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Found 5 papers, 1 papers with code
Bures-Wasserstein Means of Graphs
Finding the mean of sampled data is a fundamental task in machine learning and statistics.
On the complexity of the optimal transport problem with graph-structured cost
Multi-marginal optimal transport (MOT) is a generalization of optimal transport to multiple marginals.
Incremental inference of collective graphical models
We consider incremental inference problems from aggregate data for collective dynamics.
Multi-marginal optimal transport and probabilistic graphical models
We study multi-marginal optimal transport problems from a probabilistic graphical model perspective.
Inference with Aggregate Data: An Optimal Transport Approach
Consequently, the celebrated Sinkhorn/iterative scaling algorithm for multi-marginal optimal transport can be leveraged together with the standard belief propagation algorithm to establish an efficient inference scheme which we call Sinkhorn belief propagation (SBP).
