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Found 5 papers, 0 papers with code
Optimal Transport with Cyclic Symmetry
On the basis of this reduction, our algorithms solve the small optimization problem instead of the original OT.
Aggregated Multi-output Gaussian Processes with Knowledge Transfer Across Domains
Since the supports may have various granularities depending on attributes (e. g., poverty rate and crime rate), modeling such data is not straightforward.
Non-approximate Inference for Collective Graphical Models on Path Graphs via Discrete Difference of Convex Algorithm
First we show that the MAP inference problem can be formulated as a (non-linear) minimum cost flow problem.
Probabilistic Optimal Transport based on Collective Graphical Models
In this study, we propose a new framework in which OT is considered as a maximum a posteriori (MAP) solution of a probabilistic generative model.
Spatially Aggregated Gaussian Processes with Multivariate Areal Outputs
By deriving the posterior GP, we can predict the data value at any location point by considering the spatial correlations and the dependences between areal data sets, simultaneously.
