Search Results for author: Philipp Geiger

Found 7 papers, 1 papers with code

Fail-Safe Generative Adversarial Imitation Learning

no code implementations3 Mar 2022 Philipp Geiger, Christoph-Nikolas Straehle

For flexible yet safe imitation learning (IL), we propose a modular approach that uses a generative imitator policy with a safety layer, has an overall explicit density/gradient, can therefore be end-to-end trained using generative adversarial IL (GAIL), and comes with theoretical worst-case safety/robustness guarantees.

Imitation Learning

Learning Game-Theoretic Models of Multiagent Trajectories Using Implicit Layers

2 code implementations17 Aug 2020 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 intermediate representations, and transfers to downstream decision making.

Decision Making Interpretable Machine Learning

Coordinating users of shared facilities via data-driven predictive assistants and game theory

no code implementations16 Mar 2018 Philipp Geiger, Michel Besserve, Justus Winkelmann, Claudius Proissl, Bernhard Schölkopf

We study data-driven assistants that provide congestion forecasts to users of shared facilities (roads, cafeterias, etc.

Time Series

Experimental and causal view on information integration in autonomous agents

no code implementations14 Jun 2016 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 things may further increase it.

Decision Making Self-Driving Cars +1

Causal Inference by Identification of Vector Autoregressive Processes with Hidden Components

no code implementations14 Nov 2014 Philipp Geiger, Kun Zhang, Mingming Gong, Dominik Janzing, Bernhard Schölkopf

A widely applied approach to causal inference from a non-experimental time series $X$, often referred to as "(linear) Granger causal analysis", is to regress present on past and interpret the regression matrix $\hat{B}$ causally.

Causal Inference Time Series

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