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Found 12 papers, 5 papers with code
Gaussian-Smoothed Sliced Probability Divergences
We particularly focus on the Gaussian smoothed sliced Wasserstein distance and prove that it converges with a rate $O(n^{-1/2})$.
Adversarial Semi-Supervised Domain Adaptation for Semantic Segmentation: A New Role for Labeled Target Samples
Adversarial learning baselines for domain adaptation (DA) approaches in the context of semantic segmentation are under explored in semi-supervised framework.
Statistical and Topological Properties of Gaussian Smoothed Sliced Probability Divergences
In this paper, we analyze the theoretical properties of this distance as well as those of generalized versions denoted as Gaussian smoothed sliced divergences.
Heterogeneous Wasserstein Discrepancy for Incomparable Distributions
We provide a theoretical analysis of this new divergence, called $\textit{heterogeneous Wasserstein discrepancy (HWD)}$, and we show that it preserves several interesting properties including rotation-invariance.
Open Set Domain Adaptation using Optimal Transport
We present a 2-step optimal transport approach that performs a mapping from a source distribution to a target distribution.
Optimal Transport for Conditional Domain Matching and Label Shift
We address the problem of unsupervised domain adaptation under the setting of generalized target shift (joint class-conditional and label shifts).
Theoretical Guarantees for Bridging Metric Measure Embedding and Optimal Transport
Unlike Gromov-Wasserstein (GW) distance which compares pairwise distances of elements from each distribution, we consider a method allowing to embed the metric measure spaces in a common Euclidean space and compute an optimal transport (OT) on the embedded distributions.
Partial Optimal Transport with Applications on Positive-Unlabeled Learning
In this paper, we address the partial Wasserstein and Gromov-Wasserstein problems and propose exact algorithms to solve them.
Screening Sinkhorn Algorithm for Regularized Optimal Transport
We introduce in this paper a novel strategy for efficiently approximating the Sinkhorn distance between two discrete measures.
Binacox: automatic cut-point detection in high-dimensional Cox model with applications in genetics
We introduce the binacox, a prognostic method to deal with the problem of detecting multiple cut-points per features in a multivariate setting where a large number of continuous features are available.
Collective Matrix Completion
Usually in matrix completion a single matrix is considered, which can be, for example, a rating matrix in recommendation system.
Binarsity: a penalization for one-hot encoded features in linear supervised learning
In each group of binary features coming from the one-hot encoding of a single raw continuous feature, this penalization uses total-variation regularization together with an extra linear constraint.
