DSS
Decision Sciences & Systems
Technical University of Munich
 

Druck 2 sw

Nils Kohring

Department of Informatics (I18)
Technical University of Munich
 
E-Mail: nils.kohringzzin.tum.de
Office:
Boltzmannstr. 3
85748 München, Germany
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 via pseudogradient dynamics. 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, M. Fichtl, and M. Bichler. Equilibrium learning in combinatorial auctions: Computing approximate bayesian nash equilibria via pseudogradient dynamics. In Proceedings of the 20th International Conference on Autonomous Agents and Multiagent Systems (AAMAS 2021), 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 via pseudogradient dynamics. 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

 

 

 

Decision Sciences & Systems (DSS), Department of Informatics (I18), Technische Universität München, Boltzmannstr. 3, 85748 Garching, Germany
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