Marius Lindauer

Ziawasch Abedjan

Proceedings of the Proceedings 28th International Conference on Extending Database Technology, 2025

2024

AutoML in heavily constrained applications.

[BibT_eX]

[DOI]

Felix Neutatz

Ziawasch Abedjan

VLDB J., July, 2024

AutoML in the Age of Large Language Models: Current Challenges, Future Opportunities and Risks.

[BibT_eX]

[DOI]

Daphne Theodorakopoulos

Tanja Tornede

Henning Wachsmuth

Trans. Mach. Learn. Res., 2024

Structure in Deep Reinforcement Learning: A Survey and Open Problems.

[BibT_eX]

[DOI]

Aditya Mohan

Amy Zhang

J. Artif. Intell. Res., 2024

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

[BibT_eX]

[DOI]

CoRR, 2024

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

[BibT_eX]

[DOI]

Difan Deng

CoRR, 2024

Position: A Call to Action for a Human-Centered AutoML Paradigm.

[BibT_eX]

[DOI]

CoRR, 2024

Hyperparameter Importance Analysis for Multi-Objective AutoML.

[BibT_eX]

[DOI]

Daphne Theodorakopoulos

Frederic T. Stahl

CoRR, 2024

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

[BibT_eX]

[DOI]

Leona Hennig

Tanja Tornede

CoRR, 2024

Position: A Call to Action for a Human-Centered AutoML Paradigm.

[BibT_eX]

[DOI]

Proceedings of the Forty-first International Conference on Machine Learning, 2024

Instance Selection for Dynamic Algorithm Configuration with Reinforcement Learning: Improving Generalization.

[BibT_eX]

[DOI]

Proceedings of the Genetic and Evolutionary Computation Conference Companion, 2024

Hyperparameter Importance Analysis for Multi-Objective AutoML.

[BibT_eX]

[DOI]

Frederic T. Stahl

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.

[BibT_eX]

[DOI]

Proceedings of the Thirty-Eighth AAAI Conference on Artificial Intelligence, 2024

2023

Hyperparameter optimization: Foundations, algorithms, best practices, and open challenges.

[BibT_eX]

[DOI]

Anne-Laure Boulesteix

Difan Deng

WIREs Data. Mining. Knowl. Discov., 2023

POLTER: Policy Trajectory Ensemble Regularization for Unsupervised Reinforcement Learning.

[BibT_eX]

[DOI]

Trans. Mach. Learn. Res., 2023

MASIF: Meta-learned Algorithm Selection using Implicit Fidelity Information.

[BibT_eX]

[DOI]

Trans. Mach. Learn. Res., 2023

Contextualize Me - The Case for Context in Reinforcement Learning.

[BibT_eX]

[DOI]

Trans. Mach. Learn. Res., 2023

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

[BibT_eX]

[DOI]

Dagstuhl Reports, 2023

auto-sktime: Automated Time Series Forecasting.

[BibT_eX]

[DOI]

Marc-André Zöller

Marco F. Huber

CoRR, 2023

Structure in Reinforcement Learning: A Survey and Open Problems.

[BibT_eX]

[DOI]

Aditya Mohan

Amy Zhang

CoRR, 2023

Automated Machine Learning for Remaining Useful Life Predictions.

[BibT_eX]

[DOI]

Proceedings of the IEEE International Conference on Systems, Man, and Cybernetics, 2023

PriorBand: Practical Hyperparameter Optimization in the Age of Deep Learning.

[BibT_eX]

[DOI]

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.

[BibT_eX]

[DOI]

Theresa Eimer

Roberta Raileanu

Proceedings of the International Conference on Machine Learning, 2023

Towards Self-Adjusting Weighted Expected Improvement for Bayesian Optimization.

[BibT_eX]

[DOI]

Proceedings of the Companion Proceedings of the Conference on Genetic and Evolutionary Computation, 2023

Symbolic Explanations for Hyperparameter Optimization.

[BibT_eX]

[DOI]

Proceedings of the International Conference on Automated Machine Learning, 2023

AutoRL Hyperparameter Landscapes.

