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Found 6 papers, 1 papers with code
Dendrogram of mixing measures: Hierarchical clustering and model selection for finite mixture models
The dendrogram's construction is derived from the theory of convergence of the mixing measures, and as a result, we can both consistently select the true number of mixing components and obtain the pointwise optimal convergence rate for parameter estimation from the tree, even when the model parameters are only weakly identifiable.
Strong identifiability and parameter learning in regression with heterogeneous response
We provide simulation studies and data illustrations, which shed some light on the parameter learning behavior found in several popular regression mixture models reported in the literature.
Beyond Black Box Densities: Parameter Learning for the Deviated Components
As we collect additional samples from a data population for which a known density function estimate may have been previously obtained by a black box method, the increased complexity of the data set may result in the true density being deviated from the known estimate by a mixture distribution.
On Label Shift in Domain Adaptation via Wasserstein Distance
We study the label shift problem between the source and target domains in general domain adaptation (DA) settings.
Entropic Gromov-Wasserstein between Gaussian Distributions
When the metric is the inner product, which we refer to as inner product Gromov-Wasserstein (IGW), we demonstrate that the optimal transportation plans of entropic IGW and its unbalanced variant are (unbalanced) Gaussian distributions.
Functional optimal transport: map estimation and domain adaptation for functional data
We introduce a formulation of optimal transport problem for distributions on function spaces, where the stochastic map between functional domains can be partially represented in terms of an (infinite-dimensional) Hilbert-Schmidt operator mapping a Hilbert space of functions to another.
