Spectral Learning from a Single Trajectory under Finite-State Policies

@InProceedings{pmlr-v70-balle17a,
  title = 	 {Spectral Learning from a Single Trajectory under Finite-State Policies},
  author =       {Borja Balle and Odalric-Ambrym Maillard},
  booktitle = 	 {Proceedings of the 34th International Conference on Machine Learning},
  pages = 	 {361--370},
  year = 	 {2017},
  editor = 	 {Precup, Doina and Teh, Yee Whye},
  volume = 	 {70},
  series = 	 {Proceedings of Machine Learning Research},
  month = 	 {06--11 Aug},
  publisher =    {PMLR},
  pdf = 	 {http://proceedings.mlr.press/v70/balle17a/balle17a.pdf},
  url = 	 {https://proceedings.mlr.press/v70/balle17a.html},
  abstract = 	 {We present spectral methods of moments for learning sequential models from a single trajectory, in stark contrast with the classical literature that assumes the availability of multiple i.i.d. trajectories. Our approach leverages an efficient SVD-based learning algorithm for weighted automata and provides the first rigorous analysis for learning many important models using dependent data. We state and analyze the algorithm under three increasingly difficult scenarios: probabilistic automata, stochastic weighted automata, and reactive predictive state representations controlled by a finite-state policy. Our proofs include novel tools for studying mixing properties of stochastic weighted automata.}
}