Marius Lindauer

Orcid: 0000-0002-9675-3175

Affiliations:
  • Leibniz University Hannover, Institute of Information Processing, Germany (since 2019)
  • University of Freiburg, Machine Learning Lab, Germany (2014-2019)
  • University of Potsdam, Institute of Computer Science, Germany (PhD 2015)


According to our database1, Marius Lindauer authored at least 112 papers between 2011 and 2025.

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Bibliography

2025
How Green is AutoML for Tabular Data?
Proceedings of the Proceedings 28th International Conference on Extending Database Technology, 2025

2024
AutoML in heavily constrained applications.
VLDB J., July, 2024

AutoML in the Age of Large Language Models: Current Challenges, Future Opportunities and Risks.
Trans. Mach. Learn. Res., 2024

AMLTK: A Modular AutoML Toolkit in Python.
J. Open Source Softw., 2024

Structure in Deep Reinforcement Learning: A Survey and Open Problems.
J. Artif. Intell. Res., 2024

ARLBench: Flexible and Efficient Benchmarking for Hyperparameter Optimization in Reinforcement Learning.
CoRR, 2024

Optimizing Time Series Forecasting Architectures: A Hierarchical Neural Architecture Search Approach.
CoRR, 2024

Hyperparameter Importance Analysis for Multi-Objective AutoML.
CoRR, 2024

Towards Leveraging AutoML for Sustainable Deep Learning: A Multi-Objective HPO Approach on Deep Shift Neural Networks.
CoRR, 2024

Position: A Call to Action for a Human-Centered AutoML Paradigm.
Proceedings of the Forty-first International Conference on Machine Learning, 2024

Instance Selection for Dynamic Algorithm Configuration with Reinforcement Learning: Improving Generalization.
Proceedings of the Genetic and Evolutionary Computation Conference Companion, 2024

Hyperparameter Importance Analysis for Multi-Objective AutoML.
Proceedings of the ECAI 2024 - 27th European Conference on Artificial Intelligence, 19-24 October 2024, Santiago de Compostela, Spain, 2024

Interactive Hyperparameter Optimization in Multi-Objective Problems via Preference Learning.
Proceedings of the Thirty-Eighth AAAI Conference on Artificial Intelligence, 2024

2023
Hyperparameter optimization: Foundations, algorithms, best practices, and open challenges.
WIREs Data. Mining. Knowl. Discov., 2023

POLTER: Policy Trajectory Ensemble Regularization for Unsupervised Reinforcement Learning.
Trans. Mach. Learn. Res., 2023

MASIF: Meta-learned Algorithm Selection using Implicit Fidelity Information.
Trans. Mach. Learn. Res., 2023

Contextualize Me - The Case for Context in Reinforcement Learning.
Trans. Mach. Learn. Res., 2023

Synergizing Theory and Practice of Automated Algorithm Design for Optimization (Dagstuhl Seminar 23332).
Dagstuhl Reports, 2023

auto-sktime: Automated Time Series Forecasting.
CoRR, 2023

Structure in Reinforcement Learning: A Survey and Open Problems.
CoRR, 2023

Automated Machine Learning for Remaining Useful Life Predictions.
Proceedings of the IEEE International Conference on Systems, Man, and Cybernetics, 2023

PriorBand: Practical Hyperparameter Optimization in the Age of Deep Learning.
Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Hyperparameters in Reinforcement Learning and How To Tune Them.
Proceedings of the International Conference on Machine Learning, 2023

Towards Self-Adjusting Weighted Expected Improvement for Bayesian Optimization.
Proceedings of the Companion Proceedings of the Conference on Genetic and Evolutionary Computation, 2023

Symbolic Explanations for Hyperparameter Optimization.
Proceedings of the International Conference on Automated Machine Learning, 2023

AutoRL Hyperparameter Landscapes.
Proceedings of the International Conference on Automated Machine Learning, 2023

Learning Activation Functions for Sparse Neural Networks.
Proceedings of the International Conference on Automated Machine Learning, 2023

Self-Adjusting Weighted Expected Improvement for Bayesian Optimization.
Proceedings of the International Conference on Automated Machine Learning, 2023

2022
SMAC3: A Versatile Bayesian Optimization Package for Hyperparameter Optimization.
J. Mach. Learn. Res., 2022

Auto-Sklearn 2.0: Hands-free AutoML via Meta-Learning.
J. Mach. Learn. Res., 2022

Automated Reinforcement Learning (AutoRL): A Survey and Open Problems.
J. Artif. Intell. Res., 2022

Automated Dynamic Algorithm Configuration.
J. Artif. Intell. Res., 2022

Hyperparameters in Contextual RL are Highly Situational.
CoRR, 2022

Towards Automated Design of Bayesian Optimization via Exploratory Landscape Analysis.
CoRR, 2022

