Our Involvement in the AutoML Conference 2023 – Reproducibility Chair

We are happy to announce that the ISG Siegen is involved in the organization of the AutoML Conference 2023!

Lennart Purucker, one of our Ph.D. students, is the Reproducibility Chair for the AutoML Conference 2023.

The Reproducibility Chair is a newly established chair with the goal of enabling and observing the reproducibility of scientific work published at the AutoML conference. This follows last year’s decision to introduce reproducibility reviewers and a submission checklist.

Reproducibility is essential for scientific research to guarantee that other researchers can build on previous work easily and that results can be trusted. For example, we want to make sure that code and data are publicly available such that we can obtain the same results as reported in a publication.

We’re very proud to see Lennart, who is only in his second year of his Ph.D., get to chair such a prestigious conference!

At the Automated Machine Learning Conference, a reproducibility track is a crucial aspect of the event. It allows researchers to present their work in a way that can be replicated by others, ensuring the credibility and reliability of the research being presented. This is a key element of scientific progress in the field of Automated Machine Learning and it promotes transparency and good scientific practice. Additionally, many other well-known conferences such as NeurIPS, ICML, ICLR and AISTATS have a reproducibility track, which highlights the importance of reproducibility in scientific research. By including a reproducibility track at the Automated Machine Learning Conference, organizers are encouraging and recognizing the importance of reproducibility in the field.

The AutoML Conference is a leading event for researchers and practitioners in the field of automated machine learning. The conference features presentations and discussions on the latest developments and applications of AutoML, including neural architecture search, Bayesian optimization, and interpretability. It brings together experts from academia, industry, and government to share their work and insights, and to explore the potential of AutoML to make machine learning more accessible and impactful. The conference is a valuable opportunity for individuals in the field to network, collaborate, and stay informed about the latest advances in AutoML.