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pro vyhledávání: '"Christie, Louis G."'
Autor:
Christie, Louis G., Aston, John A. D.
In this paper we present the framework of symmetry in nonparametric regression. This generalises the framework of covariate sparsity, where the regression function depends only on at most $s < d$ of the covariates, which is a special case of translat
Externí odkaz:
http://arxiv.org/abs/2404.12943
Autor:
Christie, Louis G., Aston, John A. D.
We present a method for estimating the maximal symmetry of a continuous regression function. Knowledge of such a symmetry can be used to significantly improve modelling by removing the modes of variation resulting from the symmetries. Symmetry estima
Externí odkaz:
http://arxiv.org/abs/2303.13616
Autor:
Christie, Louis G., Aston, John A. D.
Invariant and equivariant models incorporate the symmetry of an object to be estimated (here non-parametric regression functions $f : \mathcal{X} \rightarrow \mathbb{R}$). These models perform better (with respect to $L^2$ loss) and are increasingly
Externí odkaz:
http://arxiv.org/abs/2205.15280