InstituteStaff
Alexander Tornede
Alexander Tornede
Address
Appelstr. 9A
30167 Hannover
Building
Room
Alexander Tornede
Address
Appelstr. 9A
30167 Hannover
Building
Room

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


2023


Tornede, T., Tornede, A., Fehring, L., Gehring, L., Graf, H., Hanselle, J., Mohr, F., & Wever, M. (2023). PyExperimenter: Easily distribute experiments and track results.

arxiv.org/pdf/2301.06348.pdf


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)

arxiv.org/abs/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.

doi.org/10.1007/s40194-022-01339-9

Ruhkopf, T., Mohan, A., Deng, D., Tornede, A., Hutter, F., & Lindauer, M. (2022). MASIF: Meta-learned Algorithm Selection using Implicit Fidelity Information.

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.

doi.org/10.1613/jair.1.13676

Tornede, A., Gehring, L., Tornede, T., Wever, M., & Hüllermeier, E. (2022). Algorithm selection on a meta level. Machine learning.

doi.org/10.1007/s10994-022-06161-4

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

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 (Eds.), Advances in Knowledge Discovery and Data Mining - 25th Pacific-Asia Conference, PAKDD 2021, Proceedings: PAKDD 2021: Advances in Knowledge Discovery and Data Mining (Vol. 12712, pp. 152-163). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 12712 LNAI).

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)

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].

doi.org/10.1109/tpami.2021.3056950

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

dl.acm.org/doi/pdf/10.1145/3449639.3459395

Tornede, T., Tornede, A., Hanselle, J., Wever, M., Mohr, F., & Hüllermeier, E. (2021). Towards Green Automated Machine Learning: Status Quo and Future Directions.

arxiv.org/abs/2111.05850

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].

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 (Eds.), KI 2020: Advances in Artificial Intelligence - 43rd German Conference on AI, Proceedings (Vol. 12325, pp. 59-72). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 12325 LNAI).

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 (Eds.), Discovery Science - 23rd International Conference, DS 2020, Proceedings: DS 2020: Discovery Science (Vol. 12323, pp. 309-324). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 12323 LNAI).

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

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).

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

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

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

ris.uni-paderborn.de/download/15011/17060/ci_workshop_tornede.pdf

Tornede, T., Tornede, A., Wever, M., Mohr, F., & Hüllermeier, E. (2019). AutoML for Predictive Maintenance: One Tool to RUL them all. In IoT Streams 2020: IoT Streams for Data-Driven Predictive Maintenance and IoT, Edge, and Mobile for Embedded Machine Learning

link.springer.com/chapter/10.1007/978-3-030-66770-2_8

Wever, M., Mohr, F., Tornede, A., & Hüllermeier, E. (2019). Automating Multi-Label Classification Extending ML-Plan. In ICML 2019 Workshop AutoML

ris.uni-paderborn.de/download/10232/13177/Automating_MultiLabel_Classification_Extending_ML-Plan.pdf


Projects

  • ERC Starting Grant: Interactive and Explainable Human-Centered AutoML
    Trust and interactivity are key factors in the future development and use of automated machine learning (AutoML), supporting developers and researchers in determining powerful task-specific machine learning pipelines, including pre-processing, predictive algorithm, their hyperparameters and--if applicable--the architecture design of deep neural networks. Although AutoML is ready for its prime time after it achieved impressive results in several machine learning (ML) applications and its efficiency improved by several orders of magnitudes in recent years, democratization of machine learning via AutoML is still not achieved. In contrast to previously purely automation-centered approaches, ixAutoML is designed with human users at its heart in several stages. First of all, the foundation of trustful use of AutoML will be based on explanations of its results and processes. Therefore, we aim for: (i) Explaining static effects of design decisions in ML pipelines optimized by state-of-the-art AutoML systems. (ii) Explaining dynamic AutoML policies for temporal aspects of dynamically adapted hyperparameters while ML models are trained. These explanations will be the base for allowing interactions, bringing the best of two worlds together: human intuition and generalization capabilities for complex systems, and efficiency of systematic optimization approaches for AutoML. Concretely, we aim for: (iii) Enabling interactions between humans and AutoML by taking human's latent knowledge into account and learning when to interact. (iv) Building first ixAutoML prototypes and showing its efficiency in the context of Industry 4.0. Perfectly aligned with the EU's AI strategy and recent efforts on interpretability in the ML community, we strongly believe that this timely human-centered ixAutoML will have a substantial impact on the democratization of machine learning.
    Led by: Prof. Dr. Marius Lindauer
    Team: AutoML
    Year: 2022
    Funding: EU
    Duration: 2022-2027