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Found 10 papers, 4 papers with code
Generalized Sobolev Transport for Probability Measures on a Graph
In connection with the OW, we show that one only needs to simply solve a univariate optimization problem to compute the GST, unlike the complex two-level optimization problem in OW.
Optimal Transport for Measures with Noisy Tree Metric
It is known that such OT problem (i. e., tree-Wasserstein (TW)) admits a closed-form expression, but depends fundamentally on the underlying tree structure over supports of input measures.
Scalable Unbalanced Sobolev Transport for Measures on a Graph
We show that the proposed unbalanced Sobolev transport (UST) admits a closed-form formula for fast computation, and it is also negative definite.
Dynamic Flows on Curved Space Generated by Labeled Data
The scarcity of labeled data is a long-standing challenge for many machine learning tasks.
Sobolev Transport: A Scalable Metric for Probability Measures with Graph Metrics
In this work, we consider probability measures supported on a graph metric space and propose a novel Sobolev transport metric.
Adversarial Regression with Doubly Non-negative Weighting Matrices
In this paper, we propose a novel and coherent scheme for kernel-reweighted regression by reparametrizing the sample weights using a doubly non-negative matrix.
Gradient flows on the feature-Gaussian manifold
The scarcity of labeled data is a long-standing challenge for cross-domain machine learning tasks.
MOST: Multi-Source Domain Adaptation via Optimal Transport for Student-Teacher Learning
To this end, we propose in this paper a novel model for multi-source DA using the theory of optimal transport and imitation learning.
Imitation Learning
Multi-Source Unsupervised Domain Adaptation
+1
Entropy Partial Transport with Tree Metrics: Theory and Practice
In this work, we consider an \textit{entropy partial transport} (EPT) problem for nonnegative measures on a tree having different masses.
On the Convergence of Gradient Descent in GANs: MMD GAN As a Gradient Flow
We then derive an explicit condition which ensures that gradient descent on the parameter space of the generator in gradient regularized $\mathrm{MMD}$ GAN is globally convergent to the target distribution.
