

Theresa Eimer
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Appelstraße 9a
30167 Hannover
30167 Hannover
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Theresa Eimer
Research Interests
- Generalization in Reinforcement Learning
- Dynamic Algorithm Configuration
- Automated Reinforcement Learning
- Meta Reinforcement Learning
- Societal Impact of Machine Learning
Curriculum Vitae
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Education
2022: Chair for Diversity & Inclusion at the AutoML-Conf
since 2020: Doctoral Researcher at the Leibniz University Hannover
2016 - 2019: M.Sc. Computer Science at the Albert-Ludwigs University Freiburg
Thesis: Improved Meta-Learning for Dynamic Algorithm Configuration
Supervisor: Prof. Dr. Frank Hutter2013 - 2016: B.Sc. Computer Science at the University of Hamburg
Thesis: On Thue Numbers
Supervisor: Dr. Frank Heitmann
Publications
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(2022): Towards Automated Design of Bayesian Optimization via Exploratory Landscape Analysis, 6th Workshop on Meta-Learning at NeurIPS 2022, New Orleans
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(2022): PriorBand: HyperBand + Human Expert Knowledge, 2022 NeurIPS Workshop on Meta Learning (MetaLearn)
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(2022): DeepCAVE: An Interactive Analysis Tool for Automated Machine Learning, ICML Workshop on Adaptive Experimental Design and Active Learning in the Real World (ReALML)
arXiv: 2206.03493 -
(2022): Towards Meta-learned Algorithm Selection using Implicit Fidelity Information, ICML Workshop on Adaptive Experimental Design and Active Learning in the Real World (ReALML)
arXiv: 2206.03130 -
(2022): Automated Dynamic Algorithm Configuration, Computing Research Repository (CoRR) (2022)
DOI: https://doi.org/10.48550/arXiv.2205.13881
arXiv: 2205.13881 -
(2022): POLTER: Policy Trajectory Ensemble Regularization for Unsupervised Reinforcement Learning, CoRR
arXiv: arXiv:2205.11357 -
(2022): Efficient Automated Deep Learning for Time Series Forecasting, Proceedings of the European Conference on Machine Learning (ECML)
arXiv: 2205.05511 -
(2022): piBO: Augmenting Acquisition Functions with User Beliefs for Bayesian Optimization, 10th International Conference on Learning Representations, ICLR'22
arXiv: https://openreview.net/forum?id=MMAeCXIa89 -
(2022): Contextualize Me - The Case for Context in Reinforcement Learning, ArXiv Preprint
arXiv: https://arxiv.org/abs/2202.04500 -
(2022): SMAC3: A Versatile Bayesian Optimization Package for Hyperparameter Optimization, Journal of Machine Learning Research (JMLR) -- MLOSS, Vol. 23, No. 54, pp. 1-9 Weitere Informationen
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(2022): Automated Reinforcement Learning (AutoRL): A Survey and Open Problems, Journal of Artificial Intelligence Research (JAIR)
arXiv: 2201.03916 -
(2021): Well-tuned Simple Nets Excel on Tabular Datasets, Advances in Neural Information Processing Systems (NeurIPS 2021)
arXiv: 2106.11189 -
(2021): Explaining Hyperparameter Optimization via Partial Dependence Plots, Proceedings of the international conference on Neural Information Processing Systems (NeurIPS) | Datei |
arXiv: 2111.04820 -
(2021): Hyperparameters in Contextual RL are Highly Situational, International Workshop on Ecological Theory of RL (at NeurIPS) | Datei |
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(2021): Well-tuned Simple Nets Excel on Tabular Datasets, NeurIPS 2021
DOI: https://doi.org/10.48550/arXiv.2106.11189
arXiv: 2106.11189 -
(2021): Searching in the Forest for Local Bayesian Optimization, ECMLPKDD Workshop on Meta-Knowledge Transfe
