Frank Hutter

Home * People * Frank Hutter

Frank Hutter

Frank Hutter,
a German computer scientist, mathematician and professor at University of Freiburg. His research covers the fields of machine learning, deep learning, automated problem solving, stochastic optimization, stochastic gradient descent and Bayesian optimization. He defended his M.Sc. degree on stochastic local search at Darmstadt University of Technology in 2004 under Thomas Stützle and Holger H. Hoos, and his Ph.D. on Automating the Configuration of Algorithms for Solving Hard Computational Problems at University of British Columbia in 2009 under Holger H. Hoos, Kevin Leyton-Brown and Kevin P. Murphy.

Selected Publications

2002 ...

• Frank Hutter, Dave A. D. Tompkins, Holger H. Hoos (2002). Scaling and Probabilistic Smoothing: Efficient Dynamic Local Search for SAT. CP 2002, pdf
• Frank Hutter (2004). Stochastic Local Search for Solving the Most Probable Explanation Problem in Bayesian Networks. Diploma thesis (M.Sc.), Darmstadt University of Technology, supervisors Thomas Stützle and Holger H. Hoos, pdf

2005 ...

• Frank Hutter, Holger H. Hoos, Thomas Stützle (2005). Efficient Stochastic Local Search for MPE Solving. IJCAI 2005, pdf
• Frank Hutter, Youssef Hamadi, Holger H. Hoos, Kevin Leyton-Brown (2006). Performance Prediction and Automated Tuning of Randomized and Parametric Algorithms. CP 2006, pdf
• Frank Hutter, Holger H. Hoos, Thomas Stützle (2007). Automatic Algorithm Configuration Based on Local Search. AAAI 2007, pdf
• Frank Hutter (2009). Automating the Configuration of Algorithms for Solving Hard Computational Problems. Ph.D. thesis, University of British Columbia, supervisors Holger H. Hoos, Kevin Leyton-Brown and Kevin P. Murphy, pdf

2010 ...

• Frank Hutter, Holger H. Hoos, Kevin Leyton-Brown, Kevin P. Murphy (2010). Time-Bounded Sequential Parameter Optimization. LION 2010, pdf
• Frank Hutter, Holger H. Hoos, Kevin Leyton-Brown (2010). Tradeoffs in the empirical evaluation of competing algorithm designs. Annals of Mathematics and Artificial Intelligence, Vol. 60, Nos. 1-2, pdf
• Frank Hutter, Holger H. Hoos, Kevin Leyton-Brown (2011). Sequential Model-Based Optimization for General Algorithm Configuration. LION 2011, pdf
• Frank Hutter, Lin Xu, Holger H. Hoos, Kevin Leyton-Brown (2012). Algorithm Runtime Prediction: Methods & Evaluation. arXiv:1211.0906
• Frank Hutter, Holger H. Hoos, Kevin Leyton-Brown (2013). Bayesian Optimization With Censored Response Data. arXiv:1310.1947
• Kevin Leyton-Brown, Holger H. Hoos, Frank Hutter, Lin Xu (2014). Understanding the empirical hardness of NP-complete problems. Communications of the ACM, Vol. 57, No. 5, pdf

2015 ...

• Ilya Loshchilov, Frank Hutter (2015). Online Batch Selection for Faster Training of Neural Networks. arXiv:1511.06343
• Ilya Loshchilov, Frank Hutter (2016). SGDR: Stochastic Gradient Descent with Warm Restarts. arXiv:1608.03983
• Thomas Elsken, Jan Hendrik Metzen, Frank Hutter (2017). Simple And Efficient Architecture Search for Convolutional Neural Networks. arXiv:1711.04528
• Ilya Loshchilov, Frank Hutter (2017). Decoupled Weight Decay Regularization. arXiv:1711.05101
• Thomas Elsken, Jan Hendrik Metzen, Frank Hutter (2018). Neural Architecture Search: A Survey. arXiv:1808.05377
• Marius Lindauer, Frank Hutter (2019). Best Practices for Scientific Research on Neural Architecture Search. arXiv:1909.02453
• Lior Fuks, Noor Awad, Frank Hutter, Marius Lindauer (2019). An Evolution Strategy with Progressive Episode Lengths for Playing Games. IJCAI 2019, pdf

External Links

• Frank Hutter
• Frank Hutter - Learning Optimization Automated Algorithm Design
• Frank Hutter - The Mathematics Genealogy Project

References

Up one Level