[BibT_eX]

[DOI]

Proceedings of the International Conference on Automated Machine Learning, 2023

Learning Activation Functions for Sparse Neural Networks.

[BibT_eX]

[DOI]

Proceedings of the International Conference on Automated Machine Learning, 2023

Self-Adjusting Weighted Expected Improvement for Bayesian Optimization.

[BibT_eX]

[DOI]

Proceedings of the International Conference on Automated Machine Learning, 2023

2022

SMAC3: A Versatile Bayesian Optimization Package for Hyperparameter Optimization.

[BibT_eX]

[DOI]

J. Mach. Learn. Res., 2022

Auto-Sklearn 2.0: Hands-free AutoML via Meta-Learning.

[BibT_eX]

[DOI]

Stefan Falkner

J. Mach. Learn. Res., 2022

Automated Reinforcement Learning (AutoRL): A Survey and Open Problems.

[BibT_eX]

[DOI]

J. Artif. Intell. Res., 2022

Automated Dynamic Algorithm Configuration.

[BibT_eX]

[DOI]

J. Artif. Intell. Res., 2022

Hyperparameters in Contextual RL are Highly Situational.

[BibT_eX]

[DOI]

Theresa Eimer

Carolin Benjamins

CoRR, 2022

Towards Automated Design of Bayesian Optimization via Exploratory Landscape Analysis.

[BibT_eX]

[DOI]

CoRR, 2022

PI is back! Switching Acquisition Functions in Bayesian Optimization.

[BibT_eX]

[DOI]

CoRR, 2022

Enhancing Explainability of Hyperparameter Optimization via Bayesian Algorithm Execution.

[BibT_eX]

[DOI]

CoRR, 2022

DeepCAVE: An Interactive Analysis Tool for Automated Machine Learning.

[BibT_eX]

[DOI]

CoRR, 2022

Towards Meta-learned Algorithm Selection using Implicit Fidelity Information.

[BibT_eX]

[DOI]

Aditya Mohan

Tim Ruhkopf

CoRR, 2022

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

[BibT_eX]

[DOI]

CoRR, 2022

Contextualize Me - The Case for Context in Reinforcement Learning.

[BibT_eX]

[DOI]

CoRR, 2022

Developing Open Source Educational Resources for Machine Learning and Data Science.

[BibT_eX]

[DOI]

Proceedings of the Third Teaching Machine Learning and Artificial Intelligence Workshop, 2022

Efficient Automated Deep Learning for Time Series Forecasting.

[BibT_eX]

[DOI]

Proceedings of the Machine Learning and Knowledge Discovery in Databases, 2022

$\pi$BO: Augmenting Acquisition Functions with User Beliefs for Bayesian Optimization.

[BibT_eX]

[DOI]

Proceedings of the Tenth International Conference on Learning Representations, 2022

Searching in the Forest for Local Bayesian Optimization.

[BibT_eX]

[DOI]

Difan Deng

Proceedings of the ECML/PKDD Workshop on Meta-Knowledge Transfer, 2022

2021

Auto-Pytorch: Multi-Fidelity MetaLearning for Efficient and Robust AutoDL.

[BibT_eX]

[DOI]

Lucas Zimmer

IEEE Trans. Pattern Anal. Mach. Intell., 2021

Winning Solutions and Post-Challenge Analyses of the ChaLearn AutoDL Challenge 2019.

[BibT_eX]

[DOI]

IEEE Trans. Pattern Anal. Mach. Intell., 2021

CARL: A Benchmark for Contextual and Adaptive Reinforcement Learning.

[BibT_eX]

[DOI]

CoRR, 2021

SMAC3: A Versatile Bayesian Optimization Package for Hyperparameter Optimization.

[BibT_eX]

[DOI]

CoRR, 2021

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

[BibT_eX]

[DOI]

CoRR, 2021

Automatic Risk Adaptation in Distributional Reinforcement Learning.

[BibT_eX]

[DOI]

CoRR, 2021

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

[BibT_eX]

[DOI]

CoRR, 2021

Bayesian Optimization with a Prior for the Optimum.

