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Lars Kotthoff

Lars Kotthoff

Associate Professor

larsko@uwyo.edu

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.

News

Publications

For citation numbers, please see my Google Scholar page.

2022

  • Bistarelli, Stefano, Lars Kotthoff, Francesco Santini, and Carlo Taticchi. “Summary Report for the Third International Competition on Computational Models of Argumentation.” AI Magazine 42, no. 3 (2022): 70–73. preprint PDF bibTeX abstract

    The Third International Competition on Computational Models of Argumentation (ICCMA’19) focused on reasoning tasks in abstract argumentation frameworks. Submitted solvers were tested on a selected collection of benchmark instances, including artificially generated argumentation frameworks and some frameworks formalizing real-world problems. This competition introduced two main novelties over the two previous editions: the first one is the use of the Docker platform for packaging the participating solvers into virtual “light” containers; the second novelty consists of a new track for dynamic frameworks.
  • 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.
  • Pulatov, Damir, Marie Anastacio, Lars Kotthoff, and Holger Hoos. “Opening the Black Box: Automated Software Analysis for Algorithm Selection.” In First Conference on Automated Machine Learning (Main Track), 2022. preprint PDF bibTeX abstract

    Impressive performance improvements have been achieved in many areas of AI by metaalgorithmic 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 meta-algorithmic 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.
  • 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 First Conference on Automated Machine Learning (Late-Breaking Workshop Track), 2022. preprint PDF bibTeX abstract

    Machine learning system design frequently necessitates balancing multiple objectives, such as prediction error and energy consumption, for deep neural networks (DNNs). Typically, no single design performs well across all objectives; thus, finding Pareto-optimal designs is of interest. Measuring different objectives frequently incurs different costs; for example, measuring the prediction error of DNNs is significantly more expensive than measuring the energy consumption of a pre-trained DNN because it requires re-training the DNN. Current state-of-the-art methods do not account for this difference in objective evaluation cost, potentially wasting costly evaluations of objective functions for little information gain. To address this issue, we propose a novel cost-aware decoupled approach that weights the improvement of the hypervolume of the Pareto region by the measurement cost of each objective. To evaluate our approach, we perform experiments on several machine learning systems deployed on energy constraints environments.
  • Kotthoff, Lars, Sourin Dey, Jake Heil, Vivek Jain, Todd Muller, Alexander Tyrrell, Hud Wahab, and Patrick Johnson. “Optimizing Laser-Induced Graphene Production.” In 11th Conference on Prestigious Applications of Artificial Intelligence, 31–44, 2022. preprint PDF bibTeX abstract

    A lot of technological advances depend on next-generation materials, such as graphene, which enables better electronics, to name but one example. Manufacturing such materials is often difficult, in particular, producing graphene at scale is an open problem. We apply state-of-the-art machine learning to optimize the production of laser-induced graphene, an established manufacturing method that has shown great promise. We demonstrate improvements over previous results in terms of the quality of the produced graphene from a variety of different precursor materials. We use Bayesian model-based optimization to quickly improve outcomes based on little initial data and show the robustness of our approach to different experimental conditions, tackling a small-data problem in contrast to the more common big-data applications of machine learning. We analyze the learned surrogate models with respect to the quality of their predictions and learned relationships that may be of interest to domain experts and improve our understanding of the processes governing laser-induced graphene production.
See all

Software

  • 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.

Teaching

  • 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.

Awards

Other

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.