Learning stable and predictive structures in kinetic systems.

@article{Pfister2019LearningSA,
  title={Learning stable and predictive structures in kinetic systems.},
  author={Niklas Pfister and Stefan Bauer and Jonas Peters},
  journal={Proceedings of the National Academy of Sciences of the United States of America},
  year={2019}
}
Learning kinetic systems from data is one of the core challenges in many fields. Identifying stable models is essential for the generalization capabilities of data-driven inference. We introduce a computationally efficient framework, called CausalKinetiX, that identifies structure from discrete time, noisy observations, generated from heterogeneous experiments. The algorithm assumes the existence of an underlying, invariant kinetic model, a key criterion for reproducible research. Results on… CONTINUE READING