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Publications
2024
- 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
- Published
AutoML in Heavily Constrained Applications
Neutatz, F., Lindauer, M. & Abedjan, Z., Jul 2024, In: VLDB Journal. 33, 4, p. 957–979 23 p.Research output: Contribution to journal › Article › Research › peer review
- 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) NeurIPS Workshop on Time Series in the Age of Large Models. (ArXiv).Research output: Chapter in book/report/conference proceeding › Conference contribution › Research › peer review
2023
- E-pub ahead of print
Contextualize Me – The Case for Context in Reinforcement Learning
Benjamins, C., Eimer, T., Schubert, F. G., Mohan, A., Döhler, S., Biedenkapp, A., Rosenhahn, B., Hutter, F. & Lindauer, M., 5 Jun 2023, (E-pub ahead of print) In: Transactions on Machine Learning Research. 2023, 6Research output: Contribution to journal › Article › Research › peer review
- Published
POLTER: Policy Trajectory Ensemble Regularization for Unsupervised Reinforcement Learning
Schubert, F., Benjamins, C., Döhler, S., Rosenhahn, B. & Lindauer, M., Apr 2023, In: Transactions on Machine Learning Research. 2023, 4Research output: Contribution to journal › Article › Research › peer review