Showing results 1 -
9
out of 9
2024
Deng, D., & Lindauer, M. (2024).
Optimizing Time Series Forecasting Architectures: A Hierarchical Neural Architecture Search Approach. (ArXiv). Advance online publication.
https://arxiv.org/abs/2406.05088
Tornede, A., Deng, D., Eimer, T., Giovanelli, J.
, Mohan, A., Ruhkopf, T., Segel, S., Theodorakopoulos, D., Tornede, T., Wachsmuth, H., & Lindauer, M. (2024).
AutoML in the Age of Large Language Models: Current Challenges, Future Opportunities and Risks.
Transactions on Machine Learning Research. Advance online publication.
https://doi.org/10.48550/arXiv.2306.08107
2023
Bischl, B., Binder, M., Lang, M., Pielok, T., Richter, J., Coors, S., Thomas, J., Ullmann, T., Becker, M., Boulesteix, A.-L.
, Deng, D., & Lindauer, M. (2023).
Hyperparameter Optimization: Foundations, Algorithms, Best Practices and Open Challenges.
Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery,
13(2), Article e1484.
https://doi.org/10.1002/widm.1484
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. Advance online publication.
https://openreview.net/forum?id=5aYGXxByI6
2022
Deng, D., Karl, F., Hutter, F., Bischl, B.
, & Lindauer, M. (2022).
Efficient Automated Deep Learning for Time Series Forecasting. In
Proceedings of the European Conference on Machine Learning (ECML) https://doi.org/10.48550/arXiv.2205.05511
Deng, D., & Lindauer, M. (2022).
Searching in the Forest for Local Bayesian Optimization. In
ECML/PKDD workshop on Meta-learning Advance online publication.
https://arxiv.org/abs/2111.05834
Lindauer, M., Eggensperger, K., Feurer, M., Biedenkapp, A.
, Deng, D., Benjamins, C., Sass, R., & Hutter, F. (2022).
SMAC3: A Versatile Bayesian Optimization Package for Hyperparameter Optimization.
Journal of Machine Learning Research,
2022(23).
https://arxiv.org/abs/2109.09831
2021
Guerrero-Viu, J., Hauns, S., Izquierdo, S., Miotto, G., Schrodi, S., Biedenkapp, A., Elsken, T.
, Deng, D., Lindauer, M., & Hutter, F. (2021).
Bag of Baselines for Multi-objective Joint Neural Architecture Search and Hyperparameter Optimization. In
ICML 2021 Workshop AutoML Advance online publication.
https://arxiv.org/abs/2105.01015
2020
Awad, N., Shala, G.
, Deng, D., Mallik, N., Feurer, M., Eggensperger, K., Biedenkapp, A., Vermetten, D., Wang, H., Doerr, C.
, Lindauer, M., & Hutter, F. (2020).
Squirrel: A Switching Hyperparameter Optimizer. Advance online publication.
https://arxiv.org/abs/2012.08180
