Poster
PAC-Bayesian Error Bound, via Rényi Divergence, for a Class of Linear Time-Invariant State-Space Models
Deividas Eringis · john leth · Zheng-Hua Tan · Rafal Wisniewski · Mihaly Petreczky
Hall C 4-9 #908
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Abstract
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Thu 25 Jul 4:30 a.m. PDT
— 6 a.m. PDT
Abstract:
In this paper we derive a PAC-Bayesian error bound for a class of stochastic dynamical systems with inputs, namely, for linear time-invariant stochastic state-space models (stochastic LTI systems for short). This class of systems is widely used in control engineering and econometrics, in particular, they represent a special case of recurrent neural networks. In this paper we 1) formalize the learning problem for stochastic LTI systems with inputs, 2) derive a PAC-Bayesian error bound for such systems, and 3) discuss various consequences of this error bound.
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