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Michael Kölle, M.Sc. Lehrstuhl für Mobile und Verteilte Systeme Ludwig-Maximilians-Universität München, Institut für Informatik Oettingenstraße 67 Raum E109 Telefon: +49 89 / 2180-9160 Fax: +49 89 / 2180-9148 |
🔬 Research Interests
- Quantum Artificial Intelligence
- Reinforcement Learning
- Multi-Agent Systems
🎓 Teaching (Assistance)
- Rechnerarchitektur: SS23, SS22 (Lehrpreis beste Bachelor Vorlesung), SS21
- Betriebssysteme: WS23/24, WS22/23, WS21/22, WS20/21
- Quantum Applications: SS23, SS22
- Intelligent Systems: WS23/24, WS22/23
- Quantum Computing Programming: WS23/24, SS23, WS22/23, SS22, WS21/22, SS21
We are always looking for new tutors: Tutor:in für den Lehrbetrieb (m/w/d)
💡 Open thesis ideas
- Evaluating Weight Shaping for Quantum Layers in Variational Quantum Circuits
- Quantum Aware Optimizers – (e.g. Quantum-aware Adam)
- Multi Agent Quantum Reinforcement Learning using Quantum Boltzmann Machines
- Benchmarking State-of-the-Art Quantum Computing Frameworks (Simulators) for Quantum Machine Learning Applications
- Optimizing/Building Quantum Circuits with Reinforcement Learning
- Quantum Planning – Monte Carlo Rollouts with Quantum Walks
- Emergent Boids in a Predator-Prey Setting using Reinforcement Learning
- Quantum Soups – Self Replicating Quantum Neural Networks
- Emergent Cooperation through Quantum Networks
- Evaluating Quantum Actor-Critic Methods for Reinforcement Learning
- Evaluating Metaheuristic Optimization Algorithms for Quantum Reinforcement Learning
- Audio Preparation and Classification using Variational Quantum Circuits.
If you are interested in one of the advertised topics above or have your own ideas, send us: Anfrage Abschlussarbeiten
📖 Theses in progress
- Quantum-Enhanced Denoising Diffusion Model – Gerhard Stenzel
- Dimensionality Reduction with Autoencoders for Efficient Classification with Variational Quantum Circuits – Jonas Maurer
- Efficient unsupervised quantum anomaly detection using one-class support vector machines – Afrae Ahouzi
- A Reinforcement-Learning Environment for purposeful Quantum Circuit Design and Quantum State Preparation – Tom Schubert
- Parameter reduction with quantum circuits – The potential of Quantum Proximal Policy Optimization – Timo Witter
- Anomalous Sound Detection with Multimodal Embeddings – Lara Lanz
- Exploring Multi-Agent Reinforcement Learning Strategies in a Predator-Prey Setting – Yannick Erpelding
✅ Completed theses
- Multi-Agent Exploration through Peer Incentivization – Johannes Tochtermann
- Analyzing Reinforcement Learning strategies from a parameterized quantum walker – Lorena Wemmer
- Quantum Multi-Agent Reinforcement Learning using Evolutionary Optimization – Felix Topp
- Efficient quantum circuit architecture for parameterized coined quantum walks on many bipartite graphs – Viktoryia Patapovich
- Scalable Discrete Communication in Decentralized MARL using Clustering – Valentin Kerle
- Generalizing Agents in the Starcraft Multi-Agent Challenge – Balthasar Schüss
- Quantum Enhanced Policy Gradient Methods for Reinforcement Learning – Mohamad Hgog
- Embedding Classical Data for efficient Quantum Machine Learning – David Münzer
- Efficient Data Embedding for offline Handwriting Recognition using Quantum Support Vector Machines – Leopold Bodendörfer
- Efficient embedding in Quantum Support Vector Machines using a specialized NISQ approach – Jonathan Wulf
- A comparison of Generative Adversarial Networks and Variational Autoencoders for Density Estimation – Gerhard Stenzel
- Anomaly Detection on Medical Images using Classification of Clustering Results – Sebastian Haugg
- A Risk-Sensitive Approach for modeling the Hedging Portfolio Problem with Reinforcement Learning – Quentin Mathieu
- Exploring the impact of markets on the credit assignment problem in a multi-agent environment – Zarah Zahreddin
- Learning to Participate through Trading of Reward Shares – Tim Matheis
📚 Publications
2023
- T. Phan, F. Ritz, P. Altmann, M. Zorn, J. NNüßlein, M. Kölle, T. Gabor, and C. Linnhoff-Popien, „Attention-Based Recurrence for Multi-Agent Reinforcement Learning under Stochastic Partial Observability,“ in Proceedings of the 40th International Conference on Machine Learning (ICML), 2023.
[BibTeX] [Download PDF]@inproceedings{phanICML23, author = {Thomy Phan and Fabian Ritz and Philipp Altmann and Maximilian Zorn and Jonas NN{\"u}{\ss}lein and Michael K{\"o}lle and Thomas Gabor and Claudia Linnhoff-Popien}, title = {Attention-Based Recurrence for Multi-Agent Reinforcement Learning under Stochastic Partial Observability}, year = {2023}, publisher = {PMLR}, booktitle = {Proceedings of the 40th International Conference on Machine Learning (ICML)}, location = {Hawaii, USA}, url = {https://thomyphan.github.io/publication/2023-07-01-icml-phan}, eprint = {https://thomyphan.github.io/files/2023-icml-preprint.pdf}, }
- J. Stein, F. Chamanian, M. Zorn, J. Nüßlein, S. Zielinski, M. Kölle, and C. Linnhoff-Popien, „Evidence that PUBO outperforms QUBO when solving continuous optimization problems with the QAOA,“ arXiv preprint arXiv:2305.03390, 2023.
