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Lars Kotthoff

Lars Kotthoff

Associate Professor


EERB 422b
Department of Electrical Engineering and Computer Science
School of Computing
University of Wyoming
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.


  • Kotthoff, Lars. “Towards Machine-Generated Algorithms.” In AAAI 2023 Bridge Constraint Programming and Machine Learning, 2023. bibTeX

  • Wahab, Hud, Lars Kotthoff, and Patrick Johnson. “Optimization of Laser-Induced Graphene Manufacturing.” In AAAI 2023 Bridge AI for Materials Science, 2023. bibTeX

  • Shoaib, Mirza, Neelesh Sharma, Lars Kotthoff, Marius Lindauer, and Surya Kant. “AutoML: Advanced Tool for Mining Multivariate Plant Traits.” Trends in Plant Science, 2023. https://doi.org/https://doi.org/10.1016/j.tplants.2023.09.008. preprint PDF bibTeX

  • Iqbal, Md Shahriar, Jianhai Su, Lars Kotthoff, and Pooyan Jamshidi. “FlexiBO: A Decoupled Cost-Aware Multi-Objective Optimization Approach for Deep Neural Networks.” Journal of Artificial Intelligence Research 77 (June 2023): 645–82. preprint PDF bibTeX abstract

    The design of machine learning systems often requires trading off different objectives, for example, prediction error and energy consumption for deep neural networks (DNNs). Typically, no single design performs well in all objectives; therefore, finding Pareto-optimal designs is of interest. The search for Pareto-optimal designs involves evaluating designs in an iterative process, and the measurements are used to evaluate an acquisition function that guides the search process. However, measuring different objectives incurs different costs. For example, the cost of measuring the prediction error of DNNs is orders of magnitude higher than that of measuring the energy consumption of a pre-trained DNN as it requires re-training the DNN. Current state-of-the-art methods do not consider this difference in objective evaluation cost, potentially incurring expensive evaluations of objective functions in the optimization process. In this paper, we develop a novel decoupled and cost-aware multi-objective optimization algorithm, which we call Flexible Multi-Objective Bayesian Optimization (FlexiBO) to address this issue. For evaluating each design, FlexiBO selects the objective with higher relative gain by weighting the improvement of the hypervolume of the Pareto region with the measurement cost of each objective. This strategy, therefore, balances the expense of collecting new information with the knowledge gained through objective evaluations, preventing FlexiBO from performing expensive measurements for little to no gain. We evaluate FlexiBO on seven state-of-the-art DNNs for image recognition, natural language processing (NLP), and speech-to-text translation. Our results indicate that, given the same total experimental budget, FlexiBO discovers designs with 4.8\% to 12.4\% lower hypervolume error than the best method in state-of-the-art multi-objective optimization.
  • Kashgarani, Haniye, and Lars Kotthoff. “Automatic Parallel Portfolio Selection.” In 26th European Conference on Artificial Intelligence, 372:1215–22. Frontiers in Artificial Intelligence and Applications. IOS Press, 2023. preprint PDF bibTeX abstract

    Algorithms to solve hard combinatorial problems often exhibit complementary performance, i.e. where one algorithm fails, another shines. Algorithm portfolios and algorithm selection take advantage of this by running all algorithms in parallel or choosing the best one to run on a problem instance. In this paper, we show that neither of these approaches gives the best possible performance and propose the happy medium of running a subset of all algorithms in parallel. We propose a method to choose this subset automatically for each problem instance, and demonstrate empirical improvements of up to 19\% in terms of runtime, 81\% in terms of misclassification penalty, and 26\% in terms of penalized averaged runtime on scenarios from the ASlib benchmark library. Unlike all other algorithm selection and scheduling approaches in the literature, our performance measures are based on the actual performance for algorithms running in parallel rather than assuming overhead-free parallelization based on sequential performance. Our approach is easy to apply in practice and does not require to solve hard problems to obtain a schedule, unlike other techniques in the literature, while still delivering superior performance.
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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.