arXiv: 2111.05834 -
(2021): Maschinelles Lernen in der Prozessplanung
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(2021): CARL: A Benchmark for Contextual and Adaptive Reinforcement Learning, Workshop on Ecological Theory of Reinforcement Learning, NeurIPS 2021
arXiv: 2110.02102 -
(2021): HPOBench: A Collection of Reproducible Multi-Fidelity Benchmark Problems for HPO, Proceedings of the international conference on Neural Information Processing Systems (NeurIPS) (Datasets and Benchmarks Track)
arXiv: 2109.06716 -
(2021): Bayesian Optimization with a Prior for the Optimum, Proceedings of the European conference on machine learning (ECML)
arXiv: 2006.14608 -
(2022): Developing Open Source Educational Resources for Machine Learning and Data Science, Teaching Machine Learning Workshop at ECML 2022
arXiv: 2107.14330 -
(2021): Hyperparameter Optimization: Foundations, Algorithms, Best Practices and Open Challenges, WIREs Data Mining and Knowledge Discovery
DOI: 10.1002/widm.1484
arXiv: 2107.05847 -
(2021): Self-Paced Context Evaluation for Contextual Reinforcement Learning, Proceedings of the international conference on machine learning (ICML)
arXiv: 2106.05110 -
(2021): TempoRL: Learning When to Act, Proceedings of the international conference on machine learning (ICML) 2021
arXiv: 2106.05262 -
(2021): Towards Explaining Hyperparameter Optimization via Partial Dependence Plots, 8th ICML Workshop on Automated Machine Learning (AutoML) | Datei |
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(2021): Learning Heuristic Selection with Dynamic Algorithm Configuration, International Conference on Automated Planning and Scheduling (ICAPS) 2021 | Datei |
arXiv: 2006.08246 -
(2021): Bag of Baselines for Multi-objective Joint Neural Architecture Search and Hyperparameter Optimization, ICML 2021 Workshop AutoML
arXiv: 2105.01015 -
(2021): DACBench: A Benchmark Library for Dynamic Algorithm Configuration, Proceedings of the international joint conference on AI (IJCAI) 2021
arXiv: 2105.08541 -
(2021): Winning solutions and post-challenge analyses of the ChaLearn AutoDL challenge 2019 | Datei |
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(2021): Auto-Pytorch: Multi-Fidelity MetaLearning for Efficient and Robust AutoDL., IEEE Transactions on Pattern Analysis and Machine Intelligence
arXiv: 2006.13799 -
(2020): Using AutoML to Optimize Shape Error Prediction in Milling Processes, Proceedings of the Machining Innovations Conference (MIC) | Datei |
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(2019): Best Practices for Scientific Research on Neural Architecture Search, Computing Research Repository (CoRR) (2019)
DOI: https://doi.org/10.48550/arXiv.1908.06756
arXiv: 1908.06756 -
(2020): Learning Step-Size Adaptation in CMA-ES, Proceedings of international PPSN conference 2020 | Datei |
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(2020): Towards TempoRL: Learning When to Act, International Conference on Machine Learning (ICML) 2020 | Datei |
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(2020): Towards Self-Paced Context Evaluation for Contextual Reinforcement Learning, International Conference on Machine Learning (ICML) 2020 | Datei |
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(2022): Auto-Sklearn 2.0: The Next Generation, Journal of Machine Learning Research (JMLR), 23(261):1−61 Weitere Informationen
arXiv: 2007.04074 -
(2020): Dynamic Algorithm Configuration: Foundation of a New Meta-Algorithmic Framework, European Conference on AI (ECAI) 2020 | Datei |
