Showing results 43 - 84 out of 97
2021
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) https://arxiv.org/abs/2106.05110
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 https://arxiv.org/abs/2105.01015
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) https://arxiv.org/abs/2106.11189
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. [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) https://arxiv.org/abs/2111.04820
Schubert, F., Eimer, T., Rosenhahn, B., & Lindauer, M. (2021). Automatic Risk Adaptation in Distributional Reinforcement Learning. 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 (Eds.), Machine Learning and Knowledge Discovery in Databases. Research Track: European Conference, ECML PKDD 2021, Proceedings (Vol. 3, pp. 265-296). (Lecture Notes in Artificial Intelligence (Subseries of Lecture Notes in Computer Science); Vol. 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 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 Proceedings of the International Conference on Automated Planning and Scheduling (ICAPS) https://arxiv.org/abs/2006.08246
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. [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. 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 (Eds.), ECAI 2020 - 24th European Conference on Artificial Intelligence (pp. 427-434). (Frontiers in Artificial Intelligence and Applications; Vol. 325). https://doi.org/10.3233/FAIA200122
Biedenkapp, A., Rajan, R., Hutter, F., & Lindauer, M. T. (2020). Towards TempoRL Learning When to Act. Paper presented at 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
Eggensperger, K., Haase, K., Müller, P., Lindauer, M., & Hutter, F. (2020). Neural Model-based Optimization with Right-Censored Observations. https://arxiv.org/abs/2009.13828
Eimer, T., Biedenkapp, A., Hutter, F., & Lindauer, M. T. (2020). Towards Self-Paced Context Evaluation for Contextual Reinforcement Learning. https://www.tnt.uni-hannover.de/papers/data/1454/space.pdf
Lindauer, M., & Hutter, F. (2020). Best Practices for Scientific Research on Neural Architecture Search. Journal of Machine Learning Research, 21. https://arxiv.org/abs/1909.02453
Shala, G., Biedenkapp, A., Awad, N., Adriaensen, S., Lindauer, M., & Hutter, F. (2020). Learning Step-Size Adaptation in CMA-ES. In T. Bäck, M. Preuss, A. Deutz, M. Emmerich, H. Wang, C. Doerr, & H. Trautmann (Eds.), Parallel Problem Solving from Nature – PPSN XVI: 16th International Conference, PPSN 2020, Leiden, The Netherlands, September 5-9, 2020, Proceedings, Part I (pp. 691-706). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 12269). Springer. https://doi.org/10.1007/978-3-030-58112-1_48
2019
Biedenkapp, A., Bozkurt, H. F., Hutter, F., & Lindauer, M. (2019). Towards White-box Benchmarks for Algorithm Control. https://arxiv.org/abs/1906.07644
Eggensperger, K., Lindauer, M., & Hutter, F. (2019). Pitfalls and Best Practices in Algorithm Configuration. Journal of Artificial Intelligence Research, 64, 861-893. https://doi.org/10.1613/jair.1.11420
Fuks, L., Awad, N., Hutter, F., & Lindauer, M. (2019). An evolution strategy with progressive episode lengths for playing games. In S. Kraus (Ed.), Proceedings of the 28th International Joint Conference on Artificial Intelligence, IJCAI 2019 (pp. 1234-1240). (IJCAI International Joint Conference on Artificial Intelligence). AAAI Press/International Joint Conferences on Artificial Intelligence. https://doi.org/10.24963/ijcai.2019/172
Lindauer, M. T. (2019). Automated Algorithm Selection –Predict which algorithm to use!. https://www.google.com/url?sa=t&rct=j&q=&esrc=s&source=web&cd=&ved=2ahUKEwjd94W5k9TqAhWM_KQKHVbABogQFjAAegQIARAB&url=http%3A%2F%2Fceur-ws.org%2FVol-2360%2Fpaper2Keynote.pdf&usg=AOvVaw0h4cvTGwQg-XD97fhGaytv
Lindauer, M., Eggensperger, K., Feurer, M., Biedenkapp, A., Marben, J., Müller, P., & Hutter, F. (2019). BOAH: A Tool Suite for Multi-Fidelity Bayesian Optimization & Analysis of Hyperparameters. https://arxiv.org/pdf/1908.06756
Lindauer, M. T. (2019). Hands-On Automated Machine Learning Tools: Auto-Sklearn and Auto-PyTorch. https://www.google.com/url?sa=t&rct=j&q=&esrc=s&source=web&cd=&cad=rja&uact=8&ved=2ahUKEwibqKyPlNTqAhUwMewKHR7PC0oQFjAAegQIARAB&url=https%3A%2F%2Fwww.automl.org%2Fevents%2Famir19-key-note-and-automl-hands-on%2F&usg=AOvVaw1ggAEDpu7zdnlhnRtLdGQD
Lindauer, M., van Rijn, J. N., & Kotthoff, L. (2019). The algorithm selection competitions 2015 and 2017. Artificial intelligence, 272, 86-100. https://doi.org/10.1016/j.artint.2018.10.004
