Результаты поиска - Malinovsky, Grigory

Theoretical Foundations of Communication-Efficient, Robust, and Practical Distributed and Federated Optimization по Malinovsky, Grigory

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Federated Random Reshuffling with Compression and Variance Reduction по Malinovsky, Grigory, Richtárik, Peter

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Distributed Proximal Splitting Algorithms with Rates and Acceleration по Condat, Laurent, Malinovsky, Grigory, Richtárik, Peter

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Random Reshuffling with Variance Reduction: New Analysis and Better Rates по Malinovsky, Grigory, Sailanbayev, Alibek, Richtárik, Peter

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Server-Side Stepsizes and Sampling Without Replacement Provably Help in Federated Optimization по Malinovsky, Grigory, Mishchenko, Konstantin, Richtárik, Peter

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Variance Reduced ProxSkip: Algorithm, Theory and Application to Federated Learning по Malinovsky, Grigory, Yi, Kai, Richtárik, Peter

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Can 5th Generation Local Training Methods Support Client Sampling? Yes! по Grudzień, Michał, Malinovsky, Grigory, Richtárik, Peter

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An Optimal Algorithm for Strongly Convex Min-min Optimization по Gasnikov, Alexander, Kovalev, Dmitry, Malinovsky, Grigory

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Improving Accelerated Federated Learning with Compression and Importance Sampling по Grudzień, Michał, Malinovsky, Grigory, Richtárik, Peter

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Streamlining in the Riemannian Realm: Efficient Riemannian Optimization with Loopless Variance Reduction по Demidovich, Yury, Malinovsky, Grigory, Richtárik, Peter

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ProxSkip: Yes! Local Gradient Steps Provably Lead to Communication Acceleration! Finally! по Mishchenko, Konstantin, Malinovsky, Grigory, Stich, Sebastian, Richtárik, Peter

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Federated Learning with Regularized Client Participation по Malinovsky, Grigory, Horváth, Samuel, Burlachenko, Konstantin, Richtárik, Peter

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TAMUNA: Doubly Accelerated Distributed Optimization under Partial Participation по Condat, Laurent, Agarský, Ivan, Malinovsky, Grigory, Richtárik, Peter

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A Guide Through the Zoo of Biased SGD по Demidovich, Yury, Malinovsky, Grigory, Sokolov, Igor, Richtárik, Peter

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Byzantine Robustness and Partial Participation Can Be Achieved at Once: Just Clip Gradient Differences по Malinovsky, Grigory, Richtárik, Peter, Horváth, Samuel, Gorbunov, Eduard

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MAST: Model-Agnostic Sparsified Training по Demidovich, Yury, Malinovsky, Grigory, Shulgin, Egor, Richtárik, Peter

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First Provable Guarantees for Practical Private FL: Beyond Restrictive Assumptions по Shulgin, Egor, Malinovsky, Grigory, Khirirat, Sarit, Richtárik, Peter

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Communication-Efficient Gluon in Federated Learning по Qian, Xun, Gaponov, Alexander, Malinovsky, Grigory, Richtárik, Peter

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How Does Muon Work in Private Deep Learning? Scalable Differentially Private Training via Orthogonalization по Gaponov, Alexander, Malinovsky, Grigory, Shulgin, Egor, Richtarik, Peter

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Minibatch Stochastic Three Points Method for Unconstrained Smooth Minimization по Boucherouite, Soumia, Malinovsky, Grigory, Richtárik, Peter, Bergou, EL Houcine

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