His main research concerns Mathematical optimization, Probability distribution, Computation, Regularization and Algorithm. He studies Linear programming which is a part of Mathematical optimization. His studies in Computation integrate themes in fields like Metric, Iterative proportional fitting and Probability measure.
His Probability measure research incorporates elements of Smoothing, Dimension and Convex optimization. His study in Regularization is interdisciplinary in nature, drawing from both Function, Reproducing kernel Hilbert space and Maximization. His Algorithm research includes elements of Differentiable function, Dynamic time warping, Geodesic and Heat kernel.
His scientific interests lie mostly in Algorithm, Regularization, Differentiable function, Artificial intelligence and Mathematical optimization. Series is closely connected to Dynamic time warping in his research, which is encompassed under the umbrella topic of Algorithm. His Regularization research is multidisciplinary, relying on both Regression, Entropy and Applied mathematics.
His Differentiable function research integrates issues from Smoothing, Probability distribution, Function and Sorting. His Mathematical optimization research includes themes of Computation and Metric. His research integrates issues of Entropy and Probability measure in his study of Computation.
His primary areas of study are Regularization, Differentiable function, Mathematical optimization, Applied mathematics and Linear programming. The Regularization study combines topics in areas such as Linear regression, Regression, Kernel, Space and Entropy. His work deals with themes such as Smoothing, Algorithm and Matrix, which intersect with Differentiable function.
His research in Mathematical optimization intersects with topics in Sorting, Structured prediction, Smoothness and Computation. His studies deal with areas such as Dimension, Covariance, Closed-form expression and Curse of dimensionality as well as Applied mathematics. His Linear programming study also includes
Marco Cuturi mostly deals with Regularization, Differentiable function, Applied mathematics, Degenerate energy levels and Gaussian. His work often combines Regularization and Electromagnetic field studies. His Differentiable function study combines topics in areas such as Sorting, Smoothness, Structured prediction and Constant.
The concepts of his Applied mathematics study are interwoven with issues in Projection, Riemannian optimization, Computation and Theory of computation. Degenerate energy levels is intertwined with Closed-form expression, Covariance, Mass transportation, Numerical resolution and Extension in his research. While working on this project, he studies both Gaussian and Intersection.
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Sinkhorn Distances: Lightspeed Computation of Optimal Transport
neural information processing systems (2013)
Computational Optimal Transport: With Applications to Data Science
Gabriel Peyré;Marco Cuturi.
Computational Optimal Transport
Gabriel Peyré;Marco Cuturi.
Research Papers in Economics (2017)
Iterative Bregman Projections for Regularized Transportation Problems
Jean-David Benamou;Guillaume Carlier;Marco Cuturi;Luca Nenna.
SIAM Journal on Scientific Computing (2015)
Fast Computation of Wasserstein Barycenters
Marco Cuturi;Arnaud Doucet.
international conference on machine learning (2014)
Convolutional wasserstein distances: efficient optimal transportation on geometric domains
Justin Solomon;Fernando de Goes;Gabriel Peyré;Marco Cuturi.
international conference on computer graphics and interactive techniques (2015)
Learning Generative Models with Sinkhorn Divergences
Aude Genevay;Gabriel Peyre;Marco Cuturi.
international conference on artificial intelligence and statistics (2018)
Fast Global Alignment Kernels
international conference on machine learning (2011)
A Kernel for Time Series Based on Global Alignments
M. Cuturi;J. P. Vert;O. Birkenes;T. Matsui.
international conference on acoustics, speech, and signal processing (2007)
Stochastic Optimization for Large-scale Optimal Transport
Aude Genevay;Marco Cuturi;Gabriel Peyré;Francis R. Bach.
neural information processing systems (2016)
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