M.Sc. Lara Kuhlmann

@article{Tkáč2024,
title = {MachineLearnAthon: An Action-Oriented Machine Learning Didactic Concept},
author = {Tkáč, M. and Sieber, J. and Kuhlmann, L. and Brüggenolte, M. and Rinciog, A. and Henke, M. and Schweidtmann, A. M. and Gao, Q. and Theisen, M. F. and El Shawi, R.},
editor = {arXiv},
url = {https://arxiv.org/pdf/2401.16291.pdf},
doi = {arXiv:2401.16291},
year = {2024},
date = {2024-01-29},
urldate = {2024-01-29},
journal = {Arxiv},
abstract = {Machine Learning (ML) techniques are encountered nowadays across disciplines, from social sciences, through natural sciences to engineering. The broad
application of ML and the accelerated pace of its evolution lead to an increasing
need for dedicated teaching concepts aimed at making the application of this
technology more reliable and responsible. However, teaching ML is a daunting
task. Aside from the methodological complexity of ML algorithms, both with
respect to theory and implementation, the interdisciplinary and empirical nature
of the field need to be taken into consideration. This paper introduces the MachineLearnAthon format, an innovative didactic concept designed to be inclusive for students of different disciplines with heterogeneous levels of mathematics, programming and domain expertise. At the heart of the concept lie ML challenges, which make use of industrial data sets to solve real-world problems. These cover the entire ML pipeline, promoting data literacy and practical skills, from data preparation, through deployment, to evaluation.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}

@article{kuhlmann2023dynamic,
title = {A Dynamic Systems Model for an Economic Evaluation of Sales Forecasting Methods},
author = {Kuhlmann, L. and Pauly, M. },
editor = {Sveučilište Sjever},
url = {https://hrcak.srce.hr/file/441852},
doi = {10.31803/tg-20230511175500},
year = {2023},
date = {2023-09-15},
journal = {Tehnički glasnik},
volume = {17},
number = {3},
pages = {397-404},
abstract = {Sales forecasts are essential for a smooth workflow and cost optimization. Usually, they are assessed using statistical error measures, which might be misleading in a business context. This paper proposes a new dynamic systems model for an economic evaluation of sales forecasts. The model describes the development of the inventory level over time and derives the resulting overstock and shortage costs. It is tested on roughly 3,000 real-world time series and compared with the commonly used approach based on statistical measures. The experiments show that different statistical measures have no coherent evaluation, making their usage even less suitable for a practical economic application.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}

@conference{10.1007/978-3-031-39831-5,
title = {RODD: Robust Outlier Detection in Data Cubes},
author = {Kuhlmann, L. and Wilmes, D. and Müller, E. and Pauly, M. and Horn, D.},
url = {https://arxiv.org/pdf/2303.08193.pdf},
doi = {10.1007/978-3-031-39831-5},
isbn = {978-3-031-39831-5},
year = {2023},
date = {2023-08-14},
booktitle = {Big Data Analytics and Knowledge Discovery},
series = {Lecture Notes in Computer Science},
abstract = {Data cubes are multidimensional databases, often built from several separate databases, that serve as flexible basis for data analysis. Surprisingly, outlier detection on data cubes has not yet been treated extensively. In this work, we provide the first framework to evaluate robust outlier detection methods in data cubes (RODD). We introduce a novel random forest-based outlier detection approach (RODD-RF) and compare it with more traditional methods based on robust location estimators. We propose a general type of test data and examine all methods in a simulation study. Moreover, we apply ROOD-RF to real world data. The results show that RODD-RF can lead to improved outlier detection.
},
keywords = {},
pubstate = {published},
tppubtype = {conference}
}