research
∙
07/19/2023
Revisiting invariances and introducing priors in Gromov-Wasserstein distances
Gromov-Wasserstein distance has found many applications in machine learn...
share
research
∙
05/12/2023
Beyond invariant representation learning: linearly alignable latent spaces for efficient closed-form domain adaptation
Optimal transport (OT) is a powerful geometric tool used to compare and ...
research
∙
06/10/2022
Meta Optimal Transport
We study the use of amortized optimization to predict optimal transport ...
research
∙
05/30/2022
Unbalanced CO-Optimal Transport
Optimal transport (OT) compares probability distributions by computing a...
research
∙
10/01/2021
Factored couplings in multi-marginal optimal transport via difference of convex programming
Optimal transport (OT) theory underlies many emerging machine learning (...
research
∙
10/30/2020
All of the Fairness for Edge Prediction with Optimal Transport
Machine learning and data mining algorithms have been increasingly used ...
research
∙
10/21/2020
Deep Neural Networks Are Congestion Games: From Loss Landscape to Wardrop Equilibrium and Beyond
The theoretical analysis of deep neural networks (DNN) is arguably among...
research
∙
10/05/2020
Putting Theory to Work: From Learning Bounds to Meta-Learning Algorithms
Most of existing deep learning models rely on excessive amounts of label...
research
∙
09/01/2020
Rank-one partitioning: formalization, illustrative examples, and a new cluster enhancing strategy
In this paper, we introduce and formalize a rank-one partitioning learni...
research
∙
04/24/2020
A survey on domain adaptation theory
All famous machine learning algorithms that correspond to both supervise...
research
∙
06/28/2018
Feature Selection for Unsupervised Domain Adaptation using Optimal Transport
In this paper, we propose a new feature selection method for unsupervise...
research
∙
05/11/2018
Cross-lingual Document Retrieval using Regularized Wasserstein Distance
Many information retrieval algorithms rely on the notion of a good dista...
research
∙
03/13/2018
Optimal Transport for Multi-source Domain Adaptation under Target Shift
In this paper, we propose to tackle the problem of reducing discrepancie...
research
∙
05/17/2017
Co-clustering through Optimal Transport
In this paper, we present a novel method for co-clustering, an unsupervi...
research
∙
10/20/2016
Kernel Alignment for Unsupervised Transfer Learning
The ability of a human being to extrapolate previously gained knowledge ...
research
∙
10/14/2016
