Search Results for author:
Found 19 papers, 12 papers with code
Distributional Reduction: Unifying Dimensionality Reduction and Clustering with Gromov-Wasserstein Projection
Unsupervised learning aims to capture the underlying structure of potentially large and high-dimensional datasets.
Compressive Recovery of Sparse Precision Matrices
We consider the problem of learning a graph modeling the statistical relations of the $d$ variables from a dataset with $n$ samples $X \in \mathbb{R}^{n \times d}$.
Interpolating between Clustering and Dimensionality Reduction with Gromov-Wasserstein
We present a versatile adaptation of existing dimensionality reduction (DR) objectives, enabling the simultaneous reduction of both sample and feature sizes.
Optimal Transport with Adaptive Regularisation
Regularising the primal formulation of optimal transport (OT) with a strictly convex term leads to enhanced numerical complexity and a denser transport plan.
Implicit Differentiation for Hyperparameter Tuning the Weighted Graphical Lasso
We provide a framework and algorithm for tuning the hyperparameters of the Graphical Lasso via a bilevel optimization problem solved with a first-order method.
Entropic Wasserstein Component Analysis
Dimension reduction (DR) methods provide systematic approaches for analyzing high-dimensional data.
Learning Graphical Factor Models with Riemannian Optimization
Graphical models and factor analysis are well-established tools in multivariate statistics.
Template based Graph Neural Network with Optimal Transport Distances
Current Graph Neural Networks (GNN) architectures generally rely on two important components: node features embedding through message passing, and aggregation with a specialized form of pooling.
Ranked #1 on
Graph Classification
on NCI1
Controlling Wasserstein Distances by Kernel Norms with Application to Compressive Statistical Learning
Based on the relations between the MMD and the Wasserstein distances, we provide guarantees for compressive statistical learning by introducing and studying the concept of Wasserstein regularity of the learning task, that is when some task-specific metric between probability distributions can be bounded by a Wasserstein distance.
Semi-relaxed Gromov-Wasserstein divergence with applications on graphs
To this end, the Gromov-Wasserstein (GW) distance, based on Optimal Transport (OT), has proven to be successful in handling the specific nature of the associated objects.
Semi-relaxed Gromov-Wasserstein divergence and applications on graphs
To this end, the Gromov-Wasserstein (GW) distance, based on Optimal Transport (OT), has proven to be successful in handling the specific nature of the associated objects.
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.
Online Graph Dictionary Learning
Dictionary learning is a key tool for representation learning, that explains the data as linear combination of few basic elements.
Ranked #1 on
Graph Classification
on BZR
A contribution to Optimal Transport on incomparable spaces
Optimal Transport is a theory that allows to define geometrical notions of distance between probability distributions and to find correspondences, relationships, between sets of points.
CO-Optimal Transport
Optimal transport (OT) is a powerful geometric and probabilistic tool for finding correspondences and measuring similarity between two distributions.
Time Series Alignment with Global Invariances
Multivariate time series are ubiquitous objects in signal processing.
Sliced Gromov-Wasserstein
Recently used in various machine learning contexts, the Gromov-Wasserstein distance (GW) allows for comparing distributions whose supports do not necessarily lie in the same metric space.
Fused Gromov-Wasserstein distance for structured objects: theoretical foundations and mathematical properties
Optimal transport theory has recently found many applications in machine learning thanks to its capacity for comparing various machine learning objects considered as distributions.
Optimal Transport for structured data with application on graphs
This work considers the problem of computing distances between structured objects such as undirected graphs, seen as probability distributions in a specific metric space.
Ranked #3 on
Graph Classification
on NCI1
