Marco Cuturi

According to our database1, Marco Cuturi authored at least 94 papers between 2004 and 2022.

Collaborative distances:
  • Dijkstra number2 of four.
  • Erdős number3 of two.

Timeline

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Bibliography

2022
Supervised Training of Conditional Monge Maps.
CoRR, 2022

Rethinking Initialization of the Sinkhorn Algorithm.
CoRR, 2022

Low-rank Optimal Transport: Approximation, Statistics and Debiasing.
CoRR, 2022

Simultaneous Multiple-Prompt Guided Generation Using Differentiable Optimal Transport.
CoRR, 2022

Averaging Spatio-temporal Signals using Optimal Transport and Soft Alignments.
CoRR, 2022

Recovering Stochastic Dynamics via Gaussian Schrödinger Bridges.
CoRR, 2022

Optimal Transport Tools (OTT): A JAX Toolbox for all things Wasserstein.
CoRR, 2022

Linear-Time Gromov Wasserstein Distances using Low Rank Couplings and Costs.
Proceedings of the International Conference on Machine Learning, 2022

Debiaser Beware: Pitfalls of Centering Regularized Transport Maps.
Proceedings of the International Conference on Machine Learning, 2022

Randomized Stochastic Gradient Descent Ascent.
Proceedings of the International Conference on Artificial Intelligence and Statistics, 2022

Proximal Optimal Transport Modeling of Population Dynamics.
Proceedings of the International Conference on Artificial Intelligence and Statistics, 2022

2021
Ground Metric Learning on Graphs.
J. Math. Imaging Vis., 2021

JKOnet: Proximal Optimal Transport Modeling of Population Dynamics.
CoRR, 2021

Efficient and Modular Implicit Differentiation.
CoRR, 2021

Low-Rank Sinkhorn Factorization.
Proceedings of the 38th International Conference on Machine Learning, 2021

Equitable and Optimal Transport with Multiple Agents.
Proceedings of the 24th International Conference on Artificial Intelligence and Statistics, 2021

On Projection Robust Optimal Transport: Sample Complexity and Model Misspecification.
Proceedings of the 24th International Conference on Artificial Intelligence and Statistics, 2021

2020
Multi-subject MEG/EEG source imaging with sparse multi-task regression.
NeuroImage, 2020

Handling Multiple Costs in Optimal Transport: Strong Duality and Efficient Computation.
CoRR, 2020

Noisy Adaptive Group Testing using Bayesian Sequential Experimental Design.
CoRR, 2020

Learning with Differentiable Perturbed Optimizers.
CoRR, 2020

Revisiting Fixed Support Wasserstein Barycenter: Computational Hardness and Efficient Algorithms.
CoRR, 2020

Supervised Quantile Normalization for Low-rank Matrix Approximation.
CoRR, 2020

Fast and Robust Comparison of Probability Measures in Heterogeneous Spaces.
CoRR, 2020

Linear Time Sinkhorn Divergences using Positive Features.
Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

Fixed-Support Wasserstein Barycenters: Computational Hardness and Fast Algorithm.
Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

Projection Robust Wasserstein Distance and Riemannian Optimization.
Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

Entropic Optimal Transport between Unbalanced Gaussian Measures has a Closed Form.
Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

Learning with Differentiable Pertubed Optimizers.
Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

Regularized Optimal Transport is Ground Cost Adversarial.
Proceedings of the 37th International Conference on Machine Learning, 2020

Missing Data Imputation using Optimal Transport.
Proceedings of the 37th International Conference on Machine Learning, 2020

Debiased Sinkhorn barycenters.
Proceedings of the 37th International Conference on Machine Learning, 2020

Supervised Quantile Normalization for Low Rank Matrix Factorization.
Proceedings of the 37th International Conference on Machine Learning, 2020

Regularity as Regularization: Smooth and Strongly Convex Brenier Potentials in Optimal Transport.
Proceedings of the 23rd International Conference on Artificial Intelligence and Statistics, 2020

Spatio-temporal alignments: Optimal transport through space and time.
Proceedings of the 23rd International Conference on Artificial Intelligence and Statistics, 2020

Precision-Recall Curves Using Information Divergence Frontiers.
Proceedings of the 23rd International Conference on Artificial Intelligence and Statistics, 2020

2019
Information Geometry for Regularized Optimal Transport and Barycenters of Patterns.
Neural Comput., 2019

Computational Optimal Transport.
Found. Trends Mach. Learn., 2019

On the Complexity of Approximating Multimarginal Optimal Transport.
CoRR, 2019

Deep multi-class learning from label proportions.
CoRR, 2019

Differentiable Sorting using Optimal Transport: The Sinkhorn CDF and Quantile Operator.
CoRR, 2019

Evaluating Generative Models Using Divergence Frontiers.
CoRR, 2019

Tree-Sliced Approximation of Wasserstein Distances.
CoRR, 2019

Subspace Detours: Building Transport Plans that are Optimal on Subspace Projections.
Proceedings of the Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, 2019

Tree-Sliced Variants of Wasserstein Distances.
Proceedings of the Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, 2019

Differentiable Ranking and Sorting using Optimal Transport.
Proceedings of the Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, 2019

Group Level MEG/EEG Source Imaging via Optimal Transport: Minimum Wasserstein Estimates.
Proceedings of the Information Processing in Medical Imaging, 2019

Subspace Robust Wasserstein Distances.
Proceedings of the 36th International Conference on Machine Learning, 2019

Stochastic Deep Networks.
Proceedings of the 36th International Conference on Machine Learning, 2019

Unsupervised Hyper-alignment for Multilingual Word Embeddings.
Proceedings of the 7th International Conference on Learning Representations, 2019

Wasserstein regularization for sparse multi-task regression.
Proceedings of the 22nd International Conference on Artificial Intelligence and Statistics, 2019