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(2019): Towards Assessing the Impact of Bayesian Optimization’s Own Hyperparameters, Computing Research Repository (CoRR) (2019)
DOI: https://doi.org/10.48550/arXiv.1908.06674
arXiv: 1908.06674 -
(2019): BOAH: A Tool Suite for Multi-Fidelity Bayesian Optimization & Analysis of Hyperparameters, Computing Research Repository (CoRR) (2019)
DOI: https://doi.org/10.48550/arXiv.1908.06756
arXiv: 1908.06756 -
(2019): Towards Automatically-Tuned Deep Neural Networks, Automated Machine Learning
DOI: https://doi.org/10.1007/978-3-030-05318-5
ISBN: 978-3-030-05317-8
ISSN: 2520-131X -
(2018): CAVE: Configuration Assessment, Visualization and Evaluation, Learning and Intelligent Optimization (LION) 2018 | Datei |
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(2018): Neural Networks for Predicting Algorithm Runtime Distributions, International Joint Conference on Artificial Intelligence (IJCAI) 2018
DOI: https://doi.org/10.48550/arXiv.1709.07615
arXiv: 1709.07615 -
(2018): Selection and Configuration of Parallel Portfolios, Handbook of Parallel Constraint Reasoning 2018: 583-615 | Datei |
ISBN: 978-3-319-63515-6 -
(2017): Warmstarting of Model-based Algorithm Configuration, Computing Research Repository (CoRR) (2017)
DOI: https://doi.org/10.48550/arXiv.1709.04636
arXiv: 1709.04636 -
(2017): Open Algorithm Selection Challenge 2017 Setup and Scenarios, Open Algorithm Selection Challenge 2017 Setup and Scenarios (OASC) 2017 | Datei |
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(2017): Improving local search in a minimum vertex cover solver for classes of networks, IEEE Congress on Evolutionary Computation (CEC) 2017
DOI: 10.1109/CEC.2017.7969507
ISBN: 978-1-5090-4602-7 -
(2017): Efficient Benchmarking of Algorithm Configuration Procedures via Model-Based Surrogates, Computing Research Repository (CoRR) (2017)
DOI: https://doi.org/10.48550/arXiv.1703.10342
arXiv: 1703.10342 -
(2017): Pitfalls and Best Practices in Algorithm Configuration, Computing Research Repository (CoRR) (2017)
DOI: https://doi.org/10.48550/arXiv.1705.06058
arXiv: 1705.06058 -
(2017): Automatic Construction of Parallel Portfolios via Algorithm Configuration, Artif. Intell. (2017)
DOI: https://doi.org/10.1016/j.artint.2016.05.004 -
(2017): Efficient Parameter Importance Analysis via Ablation with Surrogates, Association for the Advancement of Artificial Intelligence (AAAI) 2017
DOI: https://doi.org/10.1609/aaai.v31i1.10657 -
(2016): An Empirical Study of Per-Instance Algorithm Scheduling, Learning and Intelligent Optimization (LION) 2016
DOI: https://doi.org/10.1007/978-3-319-50349-3_20
ISBN: 978-3-319-50348-6 -
(2016): A case study of algorithm selection for the traveling thief problem, Computing Research Repository (CoRR) (2016)
arXiv: 1609.00462 -
(2016): The Configurable SAT Solver Challenge (CSSC), Computing Research Repository (CoRR) (2015)
DOI: https://doi.org/10.48550/arXiv.1505.01221
arXiv: 1505.01221 -
(2016): SpyBug: Automated Bug Detection in the Configuration Space of SAT Solvers, SAT 2016
DOI: 10.1007/978-3-319-40970-2_36 -
(2015): SpySMAC: Automated Configuration and Performance Analysis of SAT Solvers, SAT 2015 | Datei |
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(2015): AutoFolio: An Automatically Configured Algorithm Selector, Journal of Artificial Intelligence Research (2015)
DOI: https://doi.org/10.1613/jair.4726 -
(2015): AutoFolio: Algorithm Configuration for Algorithm Selection, Association for the Advancement of Artificial Intelligence (AAAI) Workshop: Algorithm Configuration 2015 | Datei |
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(2015): ASlib: A Benchmark Library for Algorithm Selection, Computing Research Repository (CoRR) (2015)