PI is back! Switching Acquisition Functions in Bayesian Optimization.
CoRR, 2022

Enhancing Explainability of Hyperparameter Optimization via Bayesian Algorithm Execution.
CoRR, 2022

DeepCAVE: An Interactive Analysis Tool for Automated Machine Learning.
CoRR, 2022

Towards Meta-learned Algorithm Selection using Implicit Fidelity Information.
CoRR, 2022

πBO: Augmenting Acquisition Functions with User Beliefs for Bayesian Optimization.
CoRR, 2022

Contextualize Me - The Case for Context in Reinforcement Learning.
CoRR, 2022

Developing Open Source Educational Resources for Machine Learning and Data Science.
Proceedings of the Third Teaching Machine Learning and Artificial Intelligence Workshop, 2022

Efficient Automated Deep Learning for Time Series Forecasting.
Proceedings of the Machine Learning and Knowledge Discovery in Databases, 2022

$\pi$BO: Augmenting Acquisition Functions with User Beliefs for Bayesian Optimization.
Proceedings of the Tenth International Conference on Learning Representations, 2022

Searching in the Forest for Local Bayesian Optimization.
Proceedings of the ECML/PKDD Workshop on Meta-Knowledge Transfer, 2022

2021
Auto-Pytorch: Multi-Fidelity MetaLearning for Efficient and Robust AutoDL.
IEEE Trans. Pattern Anal. Mach. Intell., 2021

Winning Solutions and Post-Challenge Analyses of the ChaLearn AutoDL Challenge 2019.
IEEE Trans. Pattern Anal. Mach. Intell., 2021

CARL: A Benchmark for Contextual and Adaptive Reinforcement Learning.
CoRR, 2021

SMAC3: A Versatile Bayesian Optimization Package for Hyperparameter Optimization.
CoRR, 2021

Regularization is all you Need: Simple Neural Nets can Excel on Tabular Data.
CoRR, 2021

Automatic Risk Adaptation in Distributional Reinforcement Learning.
CoRR, 2021

Bag of Baselines for Multi-objective Joint Neural Architecture Search and Hyperparameter Optimization.
CoRR, 2021

Bayesian Optimization with a Prior for the Optimum.
Proceedings of the Machine Learning and Knowledge Discovery in Databases. Research Track, 2021

Explaining Hyperparameter Optimization via Partial Dependence Plots.
Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

Well-tuned Simple Nets Excel on Tabular Datasets.
Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

HPOBench: A Collection of Reproducible Multi-Fidelity Benchmark Problems for HPO.
Proceedings of the Neural Information Processing Systems Track on Datasets and Benchmarks 1, 2021

DACBench: A Benchmark Library for Dynamic Algorithm Configuration.
Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence, 2021

Self-Paced Context Evaluation for Contextual Reinforcement Learning.
Proceedings of the 38th International Conference on Machine Learning, 2021

TempoRL: Learning When to Act.
Proceedings of the 38th International Conference on Machine Learning, 2021

Learning Heuristic Selection with Dynamic Algorithm Configuration.
Proceedings of the Thirty-First International Conference on Automated Planning and Scheduling, 2021

2020
Squirrel: A Switching Hyperparameter Optimizer.
CoRR, 2020

Neural Model-based Optimization with Right-Censored Observations.
CoRR, 2020

Auto-Sklearn 2.0: The Next Generation.
CoRR, 2020

Prior-guided Bayesian Optimization.
CoRR, 2020

Auto-PyTorch Tabular: Multi-Fidelity MetaLearning for Efficient and Robust AutoDL.
CoRR, 2020

Learning Step-Size Adaptation in CMA-ES.
Proceedings of the Parallel Problem Solving from Nature - PPSN XVI, 2020

Dynamic Algorithm Configuration: Foundation of a New Meta-Algorithmic Framework.
Proceedings of the ECAI 2020 - 24th European Conference on Artificial Intelligence, 29 August-8 September 2020, Santiago de Compostela, Spain, August 29 - September 8, 2020, 2020

2019
Pitfalls and Best Practices in Algorithm Configuration.
J. Artif. Intell. Res., 2019

Best Practices for Scientific Research on Neural Architecture Search.
CoRR, 2019

BOAH: A Tool Suite for Multi-Fidelity Bayesian Optimization & Analysis of Hyperparameters.
CoRR, 2019

Towards Assessing the Impact of Bayesian Optimization's Own Hyperparameters.
CoRR, 2019

Towards White-box Benchmarks for Algorithm Control.
CoRR, 2019

The algorithm selection competitions 2015 and 2017.
Artif. Intell., 2019

An Evolution Strategy with Progressive Episode Lengths for Playing Games.
Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence, 2019

Hands-On Automated Machine Learning Tools: Auto-Sklearn and Auto-PyTorch.
Proceedings of the 1st Interdisciplinary Workshop on Algorithm Selection and Meta-Learning in Information Retrieval co-located with the 41st European Conference on Information Retrieval (ECIR 2019), 2019

