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

Posted on July 18, 2024 by Marius Lindauer

Paper Authors Marius Lindauer, Florian Karl, Anne Klier, Julia Moosbauer, Alexander Tornede, Andreas Mueller, Frank Hutter, Matthias Feurer, Bernd Bischl Motivation Automated Machine Learning (AutoML) has significantly transformed the machine learning landscape by automating the creation and optimization of ML models. This has opened up a path towards democratized ML, making it accessible to a […]

Review of the Year 2023 – AutoML Hannover

Posted on December 20, 2023 by Marius Lindauer

by the AutoML Hannover Team The year 2023 was the most successful for us as a (still relatively young) AutoML group in Hannover. With the start of several big projects, including the ERC starting grant on interactive and explainable AutoML and a BMUV-funded project on Green AutoML, the group has grown and we were able […]

Wrapping Up AutoML-Conf 2022 and Introducing the 2023 Edition

Posted on December 21, 2022 by Frank Hutter, Marius Lindauer

The inaugural AutoML conference 2022 was an exciting adventure for us! With 170 attendees in the very first iteration, we assess this conference as a big success and are confirmed in our belief that it was the right time to transition from a workshop series to a full-fledged conference. In this blogpost, we will summarize […]

Review of the Year 2022 (Hannover)

Posted on December 20, 2022 by Marius Lindauer

by the AutoML Hannover Team The year 2022 was an exciting year for us. So much happened: At the Leibniz University Hannover (LUH), we founded our new institute of Artificial Intelligence AI, in short LUH|AI; Marius got tenure and was promoted to full professor; The group is further growing with our new team members Alexander […]

Rethinking AutoML: Advancing from a Machine-Centered to Human-Centered Paradigm

Posted on November 30, 2022 by Marius Lindauer, Alexander Tornede

In this blog post, we argue why the development of the first generation of AutoML tools ended up being less fruitful than expected and how we envision a new paradigm of automated machine learning (AutoML) that is focused on the needs and workflows of ML practitioners and data scientists. The Vision of AutoML The last […]

Announcing the Automated Machine Learning Conference 2022

Posted on December 3, 2021 by Frank Hutter, Marius Lindauer

Modern machine learning systems come with many design decisions (including hyperparameters, architectures of neural networks and the entire data processing pipeline), and the idea of automating these decisions gave rise to the research field of automated machine learning (AutoML). AutoML has been booming over the last decade, with hundreds of papers published each year now […]

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

Posted on November 27, 2020 by Lucas Zimmer, Marius Lindauer, Frank Hutter

By Auto-PyTorch is a framework for automated deep learning (AutoDL) that uses BOHB as a backend to optimize the full deep learning pipeline, including data preprocessing, network training techniques and regularization methods. Auto-PyTorch is the successor of AutoNet which was one of the first frameworks to perform this joint optimization.

Best Practices for Scientific Research on Neural Architecture Search

Posted on September 6, 2019 by Marius Lindauer, Frank Hutter

By Neural architecture search (NAS) is currently one of the hottest topics in automated machine learning (see AutoML book), with a seemingly exponential increase in the number of papers written on the subject, see the figure above. While many NAS methods are fascinating (please see our survey article for an overview of the main trends […]