Marius Lindauer
32 publications
14 venues
H Index 10
Affiliation
Leibniz University Hannover, Institute of Information Processing, GermanyUniversity of Freiburg, Machine Learning Lab, Germany
University of Potsdam, Institute of Computer Science, Germany
Links
DBLPGoogle Scholar
ORCID- https://www.tnt.uni-hannover.de/staff/lindauer/
- http://www2.informatik.uni-freiburg.de/~aad/people/lindauer/index.html
- http://www.cs.uni-potsdam.de/~manju/
- https://github.com/mlindauer/
- https://www.wikidata.org/entity/Q93452534
- https://en.wikipedia.org/wiki/Marius_Lindauer
- https://dblp.uni-trier.de/pers/hd/s/Schneider_0001:Marius
| Name | Venue | Year | citations |
|---|---|---|---|
Hyperparameters in Reinforcement Learning and How To Tune Them.
|
ICML | 2023 | 0 |
| PriorBand: Practical Hyperparameter Optimization in the Age of Deep Learning.
|
NIPS/NeurIPS | 2023 | 0 |
| Automated Reinforcement Learning (AutoRL): A Survey and Open Problems.
|
JAIR | 2022 | 28 |
| Automated Dynamic Algorithm Configuration.
|
JAIR | 2022 | 2 |
| Efficient Automated Deep Learning for Time Series Forecasting.
|
ECML/PKDD | 2022 | 0 |
| $\pi$BO: Augmenting Acquisition Functions with User Beliefs for Bayesian Optimization.
|
ICLR | 2022 | 0 |
| SMAC3: A Versatile Bayesian Optimization Package for Hyperparameter Optimization.
|
JMLR | 2022 | 0 |
| Auto-Sklearn 2.0: Hands-free AutoML via Meta-Learning.
|
JMLR | 2022 | 0 |
| DACBench: A Benchmark Library for Dynamic Algorithm Configuration.
|
IJCAI | 2021 | 12 |
| Well-tuned Simple Nets Excel on Tabular Datasets.
|
NIPS/NeurIPS | 2021 | 43 |
| Explaining Hyperparameter Optimization via Partial Dependence Plots.
|
NIPS/NeurIPS | 2021 | 8 |
| Self-Paced Context Evaluation for Contextual Reinforcement Learning.
|
ICML | 2021 | 7 |
| Auto-Pytorch: Multi-Fidelity MetaLearning for Efficient and Robust AutoDL.
|
TPAMI | 2021 | 30 |
| Bayesian Optimization with a Prior for the Optimum.
|
ECML/PKDD | 2021 | 17 |
| TempoRL: Learning When to Act.
|
ICML | 2021 | 8 |
| Learning Heuristic Selection with Dynamic Algorithm Configuration.
|
ICAPS | 2021 | 0 |
| Winning Solutions and Post-Challenge Analyses of the ChaLearn AutoDL Challenge 2019.
|
TPAMI | 2021 | 0 |
| Dynamic Algorithm Configuration: Foundation of a New Meta-Algorithmic Framework.
|
ECAI | 2020 | 36 |
| An Evolution Strategy with Progressive Episode Lengths for Playing Games.
|
IJCAI | 2019 | 6 |
| Pitfalls and Best Practices in Algorithm Configuration.
|
JAIR | 2019 | 0 |
| The algorithm selection competitions 2015 and 2017.
|
Artificial Intelligence | 2019 | 0 |
| Warmstarting of Model-Based Algorithm Configuration.
|
AAAI | 2018 | 0 |
| Neural Networks for Predicting Algorithm Runtime Distributions.
|
IJCAI | 2018 | 0 |
| Efficient benchmarking of algorithm configurators via model-based surrogates.
|
MLJ | 2018 | 0 |
| Automatic construction of parallel portfolios via algorithm configuration.
|
Artificial Intelligence | 2017 | 27 |
| AutoFolio: An Automatically Configured Algorithm Selector (Extended Abstract).
|
IJCAI | 2017 | 8 |
| Efficient Parameter Importance Analysis via Ablation with Surrogates.
|
AAAI | 2017 | 31 |
| The Configurable SAT Solver Challenge (CSSC).
|
Artificial Intelligence | 2017 | 0 |
| SpyBug: Automated Bug Detection in the Configuration Space of SAT Solvers.
|
SAT | 2016 | 10 |
| ASlib: A benchmark library for algorithm selection.
|
Artificial Intelligence | 2016 | 0 |
| SpySMAC: Automated Configuration and Performance Analysis of SAT Solvers.
|
SAT | 2015 | 19 |
| AutoFolio: An Automatically Configured Algorithm Selector.
|
JAIR | 2015 | 115 |