Institut
Alexander Tornede
Dr. rer. nat. Alexander Tornede
Adresse
Welfengarten 1
30167 Hannover
Gebäude
Raum
Dr. rer. nat. Alexander Tornede
Adresse
Welfengarten 1
30167 Hannover
Gebäude
Raum

My Vision

Artificial intelligence is no longer science fiction, but can be found all around us. Be it the personal assistant on your smartphone, the driving assistant of your car, or the setup of your favorite clothing store - chances are that all of these leverage AI and/or have been created with the help of AI. As such, it might seem that AI is democratized and more or less easily available as a supporting tool for researchers and businesses. However, the majority of AI solutions present in our daily life has been developed by and are only available to large companies due to the associated personnel costs of employing data scientists.

Considering the impressive advances AI achieved in the recent years in fields such as natural language processing, image processing and product recommendation, this is unfortunate. Instead, I believe that everyone should be enabled to leverage these approaches to improve the world we live in. Therefore, my goal is to democratize AI and in particular machine learning (ML) by research in the area of automated machine learning (AutoML). I aim at enabling researchers from other disciplines and companies, which might not be able to afford large data science teams, to leverage ML through the means of AutoML tools.

To achieve this, I research on interactive and explainable AutoML targeted at supporting data scientists and domain experts in leveraging ML for their particular application in an intuitive, human-centered and explainable way. Together with my awesome colleagues, we work hard to make existing AutoML tools easier to use and understand by non-experts and in particular improve the understandability of the underlying AutoML process. We hope that this will demystify AutoML, such that non-expert users can interact with AutoML tools by injecting domain knowledge and tailoring the search process to their needs.

Research Interests

My interests are centered around different topics in machine learning and artificial intelligence, and in particular the automation thereof, called automated machine learning (AutoML). I am most interested in the following topics:

  • Interactive and Explainable AutoML
  • Green AutoML
  • Algorithm Selection
  • Applications to health/medicine

Curriculum Vitae

Publications

Zeige Ergebnisse 1 - 27 von 27

2024


Giovanelli, J., Tornede, A., Tornede, T., & Lindauer, M. (Angenommen/Im Druck). Interactive Hyperparameter Optimization in Multi-Objective Problems via Preference Learning. in Proceedings of the Thirty-Eighth Conference on Artificial Intelligence (AAAI'24)

2023


Hanselle, J., Hüllermeier, E., Mohr, F., Ngomo, A. C. N., Sherif, M. A., Tornede, A., & Wever, M. (2023). Configuration and Evaluation. in On-The-Fly Computing -- Individualized IT-services in dynamic markets https://doi.org/10.5281/zenodo.8068466
Ruhkopf, T., Mohan, A., Deng, D., Tornede, A., Hutter, F., & Lindauer, M. (2023). MASIF: Meta-learned Algorithm Selection using Implicit Fidelity Information. Transactions on Machine Learning Research. https://openreview.net/forum?id=5aYGXxByI6
Segel, S., Graf, H., Tornede, A., Bischl, B., & Lindauer, M. (Angenommen/Im Druck). Symbolic Explanations for Hyperparameter Optimization. in AutoML Conference 2023 PMLR. https://doi.org/10.5281/zenodo.8123425
Tornede, A. (2023). Advanced Algorithm Selection with Machine Learning: Handling Large Algorithm Sets, Learning From Censored Data, and Simplyfing Meta Level Decisions. [Dissertation, Universität Paderborn]. https://doi.org/10.17619/UNIPB/1-1780
Tornede, A., Gehring, L., Tornede, T., Wever, M., & Hüllermeier, E. (2023). Algorithm selection on a meta level. Machine learning, 112(4), 1253-1286. https://doi.org/10.1007/s10994-022-06161-4
Tornede, A., Deng, D., Eimer, T., Giovanelli, J., Mohan, A., Ruhkopf, T., Segel, S., Theodorakopoulos, D., Tornede, T., Wachsmuth, H., & Lindauer, M. (2023). AutoML in the Age of Large Language Models: Current Challenges, Future Opportunities and Risks. https://doi.org/10.48550/arXiv.2306.08107
Tornede, T., Tornede, A., Fehring, L., Gehring, L., Graf, H., Hanselle, J., Mohr, F., & Wever, M. (2023). PyExperimenter: Easily distribute experiments and track results. Journal of Open Source Software. https://doi.org/10.21105/joss.05149
Tornede, T., Tornede, A., Hanselle, J., Wever, M., Mohr, F., & Hüllermeier, E. (2023). Towards Green Automated Machine Learning: Status Quo and Future Directions. Journal of Artificial Intelligence Research, 77, 427-457. https://doi.org/10.1613/jair.1.14340

2022


Fehring, L., Hanselle, J., & Tornede, A. (2022). HARRIS: Hybrid Ranking and Regression Forests for Algorithm Selection. in NeurIPS Workshop on Meta Learning (MetaLearn 2022) https://doi.org/10.48550/arXiv.2210.17341
Gevers, K., Tornede, A., Wever, M., Schöppner, V., & Hüllermeier, E. (2022). A comparison of heuristic, statistical, and machine learning methods for heated tool butt welding of two different materials. Welding in the world, 66(10), 2157-2170. https://doi.org/10.1007/s40194-022-01339-9
Schede, E., Brandt, J., Tornede, A., Wever, M., Bengs, V., Hüllermeier, E., & Tierney, K. (2022). A Survey of Methods for Automated Algorithm Configuration. Journal of Artificial Intelligence Research, 75, 425-487. https://doi.org/10.1613/jair.1.13676
Tornede, A., Bengs, V., & Hüllermeier, E. (2022). Machine Learning for Online Algorithm Selection under Censored Feedback. in Proceedings of the 36th AAAI Conference on Artificial Intelligence (S. 10370-10380) https://ojs.aaai.org/index.php/AAAI/article/view/21279

