Nils Kohring, M.Sc.

E-Mail: nils.kohring@in.tum.de
Phone: +49 (0) 89 289 - 17506
Room 01.10.055
Boltzmannstr. 3
85748 Garching, Germany
Hours: by arrangement

Short Bio

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.

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

M. Bichler, M. Fichtl, S. Heidekrüger, N. Kohring, and P. Sutterer. Learning equilibria in symmetric auction games using artificial neural networks. Nature Machine Intelligence, (3), 2021. [ link ]

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.

M. Bichler, M. Fichtl, S. Heidekrüger, N. Kohring, and P. Sutterer. 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. [ pdf ]

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, Online, 11/2020)


Teaching

For available thesis projects, check out our current thesis topics.

Courses
  • Business Analytics, Teaching Assistant (since WS19/20)
  • Seminar on Data Mining, Teaching Assistant (since SoSe20)
Supervised Theses

Shakiba Sheikhian. Strategy Evaluation of the Game RPS: Detecting Exploitabilities via Methods of RL, B.Sc. Information Systems (2021).

Carina Fröhlich. A Survey on Particle Swarm Optimization with an Application to non-differentiable Vector Optimization, M.Sc. Information Systems (2021).

Qiaoxi Liu. Data-driven Marketing Attribution Model Based Attention Mechanism for a Quantitative Estimation of TV-Advertisement Effects, M.Sc. Informatics (2021), in cooperation with ProSiebenSat.1 Media.

Michael Gigler. Predicting the Individual Suitability for E-Mobility Using Machine Learning, M.Sc. Information Systems (2021), in cooperation with BMW.

Wusheng Liu. Learning Approximate Bayes-Nash Equilibria with Opponent-Learning Awareness, M.Sc. Data Engineering and Analytics (2021).

Duc Anh Le. Hyperparameter Optimization of a Deep Reinforcement Learning System for Equilibrium Computation, B.Sc. Informatics (2020).