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Found 8 papers, 2 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 $O(n^{-1/2})$.
Contrastive Learning for Regression on Hyperspectral Data
Contrastive learning has demonstrated great effectiveness in representation learning especially for image classification tasks.
Stochastic gradient descent with gradient estimator for categorical features
Categorical data are present in key areas such as health or supply chain, and this data require specific treatment.
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.
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).
Singleshot : a scalable Tucker tensor decomposition
This paper introduces a new approach for the scalable Tucker decomposition problem.
Distance Measure Machines
This paper presents a distance-based discriminative framework for learning with probability distributions.
