Computational optimal transport: With applications to data science
Optimal transport (OT) theory can be informally described using the words of the French
mathematician Gaspard Monge (1746–1818): A worker with a shovel in hand has to move a …
mathematician Gaspard Monge (1746–1818): A worker with a shovel in hand has to move a …
Optimal transport in systems and control
Optimal transport began as the problem of how to efficiently redistribute goods between
production and consumers and evolved into a far-reaching geometric variational framework …
production and consumers and evolved into a far-reaching geometric variational framework …
Prognostics and Health Management (PHM): Where are we and where do we (need to) go in theory and practice
E Zio - Reliability Engineering & System Safety, 2022 - Elsevier
We are performing the digital transition of industry, living the 4th industrial revolution,
building a new World in which the digital, physical and human dimensions are interrelated in …
building a new World in which the digital, physical and human dimensions are interrelated in …
Thermodynamic unification of optimal transport: Thermodynamic uncertainty relation, minimum dissipation, and thermodynamic speed limits
Thermodynamics serves as a universal means for studying physical systems from an energy
perspective. In recent years, with the establishment of the field of stochastic and quantum …
perspective. In recent years, with the establishment of the field of stochastic and quantum …
Wasserstein distributionally robust optimization: Theory and applications in machine learning
Many decision problems in science, engineering, and economics are affected by uncertain
parameters whose distribution is only indirectly observable through samples. The goal of …
parameters whose distribution is only indirectly observable through samples. The goal of …
Inferring spatial and signaling relationships between cells from single cell transcriptomic data
Single-cell RNA sequencing (scRNA-seq) provides details for individual cells; however,
crucial spatial information is often lost. We present SpaOTsc, a method relying on structured …
crucial spatial information is often lost. We present SpaOTsc, a method relying on structured …
Generalized sliced wasserstein distances
The Wasserstein distance and its variations, eg, the sliced-Wasserstein (SW) distance, have
recently drawn attention from the machine learning community. The SW distance …
recently drawn attention from the machine learning community. The SW distance …
Computational optimal transport: Complexity by accelerated gradient descent is better than by Sinkhorn's algorithm
P Dvurechensky, A Gasnikov… - … conference on machine …, 2018 - proceedings.mlr.press
We analyze two algorithms for approximating the general optimal transport (OT) distance
between two discrete distributions of size $ n $, up to accuracy $\varepsilon $. For the first …
between two discrete distributions of size $ n $, up to accuracy $\varepsilon $. For the first …
Deep transfer learning for few-shot SAR image classification
The reemergence of Deep Neural Networks (DNNs) has lead to high-performance
supervised learning algorithms for the Electro-Optical (EO) domain classification and …
supervised learning algorithms for the Electro-Optical (EO) domain classification and …
Statistical bounds for entropic optimal transport: sample complexity and the central limit theorem
G Mena, J Niles-Weed - Advances in neural information …, 2019 - proceedings.neurips.cc
We prove several fundamental statistical bounds for entropic OT with the squared Euclidean
cost between subgaussian probability measures in arbitrary dimension. First, through a new …
cost between subgaussian probability measures in arbitrary dimension. First, through a new …