A Block Coordinate Descent Proximal Method for Simultaneous Filtering and Parameter Estimation

@InProceedings{pmlr-v97-raziperchikolaei19a,
  title = 	 {A Block Coordinate Descent Proximal Method for Simultaneous Filtering and Parameter Estimation},
  author =       {Raziperchikolaei, Ramin and Bhat, Harish},
  booktitle = 	 {Proceedings of the 36th International Conference on Machine Learning},
  pages = 	 {5380--5388},
  year = 	 {2019},
  editor = 	 {Chaudhuri, Kamalika and Salakhutdinov, Ruslan},
  volume = 	 {97},
  series = 	 {Proceedings of Machine Learning Research},
  month = 	 {09--15 Jun},
  publisher =    {PMLR},
  pdf = 	 {http://proceedings.mlr.press/v97/raziperchikolaei19a/raziperchikolaei19a.pdf},
  url = 	 {https://proceedings.mlr.press/v97/raziperchikolaei19a.html},
  abstract = 	 {We propose and analyze a block coordinate descent proximal algorithm (BCD-prox) for simultaneous filtering and parameter estimation of ODE models. As we show on ODE systems with up to d=40 dimensions, as compared to state-of-the-art methods, BCD-prox exhibits increased robustness (to noise, parameter initialization, and hyperparameters), decreased training times, and improved accuracy of both filtered states and estimated parameters. We show how BCD-prox can be used with multistep numerical discretizations, and we establish convergence of BCD-prox under hypotheses that include real systems of interest.}
}