Teaching

Lectures

Paderborn University

• Data Mining, Summer 2019 and 2020
• Online and Adaptive Machine Learning, Winter 2019/20 and 2020/21

LMU Munich

• Supervised Learning, Summer 2022
• Advanced Machine Learning, Summer 2022
• Online Machine Learning and Bandits, Winter 2022/23 and 2023/24
• Optimization for Machine Learning, Summer 2023

Project Groups

Paderborn University

• duelpy, Summer 2020 and Winter 2020/21

LMU Munich

• Arrival time prediction, Summer 2023, joint project with Lufthansa Group

(Co-)Supervised PhD Candidates

• Dr. Jasmin Brandt (2020 – 2025), Multi-armed bandits for trustworthy and resource-efficient algorithm configuration
• Dr. Björn Haddenhorst (2019 – 2022), Testing coherence and identifying winners in dueling bandits: theory and algorithm
• Patrick Irenäus Kolpaczki (2021 – )

(Co-)Supervised Master's Theses

• Jiachun Zhang, Anti-concentration Inequalities and Uniform Confidence Bands, University of Marburg, 2016
• Patrick Irenäus Kolpaczki, Non-stationary Dueling Bandits, Paderborn University, 2021
• Timo Kaufmann, Curiosity-Driven Semi-Supervised Reinforcement Learning, Paderborn University, 2021
• Jianyu Zha, Conformal Prediction for Label Ranking Problem, LMU Munich, 2022
• Shivam Sharma, Contextual Preselection Bandits in Pool-Based Realtime Algorithm Configuration, Paderborn University, 2023
• Sebastian Müller, On Practical Application of Discrete Choice Models for Personalized Pricing, LMU Munich in cooperation with Hoffmann Group, 2023

(Co-)Supervised Bachelor's Theses

• Leon Roschig, Embedding Discrete- into Continuous-time Markov Chains, University of Marburg, 2015
• Dominik Hecker, Das Binomialmodell: Exakte und asymptotische Optionspreisbewertung, University of Marburg, 2016
• Felix Lange, Detecting Concept Changes in Preference Data Streams, Paderborn University, 2021
• Sven Meyer, Implementierung des Sugeno Klassifizierers, Paderborn University, 2021
• Mohness Waizy, LSH-based Similarity Search on Big Rank Data under the Kendall's Tau Distance, Paderborn University, 2021
• Miriam Kranzlmüller, Copula Extensions of Probabilistic Circuits, LMU Munich, 2022
• Simon Nagy, Python Package for Mallows Ranking Model, LMU Munich, 2022
• Martina Georgieva, LSH-based Similarity Search for Ranking Data using Shingling, LMU Munich, 2022
• Daniel Ebensberger, Implementation of the Choquet Classifier, LMU Munich, 2022
• Björn Gillich, A Random Generator for Valued Binary Relations with Generalized Transitivity Properties, LMU Munich, 2022
• Patrick Becker, Shapley Value-based Feature Selection for Online Algorithm Selection, LMU Munich, 2023
• Aurel Eppert, Python Package for the Babington Smith Model, LMU Munich, 2023
• Johannes Möller, Ranking with the Plackett-Luce Model Allowing for Ties: A Python Implementation, LMU Munich, 2023
• Santo Maria Amado Rocco Thies, Single-Peaked Präferenzen für die Modellierung politischer Orientierungen, LMU Munich, 2023
• Manuel Hüelsekamp, On Optimizing the Convex Problem Behind Shapley Values for Feature Importance in Explainable AI, LMU Munich, 2024
• Jiyai Wang, A Comparative Analysis of Rank Aggregation Methods for the Partial Label Ranking Problem, LMU Munich, 2024
• Deben Lin, On Randomized UCB in Monte-Carlo Tree Search, LMU Munich, 2024

Tutorials and Exercise Courses

University of Marburg

• Einführung in die stochastische Analysis, Summer 2015
• Nichtparametrische Statistik, Winter 2015/16
• Mathematik II (Einführung in die Analysis), Summer 2016
• Quantitatives Risikomanagement, Winter 2016/17
• Praktikum zur Stochastik, Winter 2017/18 and Summer 2018

LMU Munich

• Uncertainty in Artificial Intelligence and Machine Learning, Winter 2021/22