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Spotlight
Online Learning of Linear Dynamical Systems
Elad Hazan · Karan Singh · Cyril Zhang
We present an efficient and practical algorithm for the online prediction of discrete-time linear dynamical systems. Despite the non-convex optimization problem, using improper learning and convex relaxation our algorithm comes with provable guarantees: it has near-optimal regret bounds compared to the best LDS in hindsight, while overparameterizing by only a small logarithmic factor. Our analysis brings together ideas from improper learning by convex relaxations, online regret minimization, and the spectral theory of Hankel matrices.
Author Information
Elad Hazan (Princeton University)
Karan Singh (Princeton University)
Cyril Zhang (Princeton University)
Related Events (a corresponding poster, oral, or spotlight)
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2017 Poster: Learning Linear Dynamical Systems via Spectral Filtering »
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