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. Before, I did my doctorate in computer science at Max Planck Institute for Intelligent Systems, and a diplom in mathematics at Heidelberg University with philosophy as a minor subject.

Research scope: I conduct research broadly in the areas of machine learning and multiagent systems, often including theoretical guarantees, validation, statistics, dynamical systems and interpretability of models. Currently I am focusing on imitation learning and data-driven realistic simulations. Previously, I also worked in the areas of causal inference and mathematical logic.

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

• On Learning the Tail Quantiles of Driving Behavior Distributions via Quantile Regression and Flows:
  

  Towards better testing the safety of autonomous vehicles through realistic simulations, we need behavior models that accurately estimate the (long) tails of human driver action distributions, that surround autonomous vehicles. In our preprint (under review), we investigate two approaches that focus on tail quantile estimation, including a version of normalizing flows, for this problem.

  [Posted on 2023-06-08.]

• Fail-Safe Adversarial Generative Imitation Learning paper accepted at TMLR:
  

  Using the flexible scalability of deep generative imitation learning, adding guaranteed safety via worst-case sample-based reachability analysis, while keeping end-to-end trainability – check out our paper accepted at TMLR.

  [Posted on 2022-08-05. Updated on 2022-10-10.]

• Learning game-theoretic models of multiagent trajectories using implicit layers at AAAI 2021:
  

  Neural learning combined with game-theoretic reasoning – check out our AAAI 2021 paper: “Learning game-theoretic models of multiagent trajectories using implicit layers”.

  [Posted on 2020-08-17. Updated on 2021-01-01.]

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. Tee, J. Y., De Candido, O., Utschick, W., & Geiger, P. (2023). On Learning the Tail Quantiles of Driving Behavior Distributions via Quantile Regression and Flows. ArXiv Preprint ArXiv:2305.13106. [DL: publication.]
2. 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:

• Currently, I am a research scientist at the Bosch Center for Artificial Intelligence (BCAI), Renningen, Germany.
• From 2017 to 2018, I was a postdoctoral researcher at the Empirical Inference Department of the Max Planck Institute for Intelligent Systems, Tübingen, Germany, working with Bernhard Schölkopf.
  • There I lead the research project "Cafeteria Coordination": machine learning and game theory for coordinating users of shared facilities and more efficient use of resources, based on a data privacy agreement reached with work councils.
• From 2013 to 2017 I pursued my PhD (Dr. rer. nat.) under the supervision of Dominik Janzing, Bernhard Schölkopf and Marc Toussaint at the Max Planck Institute for Intelligent Systems and the University of Stuttgart, Germany.
• In 2015, I did an internship at Microsoft Research Cambridge, United Kingdom, supervised by Katja Hofmann.
• From 2006 till 2012, I studied mathematics and philosophy at Humboldt University of Berlin and at Heidelberg University, Germany.
• In 2005, I finished high school in Karlsruhe, Germany

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:

• Font Awesome,
• Jekyll (static CMS).