Kavli Affiliate: Jia Liu
| First 5 Authors: Yuyang Zhang, Xinhe Zhang, Jia Liu, Na Li,
| Summary:
In this paper, we focus on learning linear dynamical systems under noisy
observations. In this setting, existing algorithms either yield biased
parameter estimates, or suffer from large sample complexities. To address these
issues, we adapt the instrumental variable method and the bias compensation
method, originally proposed for error-in-variables models, to our setting and
provide refined non-asymptotic analysis. Under mild conditions, our algorithms
achieve superior sample complexities that match the best-known sample
complexity for learning a fully observable system without observation noise.
| Search Query: ArXiv Query: search_query=au:”Jia Liu”&id_list=&start=0&max_results=3