M. Cuturi
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M. Cuturicomputer-science Degrees
Computer Science
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#8584
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Machine Learning
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M. Cuturimathematics Degrees
Mathematics
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Measure Theory
#1829
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#2236
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Computer Science Mathematics
Why Is M. Cuturi Influential?
(Suggest an Edit or Addition)M. Cuturi's Published Works
Number of citations in a given year to any of this author's works
Total number of citations to an author for the works they published in a given year. This highlights publication of the most important work(s) by the author
Published Works
- Sinkhorn Distances: Lightspeed Computation of Optimal Transport (2013) (2483)
- Computational Optimal Transport (2018) (1315)
- Computational Optimal Transport: With Applications to Data Science (2019) (766)
- Iterative Bregman Projections for Regularized Transportation Problems (2014) (649)
- Fast Computation of Wasserstein Barycenters (2013) (607)
- Learning Generative Models with Sinkhorn Divergences (2017) (464)
- Soft-DTW: a Differentiable Loss Function for Time-Series (2017) (360)
- Stochastic Optimization for Large-scale Optimal Transport (2016) (346)
- Fast Global Alignment Kernels (2011) (317)
- On Wasserstein Two-Sample Testing and Related Families of Nonparametric Tests (2015) (305)
- A Kernel for Time Series Based on Global Alignments (2006) (256)
- Gromov-Wasserstein Averaging of Kernel and Distance Matrices (2016) (251)
- Sliced Wasserstein Kernel for Persistence Diagrams (2017) (202)
- Sample Complexity of Sinkhorn Divergences (2018) (195)
- A Smoothed Dual Approach for Variational Wasserstein Problems (2015) (165)
- Convolutional wasserstein distances (2015) (151)
- Fast Dictionary Learning with a Smoothed Wasserstein Loss (2016) (127)
- Sinkhorn Distances: Lightspeed Computation of Optimal Transportation Distances (2013) (112)
- Subspace Robust Wasserstein distances (2019) (110)
- Wasserstein Dictionary Learning: Optimal Transport-based unsupervised non-linear dictionary learning (2017) (109)
- Wasserstein Training of Restricted Boltzmann Machines (2016) (103)
- Fast Optimal Transport Averaging of Neuroimaging Data (2015) (100)
- Ground metric learning (2011) (99)
- Differentiable Ranking and Sorting using Optimal Transport (2019) (90)
- Semigroup Kernels on Measures (2005) (87)
- Wasserstein discriminant analysis (2016) (79)
- Wasserstein Barycentric Coordinates: Histogram Regression Using Optimal Transport (2021) (76)
- Efficient and Modular Implicit Differentiation (2021) (74)
- Learning with Differentiable Perturbed Optimizers (2020) (70)
- Generalizing Point Embeddings using the Wasserstein Space of Elliptical Distributions (2018) (69)
- Principal Geodesic Analysis for Probability Measures under the Optimal Transport Metric (2015) (67)
- Unsupervised Hyperalignment for Multilingual Word Embeddings (2018) (55)
- Large Scale computation of Means and Clusters for Persistence Diagrams using Optimal Transport (2018) (55)
- Wasserstein barycentric coordinates (2016) (54)
- Entropic Optimal Transport between Unbalanced Gaussian Measures has a Closed Form (2020) (50)
- Sinkhorn-AutoDiff: Tractable Wasserstein Learning of Generative Models (2017) (49)
- Tree-Sliced Variants of Wasserstein Distances (2019) (49)
- Missing Data Imputation using Optimal Transport (2020) (49)
- GAN and VAE from an Optimal Transport Point of View (2017) (49)
- Learning with Differentiable Pertubed Optimizers (2020) (45)
- Fixed-Support Wasserstein Barycenters: Computational Hardness and Fast Algorithm (2020) (45)
- The context-tree kernel for strings (2005) (44)
- On the Complexity of Approximating Multimarginal Optimal Transport (2019) (43)
- Autoregressive Kernels For Time Series (2011) (43)
- Projection Robust Wasserstein Distance and Riemannian Optimization (2020) (42)
- Geodesic PCA versus Log-PCA of Histograms in the Wasserstein Space (2018) (40)
- Sinkhorn Distances: Lightspeed Computation of Optimal Transportation (2013) (39)
- Semi-dual Regularized Optimal Transport (2018) (38)
- Debiased Sinkhorn barycenters (2020) (37)
- Wasserstein regularization for sparse multi-task regression (2018) (37)
- On Projection Robust Optimal Transport: Sample Complexity and Model Misspecification (2020) (36)
- Deep multi-class learning from label proportions (2019) (30)
- Proximal Optimal Transport Modeling of Population Dynamics (2021) (28)
- Semigroup Kernels on Finite Sets (2004) (26)
- Subspace Detours: Building Transport Plans that are Optimal on Subspace Projections (2019) (22)
- Optimal Transport Tools (OTT): A JAX Toolbox for all things Wasserstein (2022) (22)
- Permanents, Transport Polytopes and Positive Definite Kernels on Histograms (2007) (21)
- Regularity as Regularization: Smooth and Strongly Convex Brenier Potentials in Optimal Transport (2019) (21)
- Regularized Optimal Transport is Ground Cost Adversarial (2020) (20)
- Unsupervised Riemannian Metric Learning for Histograms Using Aitchison Transformations (2015) (19)
