Kavli Affiliate: Jia Liu
| First 5 Authors: Yuyang Zhang, Yuyang Zhang, , ,
| Summary:
This paper addresses the problem of learning linear dynamical systems from
noisy observations. In this setting, existing algorithms either yield biased
parameter estimates or have large sample complexities. We resolve these issues
by adapting the instrumental variable method and the bias compensation method,
originally proposed for error-in-variables models, to our setting. We provide
refined non-asymptotic analysis for both methods. 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.
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