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Author Information
Filip Hanzely (KAUST)
Nikita Doikov (Université catholique de Louvain)
Yurii Nesterov (Universite catholique de Louvain)
Peter Richtarik (KAUST)
Peter Richtarik is an Associate Professor of Computer Science and Mathematics at KAUST and an Associate Professor of Mathematics at the University of Edinburgh. He is an EPSRC Fellow in Mathematical Sciences, Fellow of the Alan Turing Institute, and is affiliated with the Visual Computing Center and the Extreme Computing Research Center at KAUST. Dr. Richtarik received his PhD from Cornell University in 2007, and then worked as a Postdoctoral Fellow in Louvain, Belgium, before joining Edinburgh in 2009, and KAUST in 2017. Dr. Richtarik's research interests lie at the intersection of mathematics, computer science, machine learning, optimization, numerical linear algebra, high performance computing and applied probability. Through his recent work on randomized decomposition algorithms (such as randomized coordinate descent methods, stochastic gradient descent methods and their numerous extensions, improvements and variants), he has contributed to the foundations of the emerging field of big data optimization, randomized numerical linear algebra, and stochastic methods for empirical risk minimization. Several of his papers attracted international awards, including the SIAM SIGEST Best Paper Award, the IMA Leslie Fox Prize (2nd prize, twice), and the INFORMS Computing Society Best Student Paper Award (sole runner up). He is the founder and organizer of the Optimization and Big Data workshop series.
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2021 : EF21: A New, Simpler, Theoretically Better, and Practically Faster Error Feedback »
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2021 Poster: ADOM: Accelerated Decentralized Optimization Method for TimeVarying Networks »
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2021 Spotlight: ADOM: Accelerated Decentralized Optimization Method for TimeVarying Networks »
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2021 Poster: MARINA: Faster NonConvex Distributed Learning with Compression »
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2021 Spotlight: MARINA: Faster NonConvex Distributed Learning with Compression »
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2021 Poster: PAGE: A Simple and Optimal Probabilistic Gradient Estimator for Nonconvex Optimization »
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2021 Poster: Stochastic Sign Descent Methods: New Algorithms and Better Theory »
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2021 Poster: Distributed Second Order Methods with Fast Rates and Compressed Communication »
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2021 Spotlight: Distributed Second Order Methods with Fast Rates and Compressed Communication »
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2021 Oral: PAGE: A Simple and Optimal Probabilistic Gradient Estimator for Nonconvex Optimization »
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2021 Spotlight: Stochastic Sign Descent Methods: New Algorithms and Better Theory »
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2020 Poster: Variance Reduced Coordinate Descent with Acceleration: New Method With a Surprising Application to FiniteSum Problems »
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2020 Poster: Acceleration for Compressed Gradient Descent in Distributed and Federated Optimization »
Zhize Li · Dmitry Kovalev · Xun Qian · Peter Richtarik 
2020 Poster: From Local SGD to Local FixedPoint Methods for Federated Learning »
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2020 Poster: Inexact Tensor Methods with Dynamic Accuracies »
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2019 Poster: Nonconvex Variance Reduced Optimization with Arbitrary Sampling »
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2019 Poster: SAGA with Arbitrary Sampling »
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2019 Poster: SGD: General Analysis and Improved Rates »
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2019 Oral: SAGA with Arbitrary Sampling »
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2019 Oral: SGD: General Analysis and Improved Rates »
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2019 Oral: Nonconvex Variance Reduced Optimization with Arbitrary Sampling »
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2018 Poster: Randomized Block Cubic Newton Method »
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2018 Oral: Randomized Block Cubic Newton Method »
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2018 Poster: SGD and Hogwild! Convergence Without the Bounded Gradients Assumption »
Lam Nguyen · PHUONG_HA NGUYEN · Marten van Dijk · Peter Richtarik · Katya Scheinberg · Martin Takac 
2018 Oral: SGD and Hogwild! Convergence Without the Bounded Gradients Assumption »
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