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Found 8 papers, 2 papers with code
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.
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.
Causal inference for data-driven debugging and decision making in cloud computing
Cloud computing involves complex technical and economical systems and interactions.
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 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.
Causal Transfer for Imitation Learning and Decision Making under Sensor-shift
Learning from demonstrations (LfD) is an efficient paradigm to train AI agents.
Fail-Safe Adversarial Generative Imitation Learning
For flexible yet safe imitation learning (IL), we propose theory and a modular method, with a safety layer that enables a closed-form probability density/gradient of the safe generative continuous policy, end-to-end generative adversarial training, and worst-case safety guarantees.
On Learning the Tail Quantiles of Driving Behavior Distributions via Quantile Regression and Flows
Towards safe autonomous driving (AD), we consider the problem of learning models that accurately capture the diversity and tail quantiles of human driver behavior probability distributions, in interaction with an AD vehicle.
