Spectral Learning from a Single Trajectory under Finite-State Policies

as operators which can be expressed as the product of a generalized scalar A-unitary operator with a regular generalized scalar whose spectrum is contained in the

Semidiscrete finite element error estimates for the corresponding optimality system are derived based on estimates for the penalized Stokes problem and the BRR

The proposed learning-based path relinking (LPR) algorithm for solving the bandwidth coloring problem and the bandwidth multicoloring problem incor- porates a learning-based

, θ k mixture indicator variate is drawn from the multinomial distribution with k probabilities equal to the mixture weights.. Then, given the drawn mixture indicator value, k say,

The comparison between the hypothesized and the actual learning process shows that it is very important how students relate to diagrams on the different levels in

The paper is orga- nized as follows: in Section 2, we recall the definition of a PFA and develop the basic Spectral learning algorithm; in Section 3 we define a particular form

A computable bound of the essential spectral radius of finite range Metropolis–Hastings kernels.. Loïc Hervé,

This work presents a precise quantitative, finite sample analysis of the double descent phenomenon in the estimation of linear and non-linear models.. We make use of a