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Lars Kotthoff

Lars Kotthoff

Assistant Professor


EERB 422b
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 and direct the Artificially Intelligent Manufacturing center (AIM) at the University of Wyoming.

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? 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.


  • Pulatov, Damir, Marie Anastacio, Lars Kotthoff, and Holger H. Hoos. “Opening the Black Box: Automated Software Analysis for Algorithm Selection.” In INFORMS Computing Society Conference, 2022. bibTeX abstract

    Impressive performance improvements have been achieved in many areas of AI by meta-algorithmic techniques, such as automated algorithm selection and configuration. However, existing techniques treat the target algorithms they are applied to as black boxes -- nothing is known about their inner workings. This allows metaalgorithmic techniques to be used broadly, but leaves untapped potential performance improvements enabled by information gained from a deeper analysis of the target algorithms. In this paper, we open the black box without sacrificing universal applicability of meta-algorithmic techniques by automatically analyzing algorithms. We show how to use this information to perform algorithm selection, and demonstrate improved performance compared to previous approaches that treat algorithms as black boxes.
  • Kotthoff, Lars, Sourin Dey, Jake Heil, Vivek Jain, Todd Muller, Alexander Tyrrell, Hud Wahab, and Patrick Johnson. “Optimizing Laser-Induced Graphene Production.” In 11th International Conference on Prestigious Applications of Intelligent Systems, 2022. bibTeX

  • Pulatov, Damir, Marie Anastacio, Lars Kotthoff, and Holger Hoos. “Opening the Black Box: Automated Software Analysis for Algorithm Selection.” In 1st International Conference on Automated Machine Learning, 2022. bibTeX

  • Iqbal, Md Shahriar, Jianhai Su, Lars Kotthoff, and Pooyan Jamshidi. “Getting the Best Bang For Your Buck: Choosing What to Evaluate for Faster Bayesian Optimization.” In 1st International Conference on Automated Machine Learning, 2022. bibTeX


  • Kashgarani, Haniye, and Lars Kotthoff. “Is Algorithm Selection Worth It? Comparing Selecting Single Algorithms and Parallel Execution.” In AAAI Workshop on Meta-Learning and MetaDL Challenge, 140:58–64. Proceedings of Machine Learning Research. PMLR, 2021. preprint PDF bibTeX abstract

    For many practical problems, there is more than one algorithm or approach to solve them. Such algorithms often have complementary performance – where one fails, another performs well, and vice versa. Per-instance algorithm selection leverages this by employing portfolios of complementary algorithms to solve sets of difficult problems, choosing the most appropriate algorithm for each problem instance. However, this requires complex models to effect this selection and introduces overhead to compute the data needed for those models. On the other hand, even basic hardware is more than capable of running several algorithms in parallel. We investigate the tradeoff between selecting a single algorithm and running multiple in parallel and incurring a slowdown because of contention for shared resources. Our results indicate that algorithm selection is worth it, especially for large portfolios.
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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) and COSC 4552/5552. Lecture materials, assignments, announcements, etc. are available on WyoCourses.
  • I am teaching a practical machine learning course using mlr. The slides are available here.



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.