Frank Hutter

Frank Hutter

 Emmy Noether Research Group Lead (eq. Assistant Prof.)

Postal address:
Institut für Informatik
Albert-Ludwigs-Universität Freiburg
Sekretariat Nebel/GKI
Georges-Köhler-Allee 052
79110 Freiburg, Germany
Building 074, Room 00-018
48.014472, 7.831111 (DD)
+49 761 203-67740

I'm head of the Research Group on Machine Learning for Automated Algorithm Design. I'm lucky enough to have an amazing team; all team members are linked in the sidebar on the left.

Recent News
Auto-Net won its first competition dataset: the first dataset of the current ChaLearn AutoML challenge (it achieved 90% AUC score, compared to the 80% of the best human expert team).
More generally, in this challenge, our automatic machine learning systems outperformed over 150 teams of human experts in several phases. In the most recent ``expert phase'', we won 1st place in both tracks: competing against human experts and automated systems. More details on the faculty's website.
We won the top 3 places in the ICON Challenge on Algorithm Selection: Together with our close collaborators at UBC, we came in first with SATzilla, second with AutoFolio (first place on PAR10 score and number of solved instances), and third with a combination of the two.
We won five awards at the International Planning Competition (Planning and Learning Track), including the best learner award for Fast Downward Cedalion and the best basic solver award for Fast Downward SMAC.

General research interests

I am interested in all facets of intelligence, and how we can replicate it in artificial systems. In particular, I work on
  • Statistical machine learning (in particular deep learning), to learn effective representations for large amounts of very noisy data (including uncertainty quantification)
  • Automated problem solving, including knowledge representation
  • Autonomously-learning software systems, which can improve their performance over time without the need for a human in the loop
  • Sequential decision making under uncertainty, to trade off building better models of the world vs. acting better based on them
  • Scientific experimentation, to make empirical research more reproducible and to codify human experts' strategies to the point where an autonomous system can execute them.
Most of my research is of a methodological nature on the meta-level, such as machine learning methods that model & optimize the performance of other machine learning methods. These meta-level problems constitute a great `drosophila' for AI: they require representation learning, reasoning under uncertainty, bounded rationality, active learning, exploration/exploitation tradeoffs, transfer learning, and much more, but they also allow for a perfectly reproducible environment with complex dynamics and available ground truth (given enough computational power). Also, they are great from a practical perspective since their outputs are better algorithms for problems we care about.

Specific research areas

I currently focus on a few research areas that combine the general themes above:
  • Bayesian optimization -- sequential experimental design under uncertainty
  • Automated machine learning -- developing an AI that can compete with human data scientists
  • Deep learning -- automatically learning representations of the data; in particular, I work on automating structure & hyperparameter search for deep learning, and on improving optimization algorithms for deep networks
  • Automated algorithm design -- developing automated methods for parameter optimization, algorithm selection, and algorithm analysis. A few years ago, I gave a Google tech talk on this topic; you can watch it on youtube.


I'm a member of the Department of Computer Science at the Faculty of Engineering of the University of Freiburg. I'm affiliated with three programmes by the German Research Foundation (DFG): the Emmy Noether Programme, the Priority Programme Autonomous Learning, and the Excellence Cluster BrainLinks-BrainTools. I'm a former member of the Computer Science Department of the University of British Columbia (UBC), specifically of the Laboratory for Computational Intelligence (LCI) and the Bioinformatics and Empirical & Theoretical Algorithmics Laboratory (BETA). I'm a machine learning consultant for Zynga Inc and a co-founder of Meta-Algorithmic Technologies. I earned my PhD at UBC in 2009 and my Diplom (eq. MSc) at Darmstadt University in 2004.

I coorganized/am coorganizing the following workshops and competitions:

I'm currently on the editorial board of JAIR, area chair for NIPS 2016, senior programm committee member at IJCAI 2016, and programme committee member of ICML 2016, ICLR 2016, AAAI 2016, SAT 2016, LION 2016, GECCO 2016, and AI 2016.

For more information, please see my publication page and my academic CV (the CV is updated irregularly).


Erdös number: 3 (Anne Condon -> Michael E. Sacks -> Paul Erdös)