Speaker
Jocelyn Chi
(University of Minnesota Twin Cities)
Description
The canonical polyadic (CP) tensor decomposition represents a multidimensional data array as a sum of rank-one outer products of latent factors. Building on CP-HiFi, the hybrid infinite- and finite-dimensional CP framework of Larsen et al. (2024), which introduces quasitensors by modeling selected modes as smooth functional factors in a reproducing kernel Hilbert space, we replace the standard least-squares objective with a quantile-based loss. This modification enables estimation of conditional quantile functions (rather than conditional means) along functional modes. The resulting approach yields a distribution-aware tensor factorization that is robust to outliers and captures heterogeneity beyond the mean structure.
Author
Jocelyn Chi
(University of Minnesota Twin Cities)
Co-author
Dr
Eric Chi
(University of Minnesota Twin Cities)