Part of Advances in Neural Information Processing Systems 31 (NeurIPS 2018)
Elad Hazan, Holden Lee, Karan Singh, Cyril Zhang, Yi Zhang
We give a polynomial-time algorithm for learning latent-state linear dynamical systems without system identification, and without assumptions on the spectral radius of the system's transition matrix. The algorithm extends the recently introduced technique of spectral filtering, previously applied only to systems with a symmetric transition matrix, using a novel convex relaxation to allow for the efficient identification of phases.