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Found 18 papers, 3 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.
Fast Multiscale Diffusion on Graphs
Diffusing a graph signal at multiple scales requires computing the action of the exponential of several multiples of the Laplacian matrix.
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.
Metric Learning from Imbalanced Data
A key element of any machine learning algorithm is the use of a function that measures the dis/similarity between data points.
An Adjusted Nearest Neighbor Algorithm Maximizing the F-Measure from Imbalanced Data
In this paper, we address the challenging problem of learning from imbalanced data using a Nearest-Neighbor (NN) algorithm.
Learning Landmark-Based Ensembles with Random Fourier Features and Gradient Boosting
Unlike state-of-the-art Multiple Kernel Learning techniques that make use of a pre-computed dictionary of kernel functions to select from, at each iteration we fit a kernel by approximating it as a weighted sum of Random Fourier Features (RFF) and by optimizing their barycenter.
Deep multi-Wasserstein unsupervised domain adaptation
In unsupervised domain adaptation (DA), 1 aims at learning from labeled source data and fully unlabeled target examples a model with a low error on the target domain.
L$^3$-SVMs: Landmarks-based Linear Local Support Vectors Machines
For their ability to capture non-linearities in the data and to scale to large training sets, local Support Vector Machines (SVMs) have received a special attention during the past decade.
beta-risk: a New Surrogate Risk for Learning from Weakly Labeled Data
During the past few years, the machine learning community has paid attention to developping new methods for learning from weakly labeled data.
Similarity Learning for Time Series Classification
In this paper, we propose a novel method for learning similarities based on DTW, in order to improve time series classification.
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.
Metric Learning as Convex Combinations of Local Models With Generalization Guarantees
Over the past ten years, metric learning allowed the improvement of the numerous machine learning approaches that manipulate distances or similarities.
Lipschitz Continuity of Mahalanobis Distances and Bilinear Forms
Many theoretical results in the machine learning domain stand only for functions that are Lipschitz continuous.
Landmarks-Based Kernelized Subspace Alignment for Unsupervised Domain Adaptation
Domain adaptation (DA) has gained a lot of success in the recent years in computer vision to deal with situations where the learning process has to transfer knowledge from a source to a target domain.
Algorithmic Robustness for Learning via $(ε, γ, τ)$-Good Similarity Functions
The notion of metric plays a key role in machine learning problems such as classification, clustering or ranking.
Subspace Alignment For Domain Adaptation
We present two approaches to determine the only hyper-parameter in our method corresponding to the size of the subspaces.
A Survey on Metric Learning for Feature Vectors and Structured Data
The need for appropriate ways to measure the distance or similarity between data is ubiquitous in machine learning, pattern recognition and data mining, but handcrafting such good metrics for specific problems is generally difficult.
Similarity Learning for Provably Accurate Sparse Linear Classification
In recent years, the crucial importance of metrics in machine learning algorithms has led to an increasing interest for optimizing distance and similarity functions.
