Publikationen des Institutes

Zeige Ergebnisse 43 - 84 von 132

2022


Benjamins, C., Raponi, E., Jankovic, A., Blom, K. V. D., Santoni, M. L., Lindauer, M., & Doerr, C. (2022). PI is back! Switching Acquisition Functions in Bayesian Optimization. Vorabveröffentlichung online. https://arxiv.org/abs/2211.01455
Benjamins, C., Jankovic, A., Raponi, E., Blom, K. V. D., Lindauer, M., & Doerr, C. (2022). Towards Automated Design of Bayesian Optimization via Exploratory Landscape Analysis. Beitrag in Workshop on Meta-Learning (MetaLearn 2022). https://openreview.net/forum?id=cmxtTF_IHd
Bothmann, L., Strickroth, S., Casalicchio, G., Rügamer, D., Lindauer, M., Scheipl, F., & Bischl, B. (2022). Developing Open Source Educational Resources for Machine Learning and Data Science. In Teaching Machine Learning Workshop at ECML 2022 Vorabveröffentlichung online. https://arxiv.org/abs/2107.14330
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 Vorabveröffentlichung online. https://arxiv.org/abs/2111.05834
Feurer, M., Eggensperger, K., Falkner, S., Lindauer, M. T., & Hutter, F. (2022). Auto-Sklearn 2.0: Hands-free AutoML via Meta-Learning. Journal of Machine Learning Research, 23. https://www.jmlr.org/papers/volume23/21-0992/21-0992.pdf
Hasebrook, N., Morsbach, F., Kannengießer, N., Zöller, M., Franke, J., Lindauer, M., Hutter, F., & Sunyaev, A. (2022). Practitioner Motives to Select Hyperparameter Optimization Methods. Vorabveröffentlichung online. https://doi.org/10.48550/arXiv.2203.01717
Hutter, F., Lindauer, M., Kadra, A., & Grabocka, J. (2022). Verfahren und Vorrichtung zum Anlernen eines maschinellen Lernsystems. (Patent Nr. DE102020212108).
Hutter, F., Miotto, G., Lindauer, M., & Elsken, T. (2022). Verfahren und Vorrichtung zum Ermitteln von Netzkonfigurationen eines neuronalen Netzes unter Erfüllung einer Mehrzahl von Nebenbedingungen. (Patent Nr. DE102021109754).
Hvarfner, C., Stoll, D., Souza, A. L. F., Lindauer, M., Hutter, F., & Nardi, L. (2022). π BO: Augmenting Acquisition Functions with User Beliefs for Bayesian Optimization. In Proceedings of the International conference on Learning Representation (ICLR) https://doi.org/10.48550/arXiv.2204.11051
Lindauer, M., Zela, A., Stoll, D. O., Ferreira, F., Hutter, F., & Nierhoff, T. (2022). METHOD AND DEVICE FOR CREATING A SYSTEM FOR THE AUTOMATED CREATION OF MACHINE LEARNING SYSTEMS. (Patent Nr. US2022012636).
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
Lindauer, M., Zela, A., Stoll, D., Ferreira, F., Hutter, F., & Nierhoff, T. (2022). Verfahren und Vorrichtung zum Erstellen eines Systems zum automatisierten Erstellen von maschinellen Lernsystemen. (Patent Nr. DE102020208671).
Mallik, N., Hvarfner, C., Stoll, D., Janowski, M., Bergman, E., Lindauer, M. T., Nardi, L., & Hutter, F. (2022). PriorBand: HyperBand + Human Expert Knowledge. In 2022 NeurIPS Workshop on Meta Learning (MetaLearn) https://openreview.net/forum?id=ds21dwfBBH
Mohan, A., Ruhkopf, T., & Lindauer, M. (2022). Towards Meta-learned Algorithm Selection using Implicit Fidelity Information. In ICML Workshop on Adaptive Experimental Design and Active Learning in the Real World (ReALML) Vorabveröffentlichung online. https://arxiv.org/abs/2206.03130
Moosbauer, J., Casalicchio, G., Lindauer, M., & Bischl, B. (2022). Enhancing Explainability of Hyperparameter Optimization via Bayesian Algorithm Execution. Vorabveröffentlichung online. https://doi.org/10.48550/arXiv.2206.05447
Parker-Holder, J., Rajan, R., Song, X., Biedenkapp, A., Miao, Y., Eimer, T., Zhang, B., Nguyen, V., Calandra, R., Faust, A., Hutter, F., & Lindauer, M. (2022). Automated Reinforcement Learning (AutoRL): A Survey and Open Problems. Journal of Artificial Intelligence Research, 74(74), 517-568. https://doi.org/10.48550/arXiv.2201.03916, https://doi.org/10.1613/jair.1.13596

