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Institute of Artificial Intelligence

Organisational unit: Institute

Type of address: Visitor address.
Appelstraße 9a
30167
Hannover
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1 - 20 out of 130Page size: 20

Publications

  1. 2024
  2. Accepted/In press

    Towards Enhancing Predictive Representations using Relational Structure in Reinforcement Learning

    Mohan, A. & Lindauer, M., 30 Sept 2024, (Accepted/In press) The 17th European Workshop on Reinforcement Learning (EWRL 2024).

    Research output: Chapter in book/report/conference proceeding › Conference abstract › Research › peer review

  3. Published

    AMLTK: A Modular AutoML Toolkit in Python

    Bergman, E., Feurer, M., Bahram, A., Rezaei, A., Purucker, L., Segel, S., Lindauer, M. & Eggensperger, K., 14 Aug 2024, In: The Journal of Open Source Software. 9, 100, 4 p., 6367.

    Research output: Contribution to journal › Article › Research › peer review

  4. Published

    Disentangling Dialect from Social Bias via Multitask Learning to Improve Fairness.

    Spliethöver, M., Menon, S. N. & Wachsmuth, H., Aug 2024, p. 9294-9313.

    Research output: Contribution to conference › Paper › Research › peer review

  5. Accepted/In press

    Hyperparameter Importance Analysis for Multi-Objective AutoML

    Theodorakopoulos, D., Stahl, F. & Lindauer, M., 4 Jul 2024, (Accepted/In press) Proceedings of the european conference on AI (ECAI).

    Research output: Chapter in book/report/conference proceeding › Conference contribution › Research › peer review

  6. Published

    AutoML in Heavily Constrained Applications

    Neutatz, F., Lindauer, M. & Abedjan, Z., Jul 2024, In: VLDB Journal. 33, p. 957–979

    Research output: Contribution to journal › Article › Research › peer review

  7. E-pub ahead of print

    Position: Why We Must Rethink Empirical Research in Machine Learning

    Herrmann, M., Lange, F. J. D., Eggensperger, K., Casalicchio, G., Wever, M., Feurer, M., Rügamer, D., Hüllermeier, E., Boulesteix, A. & Bischl, B., Jul 2024, (E-pub ahead of print) Proceedings of the international conference on machine learning.

    Research output: Chapter in book/report/conference proceeding › Contribution to book/anthology › Research › peer review

  8. E-pub ahead of print

    ALPBench: A Benchmark for Active Learning Pipelines on Tabular Data

    Margraf, V., Wever, M., Gilhuber, S., Tavares, G. M., Seidl, T. & Hüllermeier, E., 15 Jun 2024, (E-pub ahead of print).

    Research output: Working paper/Preprint › Preprint

  9. E-pub ahead of print

    Optimizing Time Series Forecasting Architectures: A Hierarchical Neural Architecture Search Approach

    Deng, D. & Lindauer, M., 10 Jun 2024, (E-pub ahead of print) (ArXiv).

    Research output: Working paper/Preprint › Preprint

  10. Published

    Analyzing the Use of Metaphors in News Editorials for Political Framing

    Sengupta, M., El Baff, R., Alshomary, M. & Wachsmuth, H., Jun 2024, Analyzing the Use of Metaphors in News Editorials for Political Framing. Mexico City, Mexico, p. 3621–3631

    Research output: Chapter in book/report/conference proceeding › Conference contribution › Research › peer review

  11. Published

    A School Student Essay Corpus for Analyzing Interactions of Argumentative Structure and Quality

    Stahl, M., Michel, N., Kilsbach, S., Schmidtke, J., Rezat, S. & Wachsmuth, H., Jun 2024, Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers). p. 2661–2674

    Research output: Chapter in book/report/conference proceeding › Conference contribution › Research › peer review

  12. Published

    Exploring LLM Prompting Strategies for Joint Essay Scoring and Feedback Generation

    Stahl, M., Biermann, L., Nehring, A. & Wachsmuth, H., Jun 2024, Proceedings of the 19th Workshop on Innovative Use of NLP for Building Educational Applications (BEA 2024). p. 283–298

