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Author Information
Peter Richtarik (KAUST)
Marco Cuturi (Google Brain & CREST - ENSAE)
Marco Cuturi is a research scientist at Apple, in Paris. He received his Ph.D. in 11/2005 from the Ecole des Mines de Paris in applied mathematics. Before that he graduated from National School of Statistics (ENSAE) with a master degree (MVA) from ENS Cachan. He worked as a post-doctoral researcher at the Institute of Statistical Mathematics, Tokyo, between 11/2005 and 3/2007 and then in the financial industry between 4/2007 and 9/2008. After working at the ORFE department of Princeton University as a lecturer between 2/2009 and 8/2010, he was at the Graduate School of Informatics of Kyoto University between 9/2010 and 9/2016 as a tenured associate professor. He joined ENSAE in 9/2016 as a professor, where he is now working part-time. He was at Google between 10/2018 and 1/2022. His main employment is now with Apple, since 1/2022, as a research scientist working on fundamental aspects of machine learning.
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2021 : Better Linear Rates for SGD with Data Shuffling »
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2021 : FedMix: A Simple and Communication-Efficient Alternative to Local Methods in Federated Learning »
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2021 : Linear-Time Gromov Wasserstein Distances using Low Rank Couplings and Costs »
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2022 Spotlight: Optimal Algorithms for Decentralized Stochastic Variational Inequalities »
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2022 Spotlight: Lightning Talks 4A-1 »
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2022 Workshop: Federated Learning: Recent Advances and New Challenges »
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2022 Poster: Supervised Training of Conditional Monge Maps »
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2022 Poster: Efficient and Modular Implicit Differentiation »
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2022 Poster: Communication Acceleration of Local Gradient Methods via an Accelerated Primal-Dual Algorithm with an Inexact Prox »
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2022 Poster: A Damped Newton Method Achieves Global $\mathcal O \left(\frac{1}{k^2}\right)$ and Local Quadratic Convergence Rate »
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2022 Poster: EF-BV: A Unified Theory of Error Feedback and Variance Reduction Mechanisms for Biased and Unbiased Compression in Distributed Optimization »
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2022 Poster: Optimal Algorithms for Decentralized Stochastic Variational Inequalities »
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2022 Poster: Low-rank Optimal Transport: Approximation, Statistics and Debiasing »
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2022 Poster: Distributed Methods with Compressed Communication for Solving Variational Inequalities, with Theoretical Guarantees »
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2021 Workshop: Optimal Transport and Machine Learning »
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2021 Poster: Error Compensated Distributed SGD Can Be Accelerated »
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2020 : Poster Session 1 (gather.town) »
Laurent Condat · Tiffany Vlaar · Ohad Shamir · Mohammadi Zaki · Zhize Li · Guan-Horng Liu · Samuel Horváth · Mher Safaryan · Yoni Choukroun · Kumar Shridhar · Nabil Kahale · Jikai Jin · Pratik Kumar Jawanpuria · Gaurav Kumar Yadav · Kazuki Koyama · Junyoung Kim · Xiao Li · Saugata Purkayastha · Adil Salim · Dighanchal Banerjee · Peter Richtarik · Lakshman Mahto · Tian Ye · Bamdev Mishra · Huikang Liu · Jiajie Zhu -
2020 Poster: Projection Robust Wasserstein Distance and Riemannian Optimization »
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2020 Poster: Fixed-Support Wasserstein Barycenters: Computational Hardness and Fast Algorithm »
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2020 Spotlight: Projection Robust Wasserstein Distance and Riemannian Optimization »
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2020 Poster: Learning with Differentiable Pertubed Optimizers »
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2020 Poster: Primal Dual Interpretation of the Proximal Stochastic Gradient Langevin Algorithm »
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2020 Poster: Entropic Optimal Transport between Unbalanced Gaussian Measures has a Closed Form »
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2020 Poster: Linearly Converging Error Compensated SGD »
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2020 Poster: Linear Time Sinkhorn Divergences using Positive Features »
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2020 Oral: Entropic Optimal Transport between Unbalanced Gaussian Measures has a Closed Form »
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2019 Workshop: Optimal Transport for Machine Learning »
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2019 Poster: Subspace Detours: Building Transport Plans that are Optimal on Subspace Projections »
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2019 Poster: Differentiable Ranking and Sorting using Optimal Transport »
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2019 Spotlight: Differentiable Ranking and Sorting using Optimal Transport »
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2019 Poster: RSN: Randomized Subspace Newton »
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2019 Poster: Stochastic Proximal Langevin Algorithm: Potential Splitting and Nonasymptotic Rates »
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2019 Spotlight: Stochastic Proximal Langevin Algorithm: Potential Splitting and Nonasymptotic Rates »
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2019 Poster: Tree-Sliced Variants of Wasserstein Distances »
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2018 Poster: Stochastic Spectral and Conjugate Descent Methods »
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2018 Poster: Large Scale computation of Means and Clusters for Persistence Diagrams using Optimal Transport »
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2018 Poster: Accelerated Stochastic Matrix Inversion: General Theory and Speeding up BFGS Rules for Faster Second-Order Optimization »
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2018 Poster: SEGA: Variance Reduction via Gradient Sketching »
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2018 Poster: Generalizing Point Embeddings using the Wasserstein Space of Elliptical Distributions »
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2017 Workshop: Optimal Transport and Machine Learning »
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2017 Tutorial: A Primer on Optimal Transport »
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2016 Workshop: Time Series Workshop »
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2016 Poster: Wasserstein Training of Restricted Boltzmann Machines »
Grégoire Montavon · Klaus-Robert Müller · Marco Cuturi -
2016 Poster: Stochastic Optimization for Large-scale Optimal Transport »
Aude Genevay · Marco Cuturi · Gabriel Peyré · Francis Bach -
2015 Poster: Quartz: Randomized Dual Coordinate Ascent with Arbitrary Sampling »
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2015 Poster: Principal Geodesic Analysis for Probability Measures under the Optimal Transport Metric »
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2014 Workshop: Optimal Transport and Machine Learning »
Marco Cuturi · Gabriel Peyré · Justin Solomon · Alexander Barvinok · Piotr Indyk · Robert McCann · Adam Oberman -
2013 Poster: Sinkhorn Distances: Lightspeed Computation of Optimal Transport »
Marco Cuturi -
2013 Spotlight: Sinkhorn Distances: Lightspeed Computation of Optimal Transport »
Marco Cuturi -
2009 Poster: White Functionals for Anomaly Detection in Dynamical Systems »
Marco Cuturi · Jean-Philippe Vert · Alexandre d'Aspremont -
2006 Poster: Kernels on Structured Objects Through Nested Histograms »
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