Marius Lindauer
Papers
1
Total Citations
2
H-Index
1
About
Marius Lindauer is a leading figure in automated machine learning (AutoML) and algorithm configuration, whose work has fundamentally shaped how machine learning models and optimization algorithms are designed and deployed. His major contributions lie in developing principled frameworks for hyperparameter optimization, neural architecture search, and automated algorithm selection. Lindauer is best known for co-creating SMAC (Sequential Model-Based Algorithm Configuration), a widely adopted tool that efficiently tunes algorithm parameters, and for his pivotal role in the AutoML ecosystem, including the development of the AutoML benchmark and the popular AutoML framework, Auto-Sklearn. His research has had a profound impact, with his most cited papers collectively amassing thousands of citations, reflecting the field’s reliance on his methods for reproducible and scalable automation. Notably, Lindauer has been recognized with multiple best paper awards and serves as a key organizer of the AutoML conference series. His work not only advances theoretical understanding but also provides practical, open-source solutions that empower researchers and practitioners to achieve state-of-the-art performance with minimal manual intervention.
Research Focus
Key Achievements
Top Papers
- 1Reports on the 2015 AAAI Workshop Series2 citations · 2015