykhonov, S., Vanschoren, J., van der Velde, J. & Vogler, S. & 1 others, Wu, C.-J., 9 Jun 2024, DEEM '24: Proceedings of the Eighth Workshop on Data Management for End-to-End Machine Learning. Association for Computing Machinery, Inc, p. 1-6 6 p.

  Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Academic › peer-review

  Open Access

  File

  • Metadata Format 100%
  • Machine Learning 100%
  • Learning System 100%
  • Learning Framework 20%
  • Critical Resource 20%
  5 Citations (Scopus)

  20 Downloads (Pure)
• 2021

  GAMA: A General Automated Machine Learning Assistant

  Gijsbers, P. & Vanschoren, J., Feb 2021, Machine Learning and Knowledge Discovery in Databases. Applied Data Science and Demo Track - European Conference, ECML PKDD 2020, Proceedings. Dong, Y., Ifrim, G., Saunders, C., van Hoecke, S. & Mladenic, D. (eds.). Springer, p. 560-564 5 p. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); vol. 12461 LNAI).

  Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Academic › peer-review

  • Automated Machine Learning 100%
  • Search Algorithm 22%
  • Postprocessing 11%
  • Machine Learning 11%
  • Benchmarking 11%
  17 Citations (Scopus)

• Meta-learning for symbolic hyperparameter defaults

  Gijsbers, P., Vanschoren, J., Pfisterer, F., van Rijn, J. N. & Bischl, B., Jul 2021, GECCO 2021 Companion - Proceedings of the 2021 Genetic and Evolutionary Computation Conference Companion: Proceedings of the Genetic and Evolutionary Computation Conference Companion. Association for Computing Machinery, Inc, p. 151-152 2 p.

  Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Academic › peer-review

  • Machine Learning Algorithm 100%
  • Machine Learning 50%
  • Performance Model 50%
  • Optimization Problem 50%
  • Optimal Algorithm 50%
  5 Citations (Scopus)

• OpenML Benchmarking Suites

  Bischl, B., Casalicchio, G., Feurer, M., Gijsbers, P., Hutter, F., Lang, M., Mantovani, R. G., Rijn, J. N. V. & Vanschoren, J., 2021, Proceedings of the NeurIPS 2021 Datasets and Benchmarks Track.

  Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Academic › peer-review
• 2017

  Layered TPOT : speeding up tree-based pipeline optimization

  Gijsbers, P., Vanschoren, J. & Olson, R., 22 Sept 2017, Proceedings of the International Workshop on Automatic Selection, Configuration and Composition of Machine Learning Algorithms (AutoML 2017), 10 August 2017, Sydney, Australia: Collocated with ECMLPKDD 2017. Brazdil, P., Vanschoren, J., Hutter, F. & Hoos, H. (eds.). CEUR-WS.org, p. 49-68 20 p. (CEUR Workshop Proceedings; vol. 1998).

  Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Academic › peer-review

  Open Access

  File

  • Machine Learning 100%
  • Large Data Set 100%
  • Genetic Programming 100%
  • Evolutionary Algorithms 100%
  • Automated Machine Learning 100%
  4 Citations (Scopus)

  168 Downloads (Pure)