2021


Benjamins, C., Eimer, T., Schubert, F., Biedenkapp, A., Rosenhahn, B., Hutter, F., & Lindauer, M. (2021). CARL: A Benchmark for Contextual and Adaptive Reinforcement Learning. In Workshop on Ecological Theory of Reinforcement Learning, NeurIPS 2021 Vorabveröffentlichung online. https://arxiv.org/abs/2110.02102
Biedenkapp, A., Rajan, R., Hutter, F., & Lindauer, M. (2021). TempoRL: Learning When to Act. In Proceedings of the international conference on machine learning (ICML) Vorabveröffentlichung online. https://arxiv.org/abs/2106.05262
Eggensperger, K., Müller, P., Mallik, N., Feurer, M., Sass, R., Awad, N., Lindauer, M., & Hutter, F. (2021). HPOBench: A Collection of Reproducible Multi-Fidelity Benchmark Problems for HPO. In Proceedings of the international conference on Neural Information Processing Systems (NeurIPS) (Datasets and Benchmarks Track) Vorabveröffentlichung online. https://arxiv.org/abs/2109.06716
Eimer, T., Biedenkapp, A., Reimer, M., Adriaensen, S., Hutter, F., & Lindauer, M. T. (2021). DACBench: A Benchmark Library for Dynamic Algorithm Configuration. In Z.-H. Zhou (Hrsg.), Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence (IJCAI-21) (S. 1668-1674). (IJCAI International Joint Conference on Artificial Intelligence). https://doi.org/10.24963/ijcai.2021/230
Eimer, T., Benjamins, C., & Lindauer, M. T. (2021). Hyperparameters in Contextual RL are Highly Situational. In International Workshop on Ecological Theory of RL (at NeurIPS) https://doi.org/10.48550/arXiv.2212.10876
Eimer, T., Biedenkapp, A., Hutter, F., & Lindauer, M. (2021). Self-Paced Context Evaluation for Contextual Reinforcement Learning. In Proceedings of the international conference on machine learning (ICML) (S. 2948-2958). (Proceedings of Machine Learning Research; Band 139). ML Research Press. https://www.tnt.uni-hannover.de/papers/data/1454/space.pdf
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 Vorabveröffentlichung online. https://arxiv.org/abs/2105.01015
Hutter, F., Fuks, L., Lindauer, M., & Awad, N. (2021). METHOD, DEVICE AND COMPUTER PROGRAM FOR PRODUCING A STRATEGY FOR A ROBOT. (Patent Nr. US2021008718).
Hutter, F., Fuks, L., Lindauer, M., & Awad, N. (2021). Verfahren, Vorrichtung und Computerprogramm zum Erstellen einer Strategie für einen Roboter. (Patent Nr. DE102019210372A1). Deutsches Patent- und Markenamt (DPMA). https://worldwide.espacenet.com/patent/search?q=pn%3DCN112215363A
Kadra, A., Lindauer, M., Hutter, F., & Grabocka, J. (2021). Well-tuned Simple Nets Excel on Tabular Datasets. In Proceedings of the international conference on Advances in Neural Information Processing Systems (NeurIPS 2021) Vorabveröffentlichung online. https://arxiv.org/abs/2106.11189
Lindauer, M., Hutter, F., Burkart, M., & Zimmer, L. (2021). Verfahren, Vorrichtung und Computerprogramm zum Erstellen eines künstlichen neuronalen Netzes. (Patent Nr. DE102019214625).
Liu, Z., Pavao, A., Xu, Z., Escalera, S., Ferreira, F., Guyon, I., Hong, S., Hutter, F., Ji, R., Junior, J. C. S. J., Li, G., Lindauer, M., Luo, Z., Madadi, M., Nierhoff, T., Niu, K., Pan, C., Stoll, D., Treguer, S., ... Zhang, Y. (2021). Winning Solutions and Post-Challenge Analyses of the ChaLearn AutoDL Challenge 2019. IEEE Transactions on Pattern Analysis and Machine Intelligence, 43(9), 3108-3125. Artikel 9415128. https://doi.org/10.48550/arXiv.2201.03801, https://doi.org/10.1109/TPAMI.2021.3075372
