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Frederik Schubert, M. Sc.
Frederik Schubert left the Institut für Informationsverarbeitung.
Publications and research activities from the time after the departure are not listed here.

 

Frederik Schubert studied computer engineering at Leibniz University in Hannover. He completed his master's degree in July 2019 with a thesis entitled "Multi-Task Learning by Neural Transfer Reinforcement Learning".

He has been working on his doctorate at TNT since September 2019. His research focuses on Reinforcement Learning and Procedural Content Generation, but he has also done work on object detection, small data classification and the analysis of hyperspectral images.

Projects
Semantic Scene Analysis
Semantic Scene Analysis
AI Competition
AI Competition
Reinforcement Learning
Reinforcement Learning
Publications
Show selected publications only
  • Frederik Schubert, Yannik Mahlau, Konrad Bethmann, Fabian Hartmann, Reinhard Caspary, Marco Munderloh, Jörn Ostermann, Bodo Rosenhahn
    Quantized Inverse Design for Photonic Integrated Circuits
    ACS Omega, January 2025
    (arXiv.org) BibTeX
  • Yannik Mahlau, Frederik Schubert, Konrad Bethmann, Reinhard Caspary, Antonio Calà Lesina, Marco Munderloh, Jörn Ostermann, Bodo Rosenhahn
    A flexible framework for large-scale FDTD simulations: open-source inverse design for 3D nanostructures
    Preprint, January 2025
    (arXiv.org) BibTeX
  • Christoph Reinders, Frederik Schubert, Bodo Rosenhahn
    HydraMix: Multi-Image Feature Mixing for Small Data Image Classification
    Preprint, January 2025
    (arXiv.org) BibTeX
  • Konrad Bethmann, Yannik Mahlau, Frederik Schubert, Marco Munderloh, Bodo Rosenhahn, Bernhard Roth, Jörn Ostermann
    Inverse design of robust out-of-plane coupling elements
    proceedings Photonics West, January 2025
    BibTeX
  • Maximilian Schier, Frederik Schubert, Bodo Rosenhahn
    Explainable Reinforcement Learning via Dynamic Mixture Policies
    2025 IEEE International Conference on Robotics and Automation (ICRA), IEEE, p. To be published, 2025
    (pdfGitHub) BibTeX
  • Yannik Mahlau, Frederik Schubert, Bodo Rosenhahn
    Mastering Zero-Shot Interactions in Cooperative and Competitive Simultaneous Games
    Proceedings of the 41st International Conference on Machine Learning (ICML), July 2024
    (arXiv.org) BibTeX
  • Carolin Benjamins, Theresa Eimer, Frederik Schubert, Aditya Mohan, Sebastian Döhler, André Biedenkapp, Bodo Rosenhahn, Frank Hutter, Marius Lindauer
    Contextualize Me - The Case for Context in Reinforcement Learning
    Transactions on Machine Learning Research, June 2023
    (OpenReview, PDF, arXiv.org) BibTeX
  • Frederik Schubert, Carolin Benjamins, Sebastian Döhler, Bodo Rosenhahn, Marius Lindauer
    POLTER: Policy Trajectory Ensemble Regularization for Unsupervised Reinforcement Learning
    Transactions on Machine Learning Research, April 2023
    (OpenReview, PDF) BibTeX
  • Maren Awiszus, Frederik Schubert, Bodo Rosenhahn
    Wor(l)d-GAN: Towards Natural Language Based PCG in Minecraft
    IEEE Transactions on Games, IEEE, February 2022, edited by Georgios Yannakakis
    (IEEEexplore) BibTeX
  • Christoph Reinders, Frederik Schubert, Bodo Rosenhahn
    ChimeraMix: Image Classification on Small Datasets via Masked Feature Mixing
    Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence (IJCAI), 2022
    (arXiv.org, GitHub) BibTeX
  • Carolin Benjamins, Theresa Eimer, Frederik Schubert, André Biedenkapp, Bodo Rosenhahn, Frank Hutter, Marius Lindauer
    CARL: A Benchmark for Contextual and Adaptive Reinforcement Learning
    NeurIPS 2021 Workshop on Ecological Theory of Reinforcement Learning, December 2021
    (arXiv.org) BibTeX
  • Maren Awiszus, Frederik Schubert, Bodo Rosenhahn
    World-GAN: a Generative Model for Minecraft Worlds
    IEEE Conference on Games, August 2021
    (arXiv.org) BibTeX
  • Frederik Schubert, Theresa Eimer, Bodo Rosenhahn, Marius Lindauer
    Towards Automatic Risk Adaption in Distributional Reinforcement Learning
    Reinforcement Learning for Real Life (RL4RealLife) Workshop in the 38th International Conference on Machine Learning (ICML), July 2021
    (arXiv.org) BibTeX
  • Frederik Schubert, Theresa Eimer, Bodo Rosenhahn, Marius Lindauer
    Automatic Risk Adaptation in Distributional Reinforcement Learning
    Arxiv Preprint, June 2021
    (arXiv.org) BibTeX
  • Frederik Schubert, Maren Awiszus, Bodo Rosenhahn
    TOAD-GAN: a Flexible Framework for Few-Shot Level Generation in Token-Based Games
    IEEE Transactions on Games, IEEE, March 2021, edited by Julian Togelius
    (IEEEexplore) BibTeX
  • Maren Awiszus, Frederik Schubert, Bodo Rosenhahn
    TOAD-GAN: Coherent Style Level Generation from a Single Example
    AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment Best Student Paper Award , October 2020
    (AAAI, GitHub, arXiv.org, Award, Talk (YouTube)) BibTeX
Other activities

Supervised Theses

  • Unsupervised Domain-Specific Pre-Training for Object Detection
  • Option Discovery in Hierarchical Reinforcement Learning
  • Defektprädiktion von Leiterplatten mit Verfahren der künstlichen Intelligenz
  • Wireframe Reconstruction from Single Images
  • Hyperbolic Embeddings in Distributional Reinforcement Learning
  • Implementation and Analysis of Sequential Model-Based Optimisation Methods for the Calibration of Numerical Groundwater Flow Models
  • Few-Shot Anomaly Detection Using Neural Cellular Automata
  • Feature-basierte Kombination von mehreren Bildern zur Klassifikation mit wenigen Trainingsbeispielen
  • Guided Diffusion Models with Few Training Examples
  • AlphaZero for Simultaneous Perfect-Information Games
Last changed: 2025-07-06