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Found 25 papers, 11 papers with code
A Swiss Army Knife for Minimax Optimal Transport
The Optimal transport (OT) problem and its associated Wasserstein distance have recently become a topic of great interest in the machine learning community.
Margin-aware Adversarial Domain Adaptation with Optimal Transport
In this paper, we propose a new theoretical analysis of unsupervised domain adaptation that relates notions of large margin separation, adversarial learning and optimal transport.
Unlocking the Potential of Transformers in Time Series Forecasting with Sharpness-Aware Minimization and Channel-Wise Attention
Transformer-based architectures achieved breakthrough performance in natural language processing and computer vision, yet they remain inferior to simpler linear baselines in multivariate long-term forecasting.
Leveraging Gradients for Unsupervised Accuracy Estimation under Distribution Shift
Our key idea is that the model should be adjusted with a higher magnitude of gradients when it does not generalize to the test dataset with a distribution shift.
Leveraging Ensemble Diversity for Robust Self-Training in the Presence of Sample Selection Bias
Self-training is a well-known approach for semi-supervised learning.
Understanding deep neural networks through the lens of their non-linearity
The remarkable success of deep neural networks (DNN) is often attributed to their high expressive power and their ability to approximate functions of arbitrary complexity.
Revisiting invariances and introducing priors in Gromov-Wasserstein distances
Gromov-Wasserstein distance has found many applications in machine learning due to its ability to compare measures across metric spaces and its invariance to isometric transformations.
Learning representations that are closed-form Monge mapping optimal with application to domain adaptation
Optimal transport (OT) is a powerful geometric tool used to compare and align probability measures following the least effort principle.
Meta Optimal Transport
We study the use of amortized optimization to predict optimal transport (OT) maps from the input measures, which we call Meta OT.
Unbalanced CO-Optimal Transport
With this result in hand, we provide empirical evidence of this robustness for the challenging tasks of heterogeneous domain adaptation with and without varying proportions of classes and simultaneous alignment of samples and features across single-cell measurements.
Factored couplings in multi-marginal optimal transport via difference of convex programming
Optimal transport (OT) theory underlies many emerging machine learning (ML) methods nowadays solving a wide range of tasks such as generative modeling, transfer learning and information retrieval.
All of the Fairness for Edge Prediction with Optimal Transport
Machine learning and data mining algorithms have been increasingly used recently to support decision-making systems in many areas of high societal importance such as healthcare, education, or security.
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 the most challenging research directions in machine learning (ML) right now, as it requires from scientists to lay novel statistical learning foundations to explain their behaviour in practice.
Improving Few-Shot Learning through Multi-task Representation Learning Theory
In this paper, we consider the framework of multi-task representation (MTR) learning where the goal is to use source tasks to learn a representation that reduces the sample complexity of solving a target task.
Putting Theory to Work: From Learning Bounds to Meta-Learning Algorithms
To the best of our knowledge, this is the first contribution that puts the most recent learning bounds of meta-learning theory into practice for the popular task of few-shot classification.
Rank-one partitioning: formalization, illustrative examples, and a new cluster enhancing strategy
In this paper, we introduce and formalize a rank-one partitioning learning paradigm that unifies partitioning methods that proceed by summarizing a data set using a single vector that is further used to derive the final clustering partition.
A survey on domain adaptation theory: learning bounds and theoretical guarantees
Despite a large amount of different transfer learning scenarios, the main objective of this survey is to provide an overview of the state-of-the-art theoretical results in a specific, and arguably the most popular, sub-field of transfer learning, called domain adaptation.
CO-Optimal Transport
Optimal transport (OT) is a powerful geometric and probabilistic tool for finding correspondences and measuring similarity between two distributions.
Revisiting (\epsilon, \gamma, \tau)-similarity learning for domain adaptation
Similarity learning is an active research area in machine learning that tackles the problem of finding a similarity function tailored to an observable data sample in order to achieve efficient classification.
Feature Selection for Unsupervised Domain Adaptation using Optimal Transport
In this paper, we propose a new feature selection method for unsupervised domain adaptation based on the emerging optimal transportation theory.
Cross-lingual Document Retrieval using Regularized Wasserstein Distance
Many information retrieval algorithms rely on the notion of a good distance that allows to efficiently compare objects of different nature.
Optimal Transport for Multi-source Domain Adaptation under Target Shift
In this paper, we propose to tackle the problem of reducing discrepancies between multiple domains referred to as multi-source domain adaptation and consider it under the target shift assumption: in all domains we aim to solve a classification problem with the same output classes, but with labels' proportions differing across them.
Co-clustering through Optimal Transport
The proposed method uses the entropy regularized optimal transport between empirical measures defined on data instances and features in order to obtain an estimated joint probability density function represented by the optimal coupling matrix.
Kernel Alignment for Unsupervised Transfer Learning
The ability of a human being to extrapolate previously gained knowledge to other domains inspired a new family of methods in machine learning called transfer learning.
Theoretical Analysis of Domain Adaptation with Optimal Transport
Domain adaptation (DA) is an important and emerging field of machine learning that tackles the problem occurring when the distributions of training (source domain) and test (target domain) data are similar but different.
