Research web page of Philipp Geiger

Overview

Welcome! On this web page, I give an overview over my research and background. I'm a research scientist at Bosch Center for Artificial Intelligence, with a background in mathematics, computer science and philosophy.

Research scope: I conduct research broadly in the areas of machine learning and multiagent systems, often working on theoretical guarantees/verification and interpretability, of algorithms and models. Currently I am focusing on safe and robust imitation learning and realistic simulations.

Selected publications:

  1. Fail-Safe Adversarial Generative Imitation Learning. (2022). TMLR.
  2. Learning game-theoretic models of multiagent trajectories using implicit layers. (2021). AAAI.
  3. Coordinating users of shared facilities via data-driven predictive assistants and game theory. (2019). UAI. [Slides.]

Transfer: I also work on the multi-stakeholder problem of co-evolving innovative research with applications that are relevant for society. In particular, I consider applications in the areas of safe automated driving and resource-efficient sharing economy.

Further links: my profiles on Google Scholar, GitHub.

News

Publications

All peer-reviewed publications

  1. Geiger, P., & Straehle, C.-N. (2022). Fail-Safe Adversarial Generative Imitation Learning. Transactions on Machine Learning Research. [DL: publication.]
  2. Geiger, P., & Straehle, C.-N. (2021). Learning game-theoretic models of multiagent trajectories using implicit layers. Proceedings of the Thirty-Fifth AAAI Conference on Artificial Intelligence (AAAI). [DL: publication.]
  3. Etesami, J., & Geiger, P. (2020). Causal Transfer for Imitation Learning and Decision Making under Sensor­‐shift. Proceedings of the Thirty-Third AAAI Conference on Artificial Intelligence (AAAI). [DL: publication.]
  4. Geiger, P., Besserve, M., Winkelmann, J., Proissl, C., & Schölkopf, B. (2019). Coordinating users of shared facilities based on data-driven assistants and game-theoretic analysis. Proceedings of the 35th Conference on Uncertainty in Artificial Intelligence (UAI). [DL: publication, slides.]
  5. Geiger, P., Hofmann, K., & Schölkopf, B. (2016). Experimental and causal view on information integration in autonomous agents. Proceedings of the 6th International Workshop on Combinations of Intelligent Methods and Applications (CIMA), 21–28. [DL: publication, slides.]
  6. Geiger, P., Zhang, K., Gong, M., Janzing, D., & Schölkopf, B. (2015). Causal inference by identification of vector autoregressive processes with hidden components. Proceedings of the 32nd International Conference on Machine Learning (ICML). [DL: publication, slides.]
  7. Gong, M., Zhang, K., Schoelkopf, B., Tao, D., & Geiger, P. (2015). Discovering temporal causal relations from subsampled data. Proceedings of the 32nd International Conference on Machine Learning (ICML). [DL: publication.]
  8. Geiger, P., Janzing, D., & Schölkopf, B. (2014). Estimating causal effects by bounding confounding. Proceedings of the 30th Conference on Uncertainty in Artificial Intelligence (UAI). [DL: publication, supplement, slides.]

Preprints

  1. Geiger, P., Carata, L., & Schoelkopf, B. (2016). Causal inference for data-driven debugging and decision making in cloud computing. ArXiv Preprint ArXiv:1603.01581. [DL: publication.]

Theses

  1. Geiger, P. (2017). Causal models for decision making via integrative inference [PhD thesis]. [DL: publication, slides.]
  2. Geiger, P. (2012). Mutual information and Gödel incompleteness [Diploma thesis]. [DL: publication.]

Notes

  1. Geiger, P. (2016). Notes on socio-economic transparency mechanisms. ArXiv Preprint ArXiv:1606.04703. [DL: publication.]

Bio

Short bio:

Contact

Email address: (insert first name).w.(insert last name)@de.bosch.com

See also my MPI web site (it's still valid, even though I moved away from the MPI).

About this web page: I used these tools to build this web page as well as my CV: