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Nils Kohring |
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Department of Informatics (I18)
Technical University of Munich
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| E-Mail: | nils.kohring in.tum.de |
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| Office: |
Boltzmannstr. 3
85748 München, Germany
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| Phone: | +49 (0) 89 289 - 17506 | |
| Hours: | by arrangement | |
I'm a Ph.D. student at the DSS chair supervised by Prof. Bichler. My research focuses on the computation of equilibria in markets and auctions via multi-agent reinforcement learning methods.
Short Bio
Education
- 2016 - 2019 Master of Economathematics (M.Sc.), University of Cologne
- 2018 Visiting Student at The University of Tokyo, Japan
- 2013 - 2016 Bachelor of Economathematics (B.Sc.), University of Cologne
Working Experience
- 2019/06 - 2019/08 Data Science Intern at Fintech Startup
- 2018/08 - 2019/02 Intern in the Applied Mathematics Team, Bayer (Leverkusen)
- 2016/09 - 2018/04 Student Tutor for different mathematics lectures, University of Cologne
- 2015/08 - 2015/10 Intern in Process Management, Deutsche Bank (Frankfurt a.M.)
Publications
Bichler, M.; Fichtl, M.; Heidekrüger, S.; Kohring, N.; and Sutterer, P.: Learning to Bid: Computing Bayesian Nash Equilibrium Strategies in Auctions via Neural Pseudo-gradient Ascent, Working Paper, 2020. Presented at the 2020 annual meeting of NBER Market Design Working Group. http://conference.nber.org/conf_papers/f144729.pdf.
S. Heidekrüger, N. Kohring, P. Sutterer, and M. Bichler. Equilibrium learning in combinatorial auctions: Computing approximate bayesian nash equilibria. In AAAI-21 Workshop on Reinforcement Learning in Games (AAAI-RLG 21), Online, Online, 2021.
S. Heidekrüger, N. Kohring, P. Sutterer, and M. Bichler. Multiagent learning for equilibrium computation in auction markets. In AAAI Spring Symposium on Challenges and Opportunities for Multi-Agent Reinforcement Learning (COMARL-21), Online, Online, 2021. S. Heidekrüger, P. Sutterer, N. Kohring, and M. Bichler. Learning bayesian nash equilibria in auction games. In INFORMS Workshop on Data Science, Online, 2020. S. Heidekrüger, P. Sutterer, N. Kohring, and M. Bichler. Equilibrium learning in combinatorial auctions: Computing approximate bayesian nash equilibria. In Workshop on Information Technology and Systems (WITS20), Online, Online, 2020.
Conference Talks
Learning Bayesian Nash Equilibria in Auction Games (Workshop on Data Science at the virtual INFORMS annual meeting, Washington D.C., USA, 11/2020)
Teaching
For available thesis projects, check out https://dss.in.tum.de/teaching/theses-topics.html.
Courses
- W20/21 Business Analytics, Teaching Assistant
- S20 Seminar on Data Mining, Teaching Assistant
- W19/20 Business Analytics, Teaching Assistant