Lindauer, M., Feurer, M., Eggensperger, K., Biedenkapp, A., & Hutter, F. (2019). Towards Assessing the Impact of Bayesian Optimization’s Own Hyperparameters. In DSO Workshop at IJCAI https://arxiv.org/abs/1908.06674
Mendoza, H., Klein, A., Feurer, M., Springenberg, J. T., Urban, M., Burkart, M., Dippel, M., Lindauer, M. T., & Hutter, F. (2019). Towards Automatically-Tuned Deep Neural Networks. In Automated Machine Learning https://doi.org/10.1007/978-3-030-05318-5_7
2018
Biedenkapp, A., Marben, J., Lindauer, M., & Hutter, F. (2018). CAVE: Configuration Assessment, Visualization and Evaluation. In P. M. Pardalos, R. Battiti, M. Brunato, & I. Kotsireas (Eds.), Learning and Intelligent Optimization (pp. 115-130). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 11353 LNCS). Springer Verlag. https://doi.org/10.1007/978-3-030-05348-2_10
Eggensperger, K., Lindauer, M., Hoos, H. H., Hutter, F., & Leyton-Brown, K. (2018). Efficient benchmarking of algorithm configurators via model-based surrogates. Machine learning, 107(1), 15-41. https://doi.org/10.1007/s10994-017-5683-z
Eggensperger, K., Lindauer, M., & Hutter, F. (2018). Neural Networks for Predicting Algorithm Runtime Distributions. In J. Lang (Ed.), Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence (pp. 1442-1448). AAAI Press/International Joint Conferences on Artificial Intelligence. https://doi.org/10.24963/ijcai.2018/200
Feurer, M., Eggensperger, K., Falkner, S., Lindauer, M. T., & Hutter, F. (2018). Practical Automated Machine Learning for the AutoML Challenge 2018. https://www.tnt.uni-hannover.de/papers/data/1407/18-AUTOML-AutoChallenge.pdf
Lindauer, M., Hoos, H., Hutter, F., & Leyton-Brown, K. (2018). Selection and Configuration of Parallel Portfolios. In Handbook of Parallel Constraint Reasoning (pp. 583-615). Springer International Publishing AG. https://doi.org/10.1007/978-3-319-63516-3_15
Lindauer, M. T., van Rijn, J. N., & Kotthoff, L. (2018). The Algorithm Selection Competition Series 2015-17. https://arxiv.org/abs/1805.01214v1
Lindauer, M., & Hutter, F. (2018). Warmstarting of Model-Based Algorithm Configuration. In 32nd AAAI Conference on Artificial Intelligence, AAAI 2018 (pp. 1355-1362). (Proceedings of the AAAI Conference on Artificial Intelligence). AAAI Press/International Joint Conferences on Artificial Intelligence. https://arxiv.org/abs/1709.04636v3
Wagner, M., Lindauer, M., Mısır, M., Nallaperuma, S., & Hutter, F. (2018). A case study of algorithm selection for the traveling thief problem. Journal of heuristics, 24(3), 295-320. https://doi.org/10.1007/s10732-017-9328-y
2017
Biedenkapp, A., Lindauer, M., Eggensperger, K., Hutter, F., Fawcett, C., & Hoos, H. H. (2017). Efficient Parameter Importance Analysis via Ablation with Surrogates. In Proceedings of the AAAI Conference on Artificial Intelligence https://doi.org/10.1609/aaai.v31i1.10657
Hutter, F., Lindauer, M., Balint, A., Bayless, S., Hoos, H., & Leyton-Brown, K. (2017). The Configurable SAT Solver Challenge (CSSC). Artificial intelligence, 243, 1-25. https://doi.org/10.1016/j.artint.2016.09.006
Lindauer, M., Hutter, F., Hoos, H. H., & Schaub, T. (2017). AutoFolio: An Automatically Configured Algorithm Selector (Extended Abstract). In C. Sierra (Ed.), International Joint Conference on Artificial Intelligence (IJCAI 2017) (pp. 5025-5029). AAAI Press/International Joint Conferences on Artificial Intelligence. https://www.google.com/url?sa=t&rct=j&q=&esrc=s&source=web&cd=&ved=2ahUKEwjo2uHc_87qAhVpzMQBHf2lDTwQFjABegQIAxAB&url=https%3A%2F%2Fwww.ijcai.org%2FProceedings%2F2017%2F0715.pdf&usg=AOvVaw1ART0bWLbCU4uLc4oV19yv
Lindauer, M. T., van Rijn, J. N., & Kotthoff, L. (2017). Open Algorithm Selection Challenge 2017 Setup and Scenarios. http://proceedings.mlr.press/v79/lindauer17a/lindauer17a.pdf
Wagner, M., Friedrich, T., & Lindauer, M. (2017). Improving local search in a minimum vertex cover solver for classes of networks. In 2017 IEEE Congress on Evolutionary Computation (CEC): Proceedings (pp. 1704-1711). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/cec.2017.7969507
2016
Bischl, B., Kerschke, P., Kotthoff, L., Lindauer, M., Malitsky, Y., Fréchette, A., Hoos, H., Hutter, F., Leyton-Brown, K., Tierney, K., & Vanschoren, J. (2016). ASlib: A benchmark library for algorithm selection. Artificial intelligence, 237, 41-58. https://doi.org/10.1016/j.artint.2016.04.003
