Publications of the Institute


Showing entries 43 - 78 out of 78
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2019


Biedenkapp, A., Bozkurt, H. F., Hutter, F., & Lindauer, M. (2019). Towards White-box Benchmarks for Algorithm Control.

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.

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.

doi.org/10.24963/ijcai.2019/172

Lindauer, M. T. (2019). Automated Algorithm Selection –Predict which algorithm to use!.

www.google.com/url

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.

arxiv.org/pdf/1908.06756

Lindauer, M. T. (2019). Hands-On Automated Machine Learning Tools: Auto-Sklearn and Auto-PyTorch.

www.google.com/url

Lindauer, M., van Rijn, J. N., & Kotthoff, L. (2019). The algorithm selection competitions 2015 and 2017. Artificial intelligence, 272, 86-100.

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

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

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.

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.

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.

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.

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.

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.

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.

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.

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.

www.google.com/url

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.

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.

www.google.com/url

Lindauer, M. T., van Rijn, J. N., & Kotthoff, L. (2017). Open Algorithm Selection Challenge 2017 Setup and Scenarios.

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..

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.

doi.org/10.1016/j.artint.2016.04.003

Lindauer, M., Bergdoll, R. D., & Hutter, F. (2016). An Empirical Study of Per-instance Algorithm Scheduling. In P. Festa, M. Sellmann, & J. Vanschoren (Eds.), Learning and Intelligent Optimization (pp. 253-259). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 10079 LNCS). Springer Verlag.

doi.org/10.1007/978-3-319-50349-3_20

Lindauer, M., Hoos, H., Leyton-Brown, K., & Schaub, T. (2016). Automatic construction of parallel portfolios via algorithm configuration. Artificial intelligence, 244, 272-290.

doi.org/10.1016/j.artint.2016.05.004

Manthey, N., & Lindauer, M. (2016). SpyBug: Automated Bug Detection in the Configuration Space of SAT Solvers. In D. Le Berre, & N. Creignou (Eds.), Theory and Applications of Satisfiability Testing – SAT 2016 (pp. 554-561). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 9710). Springer Verlag.

doi.org/10.1007/978-3-319-40970-2_36


2015


Albrecht, S. V., Beck, J. C., Buckeridge, D. L., Botea, A., Caragea, C., Chi, C. H., Damoulas, T., Dilkina, B., Eaton, E., Fazli, P., Ganzfried, S., Giles, C. L., Guillet, S., Holte, R., Hutter, F., Koch, T., Leonetti, M., Lindauer, M., Machado, M. C., ... Zheng, Y. (2015). Reports on the 2015 AAAI Workshop Series. AI magazine, 36(2), 90-101.

doi.org/10.1609/aimag.v36i2.2590

Falkner, S., Lindauer, M., & Hutter, F. (2015). SpySMAC: Automated Configuration and Performance Analysis of SAT Solvers. In M. Heule, & S. Weaver (Eds.), Theory and Applications of Satisfiability Testing – SAT 2015 (pp. 215-222). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 9340). Springer Verlag.

doi.org/10.1007/978-3-319-24318-4_16

Hutter, F., Lindauer, M., & Malitsky, Y. (2015). Preface. In Algorithm configuration: papers presented at the Twenty-Ninth AAAI Conference on Artificial Intelligence (pp. vii). (AAAI Workshop - Technical Report).

Lindauer, M., Hoos, H. H., Schaub, T., & Hutter, F. (2015). Auto folio: Algorithm configuration for algorithm selection. In Algorithm Configuration: papers presented at the Twenty-Ninth AAAI Conference on Artificial Intelligence (pp. 9-15). (AAAI Workshop - Technical Report). AI Access Foundation.

Lindauer, M. T., Hoos, H., Hutter, F., & Schaub, T. (2015). AutoFolio: An Automatically Configured Algorithm Selector. Journal of Artificial Intelligence Research, 53, 745-778.

doi.org/10.1613/jair.4726

Lindauer, M., Hoos, H. H., & Hutter, F. (2015). From Sequential Algorithm Selection to Parallel Portfolio Selection. In C. Dhaenens, L. Jourdan, & M-E. Marmion (Eds.), Learning and Intelligent Optimization (pp. 1-16). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 8994). Springer Verlag.

doi.org/10.1007/978-3-319-19084-6_1


2014


Hoos, H., Kaminski, R., Lindauer, M., & Schaub, T. (2014). aspeed: Solver scheduling via answer set programming. Theory and Practice of Logic Programming, 15(1), 117-142.

doi.org/10.1017/s1471068414000015

Hoos, H., Lindauer, M., & Schaub, T. (2014). claspfolio 2: Advances in Algorithm Selection for Answer Set Programming. Theory and Practice of Logic Programming, 14(4-5), 569-585.

doi.org/10.1017/S1471068414000210

Hutter, F., López-Ibáñez, M., Fawcett, C., Lindauer, M., Hoos, H. H., Leyton-Brown, K., & Stützle, T. (2014). AClib: A Benchmark Library for Algorithm Configuration. In Learning and Intelligent Optimization (pp. 36-40). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 8426 LNCS). Springer Verlag.

doi.org/10.1007/978-3-319-09584-4_4

Lindauer, M. T. (2014). Algorithm Selection, Scheduling and Configuration of Boolean Constraint Solvers. [Doctoral thesis, University of Potsdam].

nbn-resolving.org/urn:nbn:de:kobv:517-opus4-71260