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Lars Kotthoff

Lars Kotthoff

Assistant Professor


EN 4071A
Department of Computer Science
University of Wyoming
Dept 3315, 1000 E University Ave
Laramie, WY 82071-2000

My research combines artificial intelligence and machine learning to build robust systems with state-of-the-art performance. I develop techniques to induce models of how algorithms for solving computationally difficult problems behave in practice. Such models allow to select the best algorithm and choose the best parameter configuration for solving a given problem. I lead the Meta-Algorithmics, Learning and Large-scale Empirical Testing (MALLET) lab.

More broadly, I am interested in innovative ways of modelling and solving challenging problems and applying such approaches to the real world. Part of this is making cutting edge research available to and usable by non-experts. Machine learning often plays a crucial role in this, and I am also working on making machine learning more accessible and easier to use.

Interested in coming to beautiful Wyoming and joining MALLET? There are several funded PhD positions available. Please drop me an email or, if you are already here, come by my office. I also have Master's projects and projects for undergraduates seeking research experience available.



For citation numbers, please see my Google Scholar page.

  • Kotthoff, Lars, Chris Thornton, Holger H. Hoos, Frank Hutter, and Kevin Leyton-Brown. “Auto-WEKA 2.0: Automatic Model Selection and Hyperparameter Optimization in WEKA.” Journal of Machine Learning Research 18, no. 25 (2017): 1–5. preprint PDF bibTeX abstract

    WEKA is a widely used, open-source machine learning platform. Due to its intuitive interface, it is particularly popular with novice users. However, such users often find it hard to identify the best approach for their particular dataset among the many available. We describe the new version of Auto-WEKA, a system designed to help such users by automatically searching through the joint space of WEKA's learning algorithms and their respective hyperparameter settings to maximize performance, using a state-of-the-art Bayesian optimization method. Our new package is tightly integrated with WEKA, making it just as accessible to end users as any other learning algorithm.
  • Hong, Neil P. Chue, Tom Crick, Ian P. Gent, Lars Kotthoff, and Kenji Takeda. “Top Tips to Make Your Research Irreproducible.” CoRR abs/1504.00062 (2015). preprint PDF bibTeX abstract

    It is an unfortunate convention of science that research should pretend to be reproducible; our top tips will help you mitigate this fussy conventionality, enabling you to enthusiastically showcase your irreproducible work.
  • Kotthoff, Lars, Barry Hurley, and Barry O’Sullivan. “The ICON Challenge on Algorithm Selection.” AI Magazine 38, no. 2 (2017). preprint PDF bibTeX

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  • Maintainer of the FSelector R package.

  • Author and maintainer of LLAMA, an R package to simplify common algorithm selection tasks such as training a classifier as portfolio selector.

  • Core contributor to the mlr R package (Github) for all things machine learning in R.

  • Leading the Auto-WEKA project, which brings automated machine learning to WEKA.


I am teaching COSC 3020 (Algorithms and Data Structures) in the fall semester 2017. Lecture materials, assignments, announcements, etc. are available on WyoCourses.



Apart from my main affiliation, I am a research associate with the Maya Research Program. If I'm not in the office, it's possible that you can find me in the jungle of Belize excavating and/or mapping Maya ruins. Check out the interactive map.

I am also involved with the OpenML project project and a core contributor to ASlib, the benchmark library for algorithm selection.

While you're here, have a look at my overview of the Algorithm Selection literature. For something more visual, have a look at my pictures on Flickr.