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.
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).
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.
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.
Eggensperger, K., Haase, K., Müller, P., Lindauer, M., & Hutter, F. (2020). Neural Model-based Optimization with Right-Censored Observations.
Eimer, T., Biedenkapp, A., Hutter, F., & Lindauer, M. T. (2020). Towards Self-Paced Context Evaluation for Contextual Reinforcement Learning.
Lindauer, M., & Hutter, F. (2020). Best Practices for Scientific Research on Neural Architecture Search. Journal of Machine Learning Research, 21.
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 (Hrsg.), Parallel Problem Solving from Nature – PPSN XVI: 16th International Conference, PPSN 2020, Leiden, The Netherlands, September 5-9, 2020, Proceedings, Part I (S. 691-706). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Band 12269). Springer.
2019
Biedenkapp, A., Bozkurt, H. F., Hutter, F., & Lindauer, M. (2019). Towards White-box Benchmarks for Algorithm Control.
Eggensperger, K., Lindauer, M., & Hutter, F. (2019). Pitfalls and Best Practices in Algorithm Configuration. Journal of Artificial Intelligence Research, 64, 861-893.
Fuks, L., Awad, N., Hutter, F., & Lindauer, M. (2019). An evolution strategy with progressive episode lengths for playing games. in S. Kraus (Hrsg.), Proceedings of the 28th International Joint Conference on Artificial Intelligence, IJCAI 2019 (S. 1234-1240). (IJCAI International Joint Conference on Artificial Intelligence). AAAI Press/International Joint Conferences on Artificial Intelligence.
Lindauer, M. T. (2019). Automated Algorithm Selection –Predict which algorithm to use!.
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.
Lindauer, M. T. (2019). Hands-On Automated Machine Learning Tools: Auto-Sklearn and Auto-PyTorch.
Lindauer, M., van Rijn, J. N., & Kotthoff, L. (2019). The algorithm selection competitions 2015 and 2017. Artificial intelligence, 272, 86-100.
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
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
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 (Hrsg.), Learning and Intelligent Optimization (S. 115-130). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Band 11353 LNCS). Springer Verlag.
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.
Eggensperger, K., Lindauer, M., & Hutter, F. (2018). Neural Networks for Predicting Algorithm Runtime Distributions. in J. Lang (Hrsg.), Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence (S. 1442-1448). AAAI Press/International Joint Conferences on Artificial Intelligence.
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 (S. 583-615). Springer International Publishing AG.
Lindauer, M. T., van Rijn, J. N., & Kotthoff, L. (2018). The Algorithm Selection Competition Series 2015-17.
Lindauer, M., & Hutter, F. (2018). Warmstarting of Model-Based Algorithm Configuration. in 32nd AAAI Conference on Artificial Intelligence, AAAI 2018 (S. 1355-1362). (Proceedings of the AAAI Conference on Artificial Intelligence). AAAI Press/International Joint Conferences on Artificial Intelligence.
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.
2017
Biedenkapp, A., Lindauer, M., Eggensperger, K., Hutter, F., Fawcett, C., & Hoos, H. H. (2017). Efficient Parameter Importance Analysis via Ablation with Surrogates.
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.
Lindauer, M., Hutter, F., Hoos, H. H., & Schaub, T. (2017). AutoFolio: An Automatically Configured Algorithm Selector (Extended Abstract). in C. Sierra (Hrsg.), International Joint Conference on Artificial Intelligence (IJCAI 2017) (S. 5025-5029). AAAI Press/International Joint Conferences on Artificial Intelligence.
Lindauer, M. T., van Rijn, J. N., & Kotthoff, L. (2017). Open Algorithm Selection Challenge 2017 Setup and Scenarios.
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 (S. 1704-1711). Institute of Electrical and Electronics Engineers Inc..
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.
Lindauer, M., Bergdoll, R. D., & Hutter, F. (2016). An Empirical Study of Per-instance Algorithm Scheduling. in P. Festa, M. Sellmann, & J. Vanschoren (Hrsg.), Learning and Intelligent Optimization (S. 253-259). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Band 10079 LNCS). Springer Verlag.
Lindauer, M., Hoos, H., Leyton-Brown, K., & Schaub, T. (2016). Automatic construction of parallel portfolios via algorithm configuration. Artificial intelligence, 244, 272-290.
Manthey, N., & Lindauer, M. (2016). SpyBug: Automated Bug Detection in the Configuration Space of SAT Solvers. in D. Le Berre, & N. Creignou (Hrsg.), Theory and Applications of Satisfiability Testing – SAT 2016 (S. 554-561). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Band 9710). Springer Verlag.
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.
Falkner, S., Lindauer, M., & Hutter, F. (2015). SpySMAC: Automated Configuration and Performance Analysis of SAT Solvers. in M. Heule, & S. Weaver (Hrsg.), Theory and Applications of Satisfiability Testing – SAT 2015 (S. 215-222). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Band 9340). Springer Verlag.
Hutter, F., Lindauer, M., & Malitsky, Y. (2015). Preface. in Algorithm configuration: papers presented at the Twenty-Ninth AAAI Conference on Artificial Intelligence (S. 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 (S. 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.
Lindauer, M., Hoos, H. H., & Hutter, F. (2015). From Sequential Algorithm Selection to Parallel Portfolio Selection. in C. Dhaenens, L. Jourdan, & M-E. Marmion (Hrsg.), Learning and Intelligent Optimization (S. 1-16). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Band 8994). Springer Verlag.
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.
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.
