25,525 research outputs found
Bellman Error Based Feature Generation using Random Projections on Sparse Spaces
We address the problem of automatic generation of features for value function
approximation. Bellman Error Basis Functions (BEBFs) have been shown to improve
the error of policy evaluation with function approximation, with a convergence
rate similar to that of value iteration. We propose a simple, fast and robust
algorithm based on random projections to generate BEBFs for sparse feature
spaces. We provide a finite sample analysis of the proposed method, and prove
that projections logarithmic in the dimension of the original space are enough
to guarantee contraction in the error. Empirical results demonstrate the
strength of this method
Variable selection in high-dimensional additive models based on norms of projections
We consider the problem of variable selection in high-dimensional sparse
additive models. We focus on the case that the components belong to
nonparametric classes of functions. The proposed method is motivated by
geometric considerations in Hilbert spaces and consists of comparing the norms
of the projections of the data onto various additive subspaces. Under minimal
geometric assumptions, we prove concentration inequalities which lead to new
conditions under which consistent variable selection is possible. As an
application, we establish conditions under which a single component can be
estimated with the rate of convergence corresponding to the situation in which
the other components are known.Comment: 27 page
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