Philipp Geiger's web page

Publications

Selection

1. Geiger, P., & Straehle, C.-N. (2021). Learning game-theoretic models of multiagent trajectories using implicit layers. Proceedings of the Thirty-Fourth AAAI Conference on Artificial Intelligence (AAAI). [DL: publication.]
2. Geiger, P., Besserve, M., Winkelmann, J., Proissl, C., & Schölkopf, B. (2019). Coordinating users of shared facilities via data-driven predictive assistants and game theory. UAI. [DL: publication, slides.]
3. 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 32th International Conference on Machine Learning (ICML 2015). [DL: publication, slides.]

All peer-reviewed publications

1. Geiger, P., & Straehle, C.-N. (2021). Learning game-theoretic models of multiagent trajectories using implicit layers. Proceedings of the Thirty-Fourth AAAI Conference on Artificial Intelligence (AAAI). [DL: publication.]
2. 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.]
3. 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.]
4. 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.]
5. 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.]
6. 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.]
7. 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., & Straehle, C.-N. (2020). Multiagent trajectory models via game theory and implicit layer-based learning. ArXiv Preprint ArXiv:2008.07303. [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.]

Blog

Have a look at my blog!

Software

Further programs are available on GitHub.

• CLH: CLH stands for "Causal inference algorithm for Linear processes with Hidden confounders". This is a package containing a Matlab implementation of Algorithm 1 of our paper "Causal Inference by Identification of Vector Autoregressive Processes with Hidden Components", called CLH_NV. The package also contains an example how to run the algorithm on simulated data. Please keep in mind that this is a very preliminary implementation for test purposes. [Download: CLH.zip.]

Various slides and notes

Coming soon.