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Found 7 papers, 1 papers with code
Fail-Safe Generative Adversarial Imitation Learning
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.
Learning Game-Theoretic Models of Multiagent Trajectories Using Implicit Layers
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.
Causal Transfer for Imitation Learning and Decision Making under Sensor-shift
Learning from demonstrations (LfD) is an efficient paradigm to train AI agents.
Coordinating users of shared facilities via data-driven predictive assistants and game theory
We study data-driven assistants that provide congestion forecasts to users of shared facilities (roads, cafeterias, etc.
Experimental and causal view on information integration in autonomous agents
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.
Causal inference for data-driven debugging and decision making in cloud computing
Cloud computing involves complex technical and economical systems and interactions.
Causal Inference by Identification of Vector Autoregressive Processes with Hidden Components
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.