2021


Hanselle, J., Tornede, A., Wever, M., & Hüllermeier, E. (2021). Algorithm Selection as Superset Learning: Constructing Algorithm Selectors from Imprecise Performance Data. in K. Karlapalem, H. Cheng, N. Ramakrishnan, R. K. Agrawal, P. K. Reddy, J. Srivastava, & T. Chakraborty (Hrsg.), Advances in Knowledge Discovery and Data Mining - 25th Pacific-Asia Conference, PAKDD 2021, Proceedings: PAKDD 2021: Advances in Knowledge Discovery and Data Mining (Band 12712, S. 152-163). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Band 12712 LNAI). https://doi.org/10.1007/978-3-030-75762-5_13
Hüllermeier, E., Mohr, F., Tornede, A., & Wever, M. (2021). Automated Machine Learning, Bounded Rationality, and Rational Metareasoning. in ECML/PKDD workshop on Automating Data Science (ADS 2021) https://arxiv.org/abs/2109.04744
Mohr, F., Wever, M., Tornede, A., & Hüllermeier, E. (2021). Predicting Machine Learning Pipeline Runtimes in the Context of Automated Machine Learning. IEEE Transactions on Pattern Analysis and Machine Intelligence, 43(9), 3055-3066. [9347828]. https://doi.org/10.1109/tpami.2021.3056950
Tornede, T., Tornede, A., Wever, M., Mohr, F., & Hüllermeier, E. (2021). AutoML for Predictive Maintenance: One Tool to RUL Them All. in J. Gama, S. Pashami, A. Bifet, M. Sayed-Mouchawe, H. Fröning, F. Pernkopf, G. Schiele, & M. Blott (Hrsg.), IoT Streams for Data-Driven Predictive Maintenance and IoT, Edge, and Mobile for Embedded Machine Learning: Second International Workshop, IoT Streams 2020, and First International Workshop, ITEM 2020, Co-located with ECML/PKDD 2020, Ghent, Belgium, September 14-18, 2020, Revised Selected Papers (1 Aufl., S. 106–118). (Communications in Computer and Information Science; Band 1325). Springer, Cham. https://doi.org/10.1007/978-3-030-66770-2_8
Tornede, T., Tornede, A., Wever, M., & Hüllermeier, E. (2021). Coevolution of Remaining Useful Lifetime Estimation Pipelines for Automated Predictive Maintenance. in Proceedings of the Genetic and Evolutionary Computation Conference (S. 368-376). (ACM Conferences). Association for Computing Machinery (ACM). https://doi.org/10.1145/3449639.3459395
Wever, M., Tornede, A., Mohr, F., & Hüllermeier, E. (2021). AutoML for Multi-Label Classification: Overview and Empirical Evaluation. IEEE Transactions on Pattern Analysis and Machine Intelligence, 43(9), 3037-3054. [9321731]. https://doi.org/10.1109/TPAMI.2021.3051276

2020


Hanselle, J., Tornede, A., Wever, M., & Hüllermeier, E. (2020). Hybrid Ranking and Regression for Algorithm Selection. in U. Schmid, D. Wolter, & F. Klügl (Hrsg.), KI 2020: Advances in Artificial Intelligence - 43rd German Conference on AI, Proceedings (Band 12325, S. 59-72). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Band 12325 LNAI). https://doi.org/10.1007/978-3-030-58285-2_5
Tornede, A., Wever, M., & Hüllermeier, E. (2020). Extreme Algorithm Selection with Dyadic Feature Representation. in A. Appice, G. Tsoumakas, Y. Manolopoulos, & S. Matwin (Hrsg.), Discovery Science - 23rd International Conference, DS 2020, Proceedings: DS 2020: Discovery Science (Band 12323, S. 309-324). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Band 12323 LNAI). https://doi.org/10.1007/978-3-030-61527-7_21
Tornede, A., Wever, M., Werner, S., Mohr, F., & Hüllermeier, E. (2020). Run2Survive: A Decision-theoretic Approach to Algorithm Selection based on Survival Analysis. in Proceedings of The 12th Asian Conference on Machine Learning https://proceedings.mlr.press/v129/tornede20a.html
Tornede, A., Wever, M., & Hüllermeier, E. (2020). Towards Meta-Algorithm Selection. (4th Workshop on Meta-Learning at NeurIPS 2020). http://arxiv.org/abs/2011.08784v1
Wever, M., Tornede, A., Mohr, F., & Hüllermeier, E. (2020). LiBRe: Label-Wise Selection of Base Learners in Binary Relevance for Multi-Label Classification. in Lecture Notes in Computer Science: IDA 2020: Advances in Intelligent Data Analysis XVIII https://link.springer.com/chapter/10.1007/978-3-030-44584-3_44

2019


Mohr, F., Wever, M., Tornede, A., & Hüllermeier, E. (2019). From Automated to On-The-Fly Machine Learning. in INFORMATIK 2019: 50 Jahre Gesellschaft für Informatik–Informatik für Gesellschaft https://dl.gi.de/handle/20.500.12116/24989
Tornede, A., Wever, M., & Hüllermeier, E. (2019). Algorithm Selection as Recommendation: From Collaborative Filtering to Dyad Ranking. in 29th Workshop Computational Intelligence https://ris.uni-paderborn.de/download/15011/17060/ci_workshop_tornede.pdf
Wever, M., Mohr, F., Tornede, A., & Hüllermeier, E. (2019). Automating Multi-Label Classification Extending ML-Plan. in ICML 2019 Workshop AutoML https://ris.uni-paderborn.de/download/10232/13177/Automating_MultiLabel_Classification_Extending_ML-Plan.pdf