DOI: https://doi.org/10.48550/arXiv.1506.02465
arXiv: 1506.02465 -
(2015): From Sequential Algorithm Selection to Parallel Portfolio Selection, Learning and Intelligent Optimization (LION) 2015
DOI: 10.1007/978-3-319-19084-6_1 -
(2014): claspfolio 2: Advances in Algorithm Selection for Answer Set Programming, Theory and Practice of Logic Programming
DOI: 10.1017/S1471068414000210
arXiv: 1405.1520 -
(2014): AClib: a Benchmark Library for Algorithm Configuration, Learning and Intelligent Optimization (LION 8)
DOI: https://doi.org/10.1007/978-3-319-09584-4
ISBN: 978-3-319-09583-7
ISSN: 0302-9743 -
(2013): aspeed: Solver Scheduling via Answer Set Programming, Theory Pract. Log. Program. 15(1): 117-142 (2015)
DOI: 10.1017/S1471068414000015
arXiv: 1401.1024 -
(2013): Robust Benchmark Set Selection for Boolean Constraint Solvers, Learning and Intelligent Optimization (LION) 2013
DOI: https://doi.org/10.1007/978-3-642-44973-4_16
ISBN: 978-3-642-44972-7 -
(2012): aspeed: ASP-based Solver Scheduling, International Conference on Logic Programming (ICLP (Technical Communications)) 2012 | Datei |
ISBN: 978-3-939897-43-9
ISSN: 1868-8969 -
(2012): Surviving Solver Sensitivity: An ASP Practitioner’s Guide, International Conference on Logic Programming (ICLP (Technical Communications)) 2012
DOI: 10.4230/LIPIcs.ICLP.2012.164
ISBN: 978-3-939897-43-9
ISSN: 1868-8969 -
(2012): Algorithm Configuration for Portfolio-based Parallel SAT-Solving, Proceedings of the ECAI-12 Workshop on Combining Constraint Solving with MIning and Learning | Datei |
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(2012): Quantifying Homogeneity of Instance Sets for Algorithm Configuration, Learning and Intelligent Optimization (LION) 2012
DOI: https://doi.org/10.1007/978-3-642-34413-8_14
ISBN: 978-3-642-34412-1 -
(2011): A Portfolio Solver for Answer Set Programming: Preliminary Report, Logic Programming and Non-Monotonic Reasoning (LPNMR) 2011
DOI: https://doi.org/10.1007/978-3-642-20895-9_40
ISBN: 978-3-642-20894-2 -
(2011): Potassco: The Potsdam Answer Set Solving Collection, AI Commun. (2011)
DOI: 10.3233/AIC-2011-0491 -
(2010): Centurio, a General Game Player: Parallel, Java- and ASP-based, Künstliche Intell. (2011)
DOI: https://doi.org/10.1007/s13218-010-0077-4 -
(2012): clasp, claspfolio, aspeed: Three Solvers from the Answer Set Solving Collection Potassco | Datei |
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(2013): Ricochet Robots: A transverse ASP benchmark, Logic Programming and Non-Monotonic Reasoning (LPNMR) 2013
DOI: https://doi.org/10.1007/978-3-642-40564-8_35
ISBN: 978-3-642-40563-1 -
(2022): PI is back! Switching Acquisition Functions in Bayesian Optimization, 2022 NeurIPS Workshop on Gaussian Processes, Spatiotemporal Modeling, and Decision-making Systems
arXiv: 2211.01455 -
(2021): AutoML for Multi-Label Classification: Overview and Empirical Evaluation, IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI)
DOI: 10.1109/tpami.2021.3051276 -
(2020): Extreme Algorithm Selection with Dyadic Feature Representation, Discovery Science 2020
DOI: 10.1007/978-3-030-61527-7_21
arXiv: 2001.10741 -
(2021): Predicting Machine Learning Pipeline Runtimes in the Context of Automated Machine Learning, IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI)
DOI: 10.1109/TPAMI.2021.3056950 -
(2020): Run2Survive: A Decision-theoretic Approach to Algorithm Selection based on Survival Analysis, 12th Asian Conference on Machine Learning (ACML)
DOI: 10.48550/arXiv.2007.02816
arXiv: 2007.02816 -
(2019): Algorithm Selection as Recommendation: From Collaborative Filtering to Dyad Ranking, 29th Workshop Computational Intelligence
