Alexander Tornede

Orcid: 0000-0002-2415-2186

According to our database1, Alexander Tornede authored at least 26 papers between 2019 and 2024.

Collaborative distances:
  • Dijkstra number2 of five.
  • Erdős number3 of four.

Timeline

Legend:

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Online presence:

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Bibliography

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

Position: A Call to Action for a Human-Centered AutoML Paradigm.
CoRR, 2024

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

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

2023
PyExperimenter: Easily distribute experiments and track results.
J. Open Source Softw., June, 2023

Algorithm selection on a meta level.
Mach. Learn., April, 2023

Advanced Algorithm Selection with Machine Learning: Handling Large Algorithm Sets, Learning From Censored Data and Simplifying Meta Level Decisions.
PhD thesis, 2023

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

Towards Green Automated Machine Learning: Status Quo and Future Directions.
J. Artif. Intell. Res., 2023

A Survey of Methods for Automated Algorithm Configuration (Extended Abstract).
Proceedings of the Thirty-Second International Joint Conference on Artificial Intelligence, 2023

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

2022
A Survey of Methods for Automated Algorithm Configuration.
J. Artif. Intell. Res., 2022

HARRIS: Hybrid Ranking and Regression Forests for Algorithm Selection.
CoRR, 2022

Machine Learning for Online Algorithm Selection under Censored Feedback.
Proceedings of the Thirty-Sixth AAAI Conference on Artificial Intelligence, 2022

2021
AutoML for Multi-Label Classification: Overview and Empirical Evaluation.
IEEE Trans. Pattern Anal. Mach. Intell., 2021

Predicting Machine Learning Pipeline Runtimes in the Context of Automated Machine Learning.
IEEE Trans. Pattern Anal. Mach. Intell., 2021

Automated Machine Learning, Bounded Rationality, and Rational Metareasoning.
CoRR, 2021

Algorithm Selection as Superset Learning: Constructing Algorithm Selectors from Imprecise Performance Data.
Proceedings of the Advances in Knowledge Discovery and Data Mining, 2021

Coevolution of remaining useful lifetime estimation pipelines for automated predictive maintenance.
Proceedings of the GECCO '21: Genetic and Evolutionary Computation Conference, 2021

2020
Towards Meta-Algorithm Selection.
CoRR, 2020

AutoML for Predictive Maintenance: One Tool to RUL Them All.
Proceedings of the IoT Streams for Data-Driven Predictive Maintenance and IoT, Edge, and Mobile for Embedded Machine Learning, 2020

Hybrid Ranking and Regression for Algorithm Selection.
Proceedings of the KI 2020: Advances in Artificial Intelligence, 2020

LiBRe: Label-Wise Selection of Base Learners in Binary Relevance for Multi-label Classification.
Proceedings of the Advances in Intelligent Data Analysis XVIII, 2020

Extreme Algorithm Selection with Dyadic Feature Representation.
Proceedings of the Discovery Science - 23rd International Conference, 2020

Run2Survive: A Decision-theoretic Approach to Algorithm Selection based on Survival Analysis.
Proceedings of The 12th Asian Conference on Machine Learning, 2020

2019
From Automated to On-The-Fly Machine Learning.
Proceedings of the 49. Jahrestagung der Gesellschaft für Informatik, 50 Jahre Gesellschaft für Informatik, 2019


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