- Information Geometry for Regularized Optimal Transport and Barycenters of Patterns (2019) (19)
- Mapping kernels for trees (2011) (19)
- JKOnet: Proximal Optimal Transport Modeling of Population Dynamics (2021) (18)
- Multi-subject MEG/EEG source imaging with sparse multi-task regression (2019) (18)
- Wasserstein Training of Boltzmann Machines (2015) (18)
- Linear-Time Gromov Wasserstein Distances using Low Rank Couplings and Costs (2021) (18)
- Low-Rank Sinkhorn Factorization (2021) (18)
- Spatio-Temporal Alignments: Optimal transport through space and time (2019) (16)
- Noisy Adaptive Group Testing using Bayesian Sequential Experimental Design (2020) (16)
- Precision-Recall Curves Using Information Divergence Frontiers (2019) (16)
- Supervised Training of Conditional Monge Maps (2022) (15)
- Positive Definite Kernels in Machine Learning (2009) (15)
- Fast and Robust Comparison of Probability Measures in Heterogeneous Spaces (2020) (15)
- Mean Reversion with a Variance Threshold (2013) (15)
- Stochastic Deep Networks (2018) (15)
- Linear Time Sinkhorn Divergences using Positive Features (2020) (14)
- Ground Metric Learning on Graphs (2019) (13)
- A mutual information kernel for sequences (2004) (12)
- Equitable and Optimal Transport with Multiple Agents (2020) (10)
- Log-PCA versus Geodesic PCA of histograms in the Wasserstein space (2017) (10)
- Kernels on Structured Objects Through Nested Histograms (2006) (10)
- Recovering Stochastic Dynamics via Gaussian Schrödinger Bridges (2022) (9)
- Adaptive Euclidean maps for histograms: generalized Aitchison embeddings (2015) (8)
- Group level MEG/EEG source imaging via optimal transport: minimum Wasserstein estimates (2019) (7)
- Revisiting Fixed Support Wasserstein Barycenter: Computational Hardness and Efficient Algorithms (2020) (7)
- Tree-Sliced Approximation of Wasserstein Distances (2019) (6)
- Mean-Reverting Portfolios: Tradeoffs Between Sparsity and Volatility (2015) (6)
- Differentiable Sorting using Optimal Transport: The Sinkhorn CDF and Quantile Operator (2019) (6)
- Etude de noyaux de semigroupe pour objets structurés dans le cadre de l'apprentissage statistique (2005) (5)
- An Algorithmic Approach to Compute Principal Geodesics in the Wasserstein Space (2015) (5)
- Shot Boundary Detection and Low-Level Feature Extraction Experiments for TRECVID 2005 (2005) (5)
- Debiaser Beware: Pitfalls of Centering Regularized Transport Maps (2022) (5)
- Supervised Quantile Normalization for Low Rank Matrix Factorization (2020) (4)
- Shot Boundary Detection and High-Level Feature Extraction Experiments for TRECVID 2006. (2005) (4)
- Low-rank Optimal Transport: Approximation, Statistics and Debiasing (2022) (4)
- Learning from Structured Objects with Semigroup Kernels (2006) (3)
- Supervised Quantile Normalization for Low-rank Matrix Approximation (2020) (3)
- Rethinking Initialization of the Sinkhorn Algorithm (2022) (3)
- Generalized Aitchison Embeddings for Histograms (2013) (3)
- Handling Multiple Costs in Optimal Transport: Strong Duality and Efficient Computation (2020) (3)
- A Distance Between Text Documents based on Topic Models and Ground Metric Learning (人工知能学会全国大会(第26回)文化,科学技術と未来) -- (機械学習) (2012) (3)
- Evaluating Generative Models Using Divergence Frontiers (2019) (3)
- White Functionals for Anomaly Detection in Dynamical Systems (2009) (3)
- A covariance kernel for proteins (2003) (2)
- Editorial IMA IAI - Information and Inference special issue on optimal transport in data sciences (2019) (2)
- Optimal transport-based dictionary learning and its application to Euclid-like Point Spread Function representation (2017) (2)
- Positivity and Transportation (2012) (2)
- Optimal Transport meets Probability, Statistics and Machine Learning (2017) (2)
- Randomized Stochastic Gradient Descent Ascent (2021) (2)
- The Schr\"odinger Bridge between Gaussian Measures has a Closed Form (2022) (2)
- Simultaneous Multiple-Prompt Guided Generation Using Differentiable Optimal Transport (2022) (1)
- The Monge Gap: A Regularizer to Learn All Transport Maps (2023) (1)
- Multiresolution Kernels (2005) (1)
- Monge, Bregman and Occam: Interpretable Optimal Transport in High-Dimensions with Feature-Sparse Maps (2023) (0)
- Optimal Transport and Barycenters Between Several Metric Spaces (2016) (0)
- Ground Metric Learning on Graphs (2020) (0)
- Algorithmic Wasserstein Distances and Applications to Histogram Regresion (2016) (0)
- Kernels for Measures Defined on the Gram Matrix of their Support (2009) (0)
- Averaging Spatio-temporal Signals using Optimal Transport and Soft Alignments (2022) (0)
- Adaptive Euclidean maps for histograms: generalized Aitchison embeddings (2014) (0)
- Wasserstein discriminant analysis (2018) (0)
- Memo for the 2021 SIGGRAPH course Computational Optimal Transport (2021) (0)
- The generating function of the polytope of transport matrices U(r,c) as a positive semidefinite kernel of the marginals r and c (2006) (0)
- Community Extraction Based on Density-first Search and Its Application to Bid Data (特集 「命題論理の充足可能性問題SATと応用技術」および一般) (2015) (0)
- Optimal Transport Geometry for Sentiment Analysis (2016) (0)
- LSMMD-MA: Scaling multimodal data integration for single-cell genomics data analysis (2022) (0)
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