Automated Algorithm Selection - Predict which algorithm to use!
Proceedings of the 1st Interdisciplinary Workshop on Algorithm Selection and Meta-Learning in Information Retrieval co-located with the 41st European Conference on Information Retrieval (ECIR 2019), 2019

Towards Automatically-Tuned Deep Neural Networks.
Proceedings of the Automated Machine Learning - Methods, Systems, Challenges, 2019

2018
Efficient benchmarking of algorithm configurators via model-based surrogates.
Mach. Learn., 2018

A case study of algorithm selection for the traveling thief problem.
J. Heuristics, 2018

The Algorithm Selection Competition Series 2015-17.
CoRR, 2018

CAVE: Configuration Assessment, Visualization and Evaluation.
Proceedings of the Learning and Intelligent Optimization - 12th International Conference, 2018

Neural Networks for Predicting Algorithm Runtime Distributions.
Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence, 2018

Warmstarting of Model-Based Algorithm Configuration.
Proceedings of the Thirty-Second AAAI Conference on Artificial Intelligence, 2018

Selection and Configuration of Parallel Portfolios.
Proceedings of the Handbook of Parallel Constraint Reasoning., 2018

2017
Predicting Runtime Distributions using Deep Neural Networks.
CoRR, 2017

Efficient Benchmarking of Algorithm Configuration Procedures via Model-Based Surrogates.
CoRR, 2017

Automatic construction of parallel portfolios via algorithm configuration.
Artif. Intell., 2017

The Configurable SAT Solver Challenge (CSSC).
Artif. Intell., 2017

Open Algorithm Selection Challenge 2017: Setup and Scenarios.
Proceedings of the Open Algorithm Selection Challenge 2017, 2017

AutoFolio: An Automatically Configured Algorithm Selector (Extended Abstract).
Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence, 2017

Improving local search in a minimum vertex cover solver for classes of networks.
Proceedings of the 2017 IEEE Congress on Evolutionary Computation, 2017

Efficient Parameter Importance Analysis via Ablation with Surrogates.
Proceedings of the Thirty-First AAAI Conference on Artificial Intelligence, 2017

2016
ASlib: A benchmark library for algorithm selection.
Artif. Intell., 2016

SpyBug: Automated Bug Detection in the Configuration Space of SAT Solvers.
Proceedings of the Theory and Applications of Satisfiability Testing - SAT 2016, 2016

An Empirical Study of Per-instance Algorithm Scheduling.
Proceedings of the Learning and Intelligent Optimization - 10th International Conference, 2016

2015
Algorithm selection, scheduling and configuration of Boolean constraint solvers
PhD thesis, 2015

aspeed: Solver scheduling via answer set programming.
Theory Pract. Log. Program., 2015

AutoFolio: An Automatically Configured Algorithm Selector.
J. Artif. Intell. Res., 2015

Reports from the 2015 AAAI Workshop Program.
AI Mag., 2015

SpySMAC: Automated Configuration and Performance Analysis of SAT Solvers.
Proceedings of the Theory and Applications of Satisfiability Testing - SAT 2015, 2015

From Sequential Algorithm Selection to Parallel Portfolio Selection.
Proceedings of the Learning and Intelligent Optimization - 9th International Conference, 2015

AutoFolio: Algorithm Configuration for Algorithm Selection.
Proceedings of the Algorithm Configuration, 2015

2014
claspfolio 2: Advances in Algorithm Selection for Answer Set Programming.
Theory Pract. Log. Program., 2014

Solver Scheduling via Answer Set Programming.
CoRR, 2014

AClib: A Benchmark Library for Algorithm Configuration.
Proceedings of the Learning and Intelligent Optimization, 2014

2013
Ricochet Robots: A Transverse ASP Benchmark.
Proceedings of the Logic Programming and Nonmonotonic Reasoning, 2013

Robust Benchmark Set Selection for Boolean Constraint Solvers.
Proceedings of the Learning and Intelligent Optimization - 7th International Conference, 2013

2012
Quantifying Homogeneity of Instance Sets for Algorithm Configuration.
Proceedings of the Learning and Intelligent Optimization - 6th International Conference, 2012

Surviving Solver Sensitivity: An ASP Practitioner's Guide.
Proceedings of the Technical Communications of the 28th International Conference on Logic Programming, 2012

aspeed: ASP-based Solver Scheduling.
Proceedings of the Technical Communications of the 28th International Conference on Logic Programming, 2012

2011
Centurio, a General Game Player: Parallel, Java- and ASP-based.
Künstliche Intell., 2011

Potassco: The Potsdam Answer Set Solving Collection.
AI Commun., 2011

A Portfolio Solver for Answer Set Programming: Preliminary Report.
Proceedings of the Logic Programming and Nonmonotonic Reasoning, 2011


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