Showing results 43 - 84 out of 146
2023
Mohan, A., Zhang, A., & Lindauer, M. (Accepted/in press). A Patterns Framework for Incorporating Structure in Deep Reinforcement Learning. In The 16th European Workshop on Reinforcement Learning (EWRL 2023) https://openreview.net/forum?id=KkKWsPLlAx
Mohan, A., Benjamins, C., Wienecke, K., Dockhorn, A., & Lindauer, M. (2023). AutoRL Hyperparameter Landscapes. In Conference proceeding: Second Internatinal Conference on Automated Machine Learning (Proceedings of Machine Learning Research; Vol. 228). PMLR. https://doi.org/10.48550/arXiv.2304.02396
Mohan, A., Benjamins, C., Wienecke, K., Dockhorn, A., & Lindauer, M. (2023). Extended Abstract: AutoRL Hyperparameter Landscapes. Abstract from European Workshop on Reinforcement Learning 2023, Brüssel. Advance online publication. https://openreview.net/forum?id=4Zu0l5lBgc
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
Schubert, F., Benjamins, C., Döhler, S., Rosenhahn, B., & Lindauer, M. (2023). POLTER: Policy Trajectory Ensemble Regularization for Unsupervised Reinforcement Learning. Transactions on Machine Learning Research, 2023(4). https://doi.org/10.48550/arXiv.2205.11357
Segel, S., Graf, H., Tornede, A., Bischl, B., & Lindauer, M. (2023). Symbolic Explanations for Hyperparameter Optimization. In AutoML Conference 2023 PMLR. Advance online publication. https://openreview.net/forum?id=JQwAc91sg_x
Shoaib, M., Kotthoff, L., Lindauer, M., & Kant, S. (2023). AutoML: advanced tool for mining multivariate plant traits. Trends in Plant Science, 28(12), 1451-1452. https://doi.org/10.1016/j.tplants.2023.09.008
Theodorakopoulos, D., Manß, C., Stahl, F., & Lindauer, M. (2023). Green-AutoML for Plastic Litter Detection. In Proceedings of the ICLR Workshop on Tackling Climate Change with Machine Learning https://www.climatechange.ai/papers/iclr2023/53
Vermetten, D., Krejca, M. S., Lindauer, M., López-Ibáñez, M., & Malan, K. M. (2023). Synergizing Theory and Practice of Automated Algorithm Design for Optimization (Dagstuhl Seminar 23332). Dagstuhl Reports, 13(8). https://doi.org/10.4230/DagRep.13.8.46
Zoeller, M., Mauthe, F., Zeiler, P., Lindauer, M., & Huber, M. (2023). Automated Machine Learning for Remaining Useful Life Predictions. In Proceedings of the international conference on Systems Science and Engineering, Human-Machine Systems, and Cybernetics (IEEE SMC): Improving the Quality of Life, SMC 2023 - Proceedings (pp. 2907-2912). (IEEE International Conference on Systems, Man, and Cybernetics). IEEE Xplore Digital Library. https://doi.org/10.1109/SMC53992.2023.10394031, https://doi.org/10.48550/arXiv.2306.12215
2022
Adriaensen, S., Biedenkapp, A., Shala, G., Awad, N., Eimer, T., Lindauer, M., & Hutter, F. (2022). Automated Dynamic Algorithm Configuration. Journal of Artificial Intelligence Research, 75, 1633-1699. https://doi.org/10.1613/jair.1.13922, https://doi.org/10.48550/arXiv.2205.13881
Adriaenssen, S., Biedenkapp, A., Hutter, F., Shala, G., Lindauer, M., & Awad, N. (2022). Method and device for learning a strategy and for implementing the strategy. (Patent No. US2022027743A1). United States Patent and Trademark Office (USPTO). https://worldwide.espacenet.com/patent/search/family/079179186/publication/CN113971460A?q=CN113971460A
Adriaenssen, S., Biedenkapp, A., Hutter, F., Shala, G., Lindauer, M., & Awad, N. (2022). Verfahren und Vorrichtung zum Lernen einer Strategie und Betreiben der Strategie. (Patent No. DE102020209281A1). Deutsches Patent- und Markenamt (DPMA). https://worldwide.espacenet.com/patent/search/family/079179186/publication/CN113971460A?q=CN113971460A
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. Advance online publication. 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. Paper presented at 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 Advance online publication. 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 Advance online publication. 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 Use Different Hyperparameter Optimization Methods. ACM Transactions on Computer-Human Interaction. Advance online publication. https://doi.org/10.1145/3745771, 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 No. DE102020212108A1). Deutsches Patent- und Markenamt (DPMA). https://worldwide.espacenet.com/patent/search/family/080624412/publication/DE102020212108A1?q=DE102020212108A1
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 No. DE102021109754A1). Deutsches Patent- und Markenamt (DPMA). https://worldwide.espacenet.com/patent/search/family/083447429/publication/DE102021109754A1?q=DE102021109754A1
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 No. US2022012636A1). United States Patent and Trademark Office (USPTO). https://worldwide.espacenet.com/patent/search/family/079020036/publication/US2022012636A1?q=US2022012636A1
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 No. DE10202020867). Deutsches Patent- und Markenamt (DPMA). https://worldwide.espacenet.com/patent/search/family/079020036/publication/DE102020208671A1?q=DE102020208671A1
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) https://arxiv.org/abs/2206.03130
Moosbauer, J., Casalicchio, G., Lindauer, M., & Bischl, B. (2022). Enhancing Explainability of Hyperparameter Optimization via Bayesian Algorithm Execution. Advance online publication. 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
Sass, R., Bergman, E., Biedenkapp, A., Hutter, F., & Lindauer, M. (2022). DeepCAVE: An Interactive Analysis Tool for Automated Machine Learning. In ICML Workshop on Adaptive Experimental Design and Active Learning in the Real World (ReALML) https://doi.org/10.48550/arXiv.2206.03493
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 Advance online publication. https://doi.org/10.48550/arXiv.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) Advance online publication. 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) Advance online publication. 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 (Ed.), Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence (IJCAI-21) (pp. 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) (pp. 2948-2958). (Proceedings of Machine Learning Research; Vol. 139). ML Research Press. http://proceedings.mlr.press/v139/eimer21a/eimer21a.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 Advance online publication. https://arxiv.org/abs/2105.01015
Hutter, F., Fuks, L., Lindauer, M., & Awad, N. (2021). Method, device and computer programm for producing a strategy for a robot. (Patent No. US2021008718). https://at.espacenet.com/publicationDetails/biblio?FT=D&date=20210114&DB=EPODOC&locale=de_AT&CC=US&NR=2021008718A1&KC=A1&ND=4
Hutter, F., Fuks, L., Lindauer, M., & Awad, N. (2021). Verfahren, Vorrichtung und Computerprogramm zum Erstellen einer Strategie für einen Roboter. (Patent No. 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) Advance online publication. 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 No. DE102019214625A1). Deutsches Patent- und Markenamt (DPMA). https://worldwide.espacenet.com/patent/search/family/074846095/publication/DE102019214625A1?q=DE102019214625A1