    Research output: Chapter in book/report/conference proceeding › Conference contribution › Research › peer review

  13. Published

    Verfahren zum Trainieren eines Algorithmus des maschinellen Lernens durch ein bestärkendes Lernverfahren

    Eimer, T., Hutter, F., Lindauer, M. & Biedenkapp, A., 4 Apr 2024, IPC No. G06N20/00, Patent No. DE102022210480A1, 4 Oct 2022, Priority date 4 Oct 2022, Priority No. DE202210210480A

    Research output: Patent

  14. E-pub ahead of print

    Towards Leveraging AutoML for Sustainable Deep Learning: A Multi-Objective HPO Approach on Deep Shift Neural Networks

    Hennig, L., Tornede, T. & Lindauer, M., 2 Apr 2024, (E-pub ahead of print) 5th Workshop on practical ML for limited/low resource settings.

    Research output: Chapter in book/report/conference proceeding › Conference contribution › Research › peer review

  15. Accepted/In press

    auto-sktime: Automated Time Series Forecasting

    Zöller, M., Lindauer, M. & Huber, M., Apr 2024, (Accepted/In press) Proceedings of the 18TH Learning and Intelligent Optimization Conference (LION).

    Research output: Chapter in book/report/conference proceeding › Conference contribution › Research › peer review

  16. E-pub ahead of print

    Structure in Deep Reinforcement Learning: A Survey and Open Problems

    Mohan, A., Zhang, A. & Lindauer, M., Apr 2024, (E-pub ahead of print) In: Journal of Artificial Intelligence Research.

    Research output: Contribution to journal › Article › Research › peer review

  17. Published

    Who Determines What Is Relevant? Humans or AI? Why Not Both? A spectrum of human–artificial intelligence collaboration in assessing relevance

    Faggioli, G., Dietz, L., Clarke, C. L. A., Demartini, G., Hagen, M., Hauff, C., Kando, N., Kanoulas, E., Potthast, M., Stein, B. & Wachsmuth, H., 25 Mar 2024, In: Communications of the ACM. 67, 4, p. 31-34 4 p.

    Research output: Contribution to journal › Article › Research › peer review

  18. Published

    Interactive Hyperparameter Optimization in Multi-Objective Problems via Preference Learning

    Giovanelli, J., Tornede, A., Tornede, T. & Lindauer, M., 24 Mar 2024, Proceedings of the 38th conference on AAAI. Wooldridge, M., Dy, J. & Natarajan, S. (eds.). p. 12172-12180 9 p. (Proceedings of the AAAI Conference on Artificial Intelligence; vol. 38, no. 11).

    Research output: Chapter in book/report/conference proceeding › Conference contribution › Research › peer review

  19. E-pub ahead of print

    AutoML in the Age of Large Language Models: Current Challenges, Future Opportunities and Risks

    Tornede, A., Deng, D., Eimer, T., Giovanelli, J., Mohan, A., Ruhkopf, T., Segel, S., Theodorakopoulos, D., Tornede, T., Wachsmuth, H. & Lindauer, M., 9 Feb 2024, (E-pub ahead of print) In: Transactions on Machine Learning Research.

    Research output: Contribution to journal › Article › Research › peer review

  20. E-pub ahead of print

    Information Leakage Detection through Approximate Bayes-optimal Prediction

    Gupta, P., Wever, M. & Hüllermeier, E., 25 Jan 2024, (E-pub ahead of print).

    Research output: Working paper/Preprint › Preprint

  21. Published

    ARLBench: Flexible and Efficient Benchmarking for Hyperparameter Optimization in Reinforcement Learning

    Becktepe, J., Dierkes, J., Benjamins, C., Mohan, A., Salinas, D., Rajan, R., Hutter, F., Hoos, H., Lindauer, M. & Eimer, T., 2024, 17th European Workshop on Reinforcement Learning (EWRL 2024).

    Research output: Chapter in book/report/conference proceeding › Conference contribution › Research › peer review

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