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Frederik Schubert, M. Sc.
Leibniz Universität Hannover
Institut für Informationsverarbeitung
Appelstr. 9A
30167 Hannover
Germany
phone: +49 511 762-5044
office location: room 1301

 

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
Publications
Show selected publications only
  • Frederik Schubert, Carolin Benjamins, Sebastian Döhler, Bodo Rosenhahn, Marius Lindauer
    POLTER: Policy Trajectory Ensemble Regularization for Unsupervised Reinforcement Learning
    Arxiv Preprint, May 2022
    (arXiv) BibTeX
  • Carolin Benjamins, Theresa Eimer, Frederik Schubert, Aditya Mohan, André Biedenkapp, Bodo Rosenhahn, Frank Hutter, Marius Lindauer
    Contextualize Me - The Case for Context in Reinforcement Learning
    ArXiv Preprint, February 2022
    (arXiv) 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
    (link) BibTeX
  • Christoph Reinders, Frederik Schubert, Bodo Rosenhahn
    ChimeraMix: Image Classification on Small Datasets via Masked Feature Mixing
    31st International Joint Conference on Artificial Intelligence and the 25th European Conference on Artificial Intelligence (IJCAI-ECAI), 2022
    (arXiv, Code) 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) BibTeX
  • Maren Awiszus, Frederik Schubert, Bodo Rosenhahn
    World-GAN: a Generative Model for Minecraft Worlds
    IEEE Conference on Games, August 2021
    (arxiv) 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) BibTeX
  • Frederik Schubert, Theresa Eimer, Bodo Rosenhahn, Marius Lindauer
    Automatic Risk Adaptation in Distributional Reinforcement Learning
    Arxiv Preprint, June 2021
    (arXiv) 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
    (link) 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, 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
Last changed: 2022-10-03