Sample Complexity of Sinkhorn Divergences.
Proceedings of the 22nd International Conference on Artificial Intelligence and Statistics, 2019

2018
Geodesic PCA versus Log-PCA of Histograms in the Wasserstein Space.
SIAM J. Sci. Comput., 2018

Semidual Regularized Optimal Transport.
SIAM Rev., 2018

Wasserstein Dictionary Learning: Optimal Transport-Based Unsupervised Nonlinear Dictionary Learning.
SIAM J. Imaging Sci., 2018

Wasserstein discriminant analysis.
Mach. Learn., 2018

Semi-dual Regularized Optimal Transport.
CoRR, 2018

Unsupervised Hyperalignment for Multilingual Word Embeddings.
CoRR, 2018

Generalizing Point Embeddings using the Wasserstein Space of Elliptical Distributions.
Proceedings of the Advances in Neural Information Processing Systems 31: Annual Conference on Neural Information Processing Systems 2018, 2018

Large Scale computation of Means and Clusters for Persistence Diagrams using Optimal Transport.
Proceedings of the Advances in Neural Information Processing Systems 31: Annual Conference on Neural Information Processing Systems 2018, 2018

Learning Generative Models with Sinkhorn Divergences.
Proceedings of the International Conference on Artificial Intelligence and Statistics, 2018

2017
On Wasserstein Two-Sample Testing and Related Families of Nonparametric Tests.
Entropy, 2017

Wasserstein Dictionary Learning: Optimal Transport-based unsupervised non-linear dictionary learning.
CoRR, 2017

Soft-DTW: a Differentiable Loss Function for Time-Series.
Proceedings of the 34th International Conference on Machine Learning, 2017

Sliced Wasserstein Kernel for Persistence Diagrams.
Proceedings of the 34th International Conference on Machine Learning, 2017

2016
Wasserstein barycentric coordinates: histogram regression using optimal transport.
ACM Trans. Graph., 2016

A Smoothed Dual Approach for Variational Wasserstein Problems.
SIAM J. Imaging Sci., 2016

Wasserstein Training of Restricted Boltzmann Machines.
Proceedings of the Advances in Neural Information Processing Systems 29: Annual Conference on Neural Information Processing Systems 2016, 2016

Stochastic Optimization for Large-scale Optimal Transport.
Proceedings of the Advances in Neural Information Processing Systems 29: Annual Conference on Neural Information Processing Systems 2016, 2016

Gromov-Wasserstein Averaging of Kernel and Distance Matrices.
Proceedings of the 33nd International Conference on Machine Learning, 2016

Fast Dictionary Learning with a Smoothed Wasserstein Loss.
Proceedings of the 19th International Conference on Artificial Intelligence and Statistics, 2016

2015
Convolutional wasserstein distances: efficient optimal transportation on geometric domains.
ACM Trans. Graph., 2015

Iterative Bregman Projections for Regularized Transportation Problems.
SIAM J. Sci. Comput., 2015

Adaptive Euclidean maps for histograms: generalized Aitchison embeddings.
Mach. Learn., 2015

Wasserstein Training of Boltzmann Machines.
CoRR, 2015

Principal Geodesic Analysis for Probability Measures under the Optimal Transport Metric.
Proceedings of the Advances in Neural Information Processing Systems 28: Annual Conference on Neural Information Processing Systems 2015, 2015

Fast Optimal Transport Averaging of Neuroimaging Data.
Proceedings of the Information Processing in Medical Imaging, 2015

Unsupervised Riemannian Metric Learning for Histograms Using Aitchison Transformations.
Proceedings of the 32nd International Conference on Machine Learning, 2015

2014
Ground metric learning.
J. Mach. Learn. Res., 2014

Fast Computation of Wasserstein Barycenters.
Proceedings of the 31th International Conference on Machine Learning, 2014

2013
Sinkhorn Distances: Lightspeed Computation of Optimal Transport.
Proceedings of the Advances in Neural Information Processing Systems 26: 27th Annual Conference on Neural Information Processing Systems 2013. Proceedings of a meeting held December 5-8, 2013

Mean Reversion with a Variance Threshold.
Proceedings of the 30th International Conference on Machine Learning, 2013

Generalized Aitchison Embeddings for Histograms.
Proceedings of the Asian Conference on Machine Learning, 2013

2011
Mapping kernels for trees.
Proceedings of the 28th International Conference on Machine Learning, 2011

Fast Global Alignment Kernels.
Proceedings of the 28th International Conference on Machine Learning, 2011

2009
White Functionals for Anomaly Detection in Dynamical Systems.
Proceedings of the Advances in Neural Information Processing Systems 22: 23rd Annual Conference on Neural Information Processing Systems 2009. Proceedings of a meeting held 7-10 December 2009, 2009

2007
Permanents, Transport Polytopes and Positive Definite Kernels on Histograms.
Proceedings of the IJCAI 2007, 2007

A Kernel for Time Series Based on Global Alignments.
Proceedings of the IEEE International Conference on Acoustics, 2007

2006
Shot Boundary Detection and High-Level Feature Extraction Experiments for TRECVID 2006.
Proceedings of the 2006 TREC Video Retrieval Evaluation, 2006

Kernels on Structured Objects Through Nested Histograms.
Proceedings of the Advances in Neural Information Processing Systems 19, 2006

2005
The context-tree kernel for strings.
Neural Networks, 2005

Semigroup Kernels on Measures.
J. Mach. Learn. Res., 2005

Multiresolution Kernels
CoRR, 2005

2004
Semigroup Kernels on Finite Sets.
Proceedings of the Advances in Neural Information Processing Systems 17 [Neural Information Processing Systems, 2004


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