43 citations · 54 across the 7 of their papers we have counts for
7 papers
Learning Game-Theoretic Models of Multiagent Trajectories Using Implicit Layers
Philipp Geiger, Christoph-Nikolas Straehle
For prediction of interacting agents' trajectories, we propose an end-to-end trainable architecture that hybridizes neural nets with game-theoretic reasoning, has interpretable int…
Causal Transfer for Imitation Learning and Decision Making under Sensor-shift
Jalal Etesami, Philipp Geiger
Learning from demonstrations (LfD) is an efficient paradigm to train AI agents. But major issues arise when there are differences between (a) the demonstrator's own sensory input,…
Coordinating users of shared facilities via data-driven predictive assistants and game theory
Philipp Geiger, Michel Besserve, Justus Winkelmann +2
We study data-driven assistants that provide congestion forecasts to users of shared facilities (roads, cafeterias, etc.), to support coordination between them, and increase effici…
Notes on socio-economic transparency mechanisms
Philipp Geiger
Clearly, socio-economic freedom requires some extent of transparency regarding the implications of choices. In this paper, we review some established mechanisms for achieving such…
Experimental and causal view on information integration in autonomous agents
Philipp Geiger, Katja Hofmann, Bernhard Schölkopf
The amount of digitally available but heterogeneous information about the world is remarkable, and new technologies such as self-driving cars, smart homes, or the internet of thing…
Causal inference for data-driven debugging and decision making in cloud computing
Philipp Geiger, Lucian Carata, Bernhard Schoelkopf
Cloud computing involves complex technical and economical systems and interactions. This brings about various challenges, two of which are: (1) debugging and control to optimize th…
Causal Inference by Identification of Vector Autoregressive Processes with Hidden Components
Philipp Geiger, Kun Zhang, Mingming Gong +2
A widely applied approach to causal inference from a non-experimental time series , often referred to as "(linear) Granger causal analysis", is to regress present on past and in…