[BibT_eX]

[DOI]

Proceedings of the Machine Learning and Knowledge Discovery in Databases. Research Track, 2021

Explaining Hyperparameter Optimization via Partial Dependence Plots.

[BibT_eX]

[DOI]

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.

[BibT_eX]

[DOI]

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.

[BibT_eX]

[DOI]

Proceedings of the Neural Information Processing Systems Track on Datasets and Benchmarks 1, 2021

DACBench: A Benchmark Library for Dynamic Algorithm Configuration.

[BibT_eX]

[DOI]

Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence, 2021

Self-Paced Context Evaluation for Contextual Reinforcement Learning.

[BibT_eX]

[DOI]

Proceedings of the 38th International Conference on Machine Learning, 2021

TempoRL: Learning When to Act.

[BibT_eX]

[DOI]

Proceedings of the 38th International Conference on Machine Learning, 2021

Learning Heuristic Selection with Dynamic Algorithm Configuration.

[BibT_eX]

[DOI]

Proceedings of the Thirty-First International Conference on Automated Planning and Scheduling, 2021

2020

Squirrel: A Switching Hyperparameter Optimizer.

[BibT_eX]

[DOI]

CoRR, 2020

Neural Model-based Optimization with Right-Censored Observations.

[BibT_eX]

[DOI]

CoRR, 2020

Auto-Sklearn 2.0: The Next Generation.

[BibT_eX]

[DOI]

Stefan Falkner

CoRR, 2020

Prior-guided Bayesian Optimization.

[BibT_eX]

[DOI]

CoRR, 2020

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

[BibT_eX]

[DOI]

Lucas Zimmer

CoRR, 2020

Learning Step-Size Adaptation in CMA-ES.

[BibT_eX]

[DOI]

Proceedings of the Parallel Problem Solving from Nature - PPSN XVI, 2020

Dynamic Algorithm Configuration: Foundation of a New Meta-Algorithmic Framework.

[BibT_eX]

[DOI]

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.

[BibT_eX]

[DOI]

J. Artif. Intell. Res., 2019

Best Practices for Scientific Research on Neural Architecture Search.

[BibT_eX]

[DOI]

CoRR, 2019

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

[BibT_eX]

[DOI]

CoRR, 2019

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

[BibT_eX]

[DOI]

André Biedenkapp

CoRR, 2019

Towards White-box Benchmarks for Algorithm Control.

[BibT_eX]

[DOI]

CoRR, 2019

The algorithm selection competitions 2015 and 2017.

[BibT_eX]

[DOI]

Jan N. van Rijn

Lars Kotthoff

Artif. Intell., 2019

An Evolution Strategy with Progressive Episode Lengths for Playing Games.

[BibT_eX]

[DOI]

Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence, 2019

Hands-On Automated Machine Learning Tools: Auto-Sklearn and Auto-PyTorch.

[BibT_eX]

[DOI]

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!

[BibT_eX]

[DOI]

Towards Automatically-Tuned Deep Neural Networks.

[BibT_eX]

[DOI]

Hector Mendoza

Aaron Klein

Jost Tobias Springenberg

Proceedings of the Automated Machine Learning - Methods, Systems, Challenges, 2019

2018

Efficient benchmarking of algorithm configurators via model-based surrogates.

[BibT_eX]

[DOI]

Mach. Learn., 2018

A case study of algorithm selection for the traveling thief problem.

[BibT_eX]

[DOI]

J. Heuristics, 2018

The Algorithm Selection Competition Series 2015-17.

[BibT_eX]

[DOI]

Jan N. van Rijn

Lars Kotthoff

CoRR, 2018

CAVE: Configuration Assessment, Visualization and Evaluation.

[BibT_eX]

[DOI]

Proceedings of the Learning and Intelligent Optimization - 12th International Conference, 2018

Neural Networks for Predicting Algorithm Runtime Distributions.

[BibT_eX]

[DOI]

Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence, 2018

Warmstarting of Model-Based Algorithm Configuration.

[BibT_eX]

[DOI]

Proceedings of the Thirty-Second AAAI Conference on Artificial Intelligence, 2018

Selection and Configuration of Parallel Portfolios.