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(2020): Hybrid Ranking and Regression for Algorithm Selection, 43rd German Conference on Artificial Intelligence (KI 2020)
DOI: 10.1007/978-3-030-58285-2_5 -
(2021): AutoML for Predictive Maintenance: One Tool to RUL them all, IoTStream @ ECMLPKDD 2020, 2020
DOI: 10.1007/978-3-030-66770-2_8 -
(2019): Automating Multi-Label Classification Extending ML-Plan, 6th ICML Workshop on Automated Machine Learning
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(2021): Coevolution of Remaining Useful Lifetime Estimation Pipelines for Automated Predictive Maintenance, Proceedings of the Genetic and Evolutionary Computation Conference (GECCO 2021)
DOI: 10.1145/3449639.3459395 -
(2021): Towards Green Automated Machine Learning: Status Quo and Future Directions, ArXiv
arXiv: 2111.05850 -
(2020): Towards Meta-Algorithm Selection, Workshop on Meta-Learning (MetaLearn 2020) @ NeurIPS 2020
arXiv: 2011.08784 -
(2020): LiBRe: Label-Wise Selection of Base Learners in Binary Relevance for Multi-Label Classification, Symposium on Intelligent Data Analysis (IDA 2020)
DOI: 10.1007/978-3-030-44584-3_44 -
(2022): A Survey of Methods for Automated Algorithm Configuration, Journal of Artificial Intelligence Research
DOI: 10.1613/jair.1.13676 -
(2021): Automated Machine Learning, Bounded Rationality, and Rational Metareasoning, ECMLPKDD Workshop on Automating Data Science (ADS2021)
DOI: 2109.04744 -
(2019): From Automated to On-The-Fly Machine Learning, INFORMATIK 2019: 50 Jahre Gesellschaft für Informatik–Informatik für Gesellschaft
DOI: 10.18420/inf2019_40 -
(2022): A comparison of heuristic, statistical, and machine learning methods for heated tool butt welding of two different materials, Welding in the World
DOI: 10.1007/s40194-022-01339-9 -
(2022): HARRIS: Hybrid Ranking and Regression Forests for Algorithm Selection, Workshop on Meta-Learning (MetaLearn 2022) @ NeurIPS 2022
arXiv: 2210.17341 -
(2022): Machine Learning for Online Algorithm Selection under Censored Feedback, Proceedings of the 36th AAAI Conference on Artificial Intelligence (AAAI 2022)
DOI: 10.1609/aaai.v36i9.21279 -
(2017): AutoFolio: An Automatically Configured Algorithm Selector (Extended Abstract), International Joint Conference on Artificial Intelligence (IJCAI) 2017
DOI: https://doi.org/10.24963/ijcai.2017/715 -
(2018): Practical Automated Machine Learning for the AutoML Challenge 2018, International Conference on Machine Learning (ICML) 2018 AutoML Workshop | Datei |
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(2019): Towards White-box Benchmarks for Algorithm Control, CoRR abs/1906.07644 (2019)
arXiv: 1906.07644 -
(2019): An Evolution Strategy with Progressive Episode Lengths for Playing Games, IJCAI 2019: 1234-1240
DOI: https://doi.org/10.24963/ijcai.2019/172 -
(2020): Dynamic Algorithm Configuration: Foundation of a New Meta-Algorithmic Framework, European Conference on Artificial Intelligence (ECAI) 2020
DOI: 10.3233/FAIA200122 -
(2022): Learning Domain-Independent Policies for Open List Selection, Proceedings of the 3rd ICAPS workshop on Bridging the Gap Between AI Planning and Reinforcement Learning (PRL), pp. 1-9 | Datei |
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(2017): What can we learn from algorithm selection data? (Breakout Session Report), Dagstuhl Reports
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(2021): Algorithm Selection as Superset Learning: Constructing Algorithm Selectors from Imprecise Performance Data, 25th Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD-2021)
DOI: 10.1007/978-3-030-75762-5_13