Moosbauer, J., Herbinger, J., Casalicchio, G., Lindauer, M., & Bischl, B. (2021). Explaining Hyperparameter Optimization via Partial Dependence Plots. In Proceedings of the international conference on Neural Information Processing Systems (NeurIPS) Vorabveröffentlichung online. https://arxiv.org/abs/2111.04820
Schubert, F., Eimer, T., Rosenhahn, B., & Lindauer, M. (2021). Automatic Risk Adaptation in Distributional Reinforcement Learning. Vorabveröffentlichung online. https://arxiv.org/abs/2106.06317
Souza, A., Nardi, L., Oliveira, L. B., Olukotun, K., Lindauer, M., & Hutter, F. (2021). Bayesian Optimization with a Prior for the Optimum. In N. Oliver, F. Pérez-Cruz, S. Kramer, J. Read, & J. A. Lozano (Hrsg.), Machine Learning and Knowledge Discovery in Databases. Research Track: European Conference, ECML PKDD 2021, Proceedings (Band 3, S. 265-296). (Lecture Notes in Artificial Intelligence (Subseries of Lecture Notes in Computer Science); Band 12977). Springer Nature Switzerland AG. https://doi.org/10.1007/978-3-030-86523-8_17
Souza, A., Nardi, L., Oliveira, L. B., Olukotun, K., Lindauer, M., & Hutter, F. (2021). Prior-guided Bayesian Optimization. In Machine Learning and Knowledge Discovery in Databases. Research Track: European Conference, ECML PKDD 2021 Vorabveröffentlichung online. https://arxiv.org/pdf/2006.14608
Speck, D., Biedenkapp, A., Hutter, F., Mattmüller, R., & Lindauer, M. (2021). Learning Heuristic Selection with Dynamic Algorithm Configuration. In S. Biundo, M. Do, R. Goldman, M. Katz, Q. Yang, & H. H. Zhuo (Hrsg.), Proceedings of the International Conference on Automated Planning and Scheduling (ICAPS) (S. 597-605). (Proceedings International Conference on Automated Planning and Scheduling, ICAPS; Band 2021-August). https://doi.org/10.1609/icaps.v31i1.16008
Spitz, J., Biedenkapp, A., Speck, D., Hutter, F., Lindauer, M., & Mattmueller, R. (2021). DEVICE AND METHOD FOR PLANNING AN OPERATION OF A TECHNICAL SYSTEM. (Patent Nr. US2021383245).
Spitz, J., Biedenkapp, A., Speck, D., Hutter, F., Lindauer, M., & Mattmueller, R. (2021). Vorrichtung und Verfahren zur Planung eines Betriebs eines technischen Systems. (Patent Nr. DE102020207114).
Stürenburg, L., Denkena, B., Lindauer, M., & Wichmann, M. (2021). Maschinelles Lernen in der Prozessplanung. VDI-Z Integrierte Produktion, 163(11-12), 26-29. https://doi.org/10.37544/0042-1766-2021-11-12-26
Zimmer, L., Lindauer, M., & Hutter, F. (2021). Auto-PyTorch: Multi-Fidelity MetaLearning for Efficient and Robust AutoDL. IEEE Transactions on Pattern Analysis and Machine Intelligence, 43(9), 3079-3090. Artikel 9382913. https://doi.org/10.1109/TPAMI.2021.3067763

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. Vorabveröffentlichung online. https://arxiv.org/abs/2012.08180
Biedenkapp, A., Bozkurt, H. F., Eimer, T., Hutter, F., & Lindauer, M. T. (2020). Dynamic Algorithm Configuration: Foundation of a New Meta-Algorithmic Framework. In G. De Giacomo, A. Catala, B. Dilkina, M. Milano, S. Barro, A. Bugarin, & J. Lang (Hrsg.), ECAI 2020 - 24th European Conference on Artificial Intelligence (S. 427-434). (Frontiers in Artificial Intelligence and Applications; Band 325). https://doi.org/10.3233/FAIA200122
Biedenkapp, A., Rajan, R., Hutter, F., & Lindauer, M. T. (2020). Towards TempoRL Learning When to Act. Beitrag in ICML 2020 Inductive biases, invariances and generalization in RL workshop. https://www.tnt.uni-hannover.de/papers/data/1455/20-BIG-TempoRL.pdf
Denkena, B., Dittrich, M.-A., Lindauer, M. T., Mainka, J. M., & Stürenburg, L. K. (2020). Using AutoML to Optimize Shape Error Prediction in Milling Processes. SSRN Electronic Journal, 2020. https://doi.org/10.2139/ssrn.3724234