[BibT_eX]

[DOI]

Proceedings of the Handbook of Parallel Constraint Reasoning., 2018

2017

Predicting Runtime Distributions using Deep Neural Networks.

[BibT_eX]

[DOI]

CoRR, 2017

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

[BibT_eX]

[DOI]

CoRR, 2017

Automatic construction of parallel portfolios via algorithm configuration.

[BibT_eX]

[DOI]

Artif. Intell., 2017

The Configurable SAT Solver Challenge (CSSC).

[BibT_eX]

[DOI]

Artif. Intell., 2017

Open Algorithm Selection Challenge 2017: Setup and Scenarios.

[BibT_eX]

[DOI]

Jan N. van Rijn

Lars Kotthoff

Proceedings of the Open Algorithm Selection Challenge 2017, 2017

AutoFolio: An Automatically Configured Algorithm Selector (Extended Abstract).

[BibT_eX]

[DOI]

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.

[BibT_eX]

[DOI]

Markus Wagner

Tobias Friedrich

Proceedings of the 2017 IEEE Congress on Evolutionary Computation, 2017

Efficient Parameter Importance Analysis via Ablation with Surrogates.

[BibT_eX]

[DOI]

Andre Biedenkapp

Chris Fawcett

Proceedings of the Thirty-First AAAI Conference on Artificial Intelligence, 2017

2016

ASlib: A benchmark library for algorithm selection.

[BibT_eX]

[DOI]

Artif. Intell., 2016

SpyBug: Automated Bug Detection in the Configuration Space of SAT Solvers.

[BibT_eX]

[DOI]

Norbert Manthey

Proceedings of the Theory and Applications of Satisfiability Testing - SAT 2016, 2016

An Empirical Study of Per-instance Algorithm Scheduling.

[BibT_eX]

[DOI]

Rolf-David Bergdoll

Proceedings of the Learning and Intelligent Optimization - 10th International Conference, 2016

2015

Algorithm selection, scheduling and configuration of Boolean constraint solvers

[BibT_eX]

[DOI]

PhD thesis, 2015

aspeed: Solver scheduling via answer set programming.

[BibT_eX]

[DOI]

Theory Pract. Log. Program., 2015

AutoFolio: An Automatically Configured Algorithm Selector.

[BibT_eX]

[DOI]

J. Artif. Intell. Res., 2015

Reports from the 2015 AAAI Workshop Program.

[BibT_eX]

[DOI]

AI Mag., 2015

SpySMAC: Automated Configuration and Performance Analysis of SAT Solvers.

[BibT_eX]

[DOI]

Stefan Falkner

Proceedings of the Theory and Applications of Satisfiability Testing - SAT 2015, 2015

From Sequential Algorithm Selection to Parallel Portfolio Selection.

[BibT_eX]

[DOI]

Proceedings of the Learning and Intelligent Optimization - 9th International Conference, 2015

AutoFolio: Algorithm Configuration for Algorithm Selection.

[BibT_eX]

[DOI]

Proceedings of the Algorithm Configuration, 2015

2014

claspfolio 2: Advances in Algorithm Selection for Answer Set Programming.

[BibT_eX]

[DOI]

Torsten Schaub

Theory Pract. Log. Program., 2014

Solver Scheduling via Answer Set Programming.

[BibT_eX]

[DOI]

CoRR, 2014

AClib: A Benchmark Library for Algorithm Configuration.

[BibT_eX]

[DOI]

Proceedings of the Learning and Intelligent Optimization, 2014

2013

Ricochet Robots: A Transverse ASP Benchmark.

[BibT_eX]

[DOI]

Proceedings of the Logic Programming and Nonmonotonic Reasoning, 2013

Robust Benchmark Set Selection for Boolean Constraint Solvers.

[BibT_eX]

[DOI]

Proceedings of the Learning and Intelligent Optimization - 7th International Conference, 2013

2012

Quantifying Homogeneity of Instance Sets for Algorithm Configuration.

[BibT_eX]

[DOI]

Marius Schneider