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PAC-Bayesian bounds for learning LTI-ss systems with input from empirical loss
In this paper we derive a Probably Approxilmately Correct(PAC)-Bayesian error bound for linear time-invariant (LTI) stochastic dynamical systems with inputs.
Secure PAC Bayesian Regression via Real Shamir Secret Sharing
Hence, we suggest to use real number secret sharing and multiparty computation to share the data and solve the linear regression in a secure way without violating the privacy of data.
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