Publications of the Institute

Benjamins, C., Raponi, E., Jankovic, A., Doerr, C., & Lindauer, M. (Accepted/In press). Self-Adjusting Weighted Expected Improvement for Bayesian Optimization. In AutoML Conference 2023 PMLR.

Benjamins, C., Raponi, E., Jankovic, A., Doerr, C., & Lindauer, M. (Accepted/In press). Towards Self-Adjusting Weighted Expected Improvement for Bayesian Optimization. In GECCO '23: Proceedings of the Genetic and Evolutionary Computation Conference Companion Association for Computing Machinery Special Interest Group on Genetic and Evolutionary Computation (SIGEVO).

Eimer, T., Lindauer, M., & Raileanu, R. (Accepted/In press). Hyperparameters in Reinforcement Learning and How To Tune Them. In Proceeding of the Fortieth International Conference on Machine Learning (Proceeding of the International Conference on Machine Learning).

Loni, M., Mohan, A., Asadi, M., & Lindauer, M. (Accepted/In press). Learning Activation Functions for Sparse Neural Networks. In Second International Conference on Automated Machine Learning PMLR.

arxiv.org/abs/2305.10964

Mohan, A., Benjamins, C., Wienecke, K., Dockhorn, A., & Lindauer, M. (Accepted/In press). AutoRL Hyperparameter Landscapes. In Second International Conference on Automated Machine Learning PMLR.

doi.org/10.48550/arXiv.2304.02396

Segel, S., Graf, H., Tornede, A., Bischl, B., & Lindauer, M. (Accepted/In press). Symbolic Explanations for Hyperparameter Optimization. In AutoML Conference 2023 PMLR.

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

www.climatechange.ai/papers/iclr2023/53

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. In 2022 NeurIPS Workshop on Gaussian Processes, Spatiotemporal Modeling, and Decision-making Systems

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. In 6th Workshop on Meta-Learning at NeurIPS 2022

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

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)

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

arxiv.org/abs/2111.05834

Hvarfner, C., Stoll, D., Souza, A. L. F., Lindauer, M., Hutter, F., & Nardi, L. (Accepted/In press). π BO: Augmenting Acquisition Functions with User Beliefs for Bayesian Optimization. In Proceedings of the International conference on Learning Representation (ICLR)

doi.org/10.48550/arXiv.2204.11051

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)

openreview.net/forum

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)

arxiv.org/abs/2206.03130

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)

arxiv.org/pdf/2206.03493v1.pdf

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

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)

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)

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).

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)

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)

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

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)

arxiv.org/abs/2106.11189

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)

arxiv.org/abs/2111.04820

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.

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

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)

arxiv.org/abs/2006.08246