[BibTeX]@article{stein2023evidence, title={Evidence that PUBO outperforms QUBO when solving continuous optimization problems with the QAOA}, author={Stein, Jonas and Chamanian, Farbod and Zorn, Maximilian and N{\"u}{\ss}lein, Jonas and Zielinski, Sebastian and K{\"o}lle, Michael and Linnhoff-Popien, Claudia}, journal={arXiv preprint arXiv:2305.03390}, year={2023}, }
- M. Kölle, S. Illium, M. Zorn, J. Nüßlein, P. Suchostawski, and C. Linnhoff-Popien, „Improving Primate Sounds Classification using Binary Presorting for Deep Learning.“ 2023.
[BibTeX]@inproceedings {koelle23primate, title = {Improving Primate Sounds Classification using Binary Presorting for Deep Learning}, author = {K{\"o}lle, Michael and Illium, Steffen and Zorn, Maximilian and N{\"u}{\ss}lein, Jonas and Suchostawski, Patrick and Linnhoff-Popien, Claudia}, year = {2023}, organization = {Int. Conference on Deep Learning Theory and Application - DeLTA 2023}, publisher = {Springer CCIS Series}, }
2022
- S. Illium, G. Griffin, M. Kölle, M. Zorn, J. Nüßlein, and C. Linnhoff-Popien, VoronoiPatches: Evaluating A New Data Augmentation MethodarXiv, 2022. doi:10.48550/ARXIV.2212.10054
[BibTeX] [Download PDF]@misc{https://doi.org/10.48550/arxiv.2212.10054, doi = {10.48550/ARXIV.2212.10054}, url = {https://arxiv.org/abs/2212.10054}, author = {Illium, Steffen and Griffin, Gretchen and Kölle, Michael and Zorn, Maximilian and Nüßlein, Jonas and Linnhoff-Popien, Claudia}, keywords = {Computer Vision and Pattern Recognition (cs.CV), Machine Learning (cs.LG), FOS: Computer and information sciences, FOS: Computer and information sciences}, title = {VoronoiPatches: Evaluating A New Data Augmentation Method}, publisher = {arXiv}, year = {2022}, copyright = {Creative Commons Attribution 4.0 International} }
- S. Illium, M. Zorn, C. Lenta, M. Kölle, C. Linnhoff-Popien, and T. Gabor, Constructing Organism Networks from Collaborative Self-ReplicatorsarXiv, 2022. doi:10.48550/ARXIV.2212.10078
[BibTeX] [Download PDF]@misc{https://doi.org/10.48550/arxiv.2212.10078, doi = {10.48550/ARXIV.2212.10078}, url = {https://arxiv.org/abs/2212.10078}, author = {Illium, Steffen and Zorn, Maximilian and Lenta, Cristian and Kölle, Michael and Linnhoff-Popien, Claudia and Gabor, Thomas}, keywords = {Neural and Evolutionary Computing (cs.NE), Machine Learning (cs.LG), FOS: Computer and information sciences, FOS: Computer and information sciences}, title = {Constructing Organism Networks from Collaborative Self-Replicators}, publisher = {arXiv}, year = {2022}, copyright = {Creative Commons Attribution 4.0 International} }
- K. Schmid, M. Kölle, and T. Matheis, „Learning to Participate through Trading of Reward Shares,“ , 2022.
[BibTeX]@article{schmidlearning, title = {Learning to Participate through Trading of Reward Shares}, author = {Schmid, Kyrill and Kölle, Michael and Matheis, Tim}, year = {2022} }
- M. Kölle, L. Rietdorf, and K. Schmid, Decentralized scheduling through an adaptive, trading-based multi-agent systemarXiv, 2022. doi:10.48550/ARXIV.2207.11172
[BibTeX] [Download PDF]@misc{https://doi.org/10.48550/arxiv.2207.11172, doi = {10.48550/ARXIV.2207.11172}, url = {https://arxiv.org/abs/2207.11172}, author = {Kölle, Michael and Rietdorf, Lennart and Schmid, Kyrill}, keywords = {Artificial Intelligence (cs.AI), Machine Learning (cs.LG), FOS: Computer and information sciences, FOS: Computer and information sciences}, title = {Decentralized scheduling through an adaptive, trading-based multi-agent system}, publisher = {arXiv}, year = {2022}, copyright = {arXiv.org perpetual, non-exclusive license} }
- A. Sedlmeier, M. Kölle, R. Müller, L. Baudrexel, and C. Linnhoff-Popien, „Quantifying Multimodality in World Models,“ in Proceedings of the 14th International Conference on Agents and Artificial Intelligence – Volume 1: ICAART,, 2022, pp. 367-374. doi:10.5220/0010898500003116
[BibTeX]@conference{multimod_icaart22, author = {Andreas Sedlmeier and Michael Kölle and Robert Müller and Leo Baudrexel and Claudia Linnhoff-Popien}, title = {Quantifying Multimodality in World Models}, booktitle = {Proceedings of the 14th International Conference on Agents and Artificial Intelligence - Volume 1: ICAART,}, year = {2022}, pages = {367-374}, publisher = {SciTePress}, organization = {INSTICC}, doi = {10.5220/0010898500003116}, isbn = {978-989-758-547-0} }