Peter Richtárik

ORCID

Publication Activity (10 Years)

Years Active: 2010-2024
Publications (10 Years): 302

Top Venues

Publications

Peter Richtárik, Elnur Gasanov, Konstantin Burlachenko
Error Feedback Reloaded: From Quadratic to Arithmetic Mean of Smoothness Constants. CoRR (2024)
Alexander Tyurin, Marta Pozzi, Ivan Ilin, Peter Richtárik
Shadowheart SGD: Distributed Asynchronous SGD with Optimal Time Complexity Under Arbitrary Computation and Communication Heterogeneity. CoRR (2024)
Kaja Gruntkowska, Alexander Tyurin, Peter Richtárik
Improving the Worst-Case Bidirectional Communication Complexity for Nonconvex Distributed Optimization under Function Similarity. CoRR (2024)
Andrei Panferov, Yury Demidovich, Ahmad Rammal, Peter Richtárik
Correlated Quantization for Faster Nonconvex Distributed Optimization. CoRR (2024)
Alexander Tyurin, Peter Richtárik
Optimal Time Complexities of Parallel Stochastic Optimization Methods Under a Fixed Computation Model. NeurIPS (2023)
Jihao Xin, Ivan Ilin, Shunkang Zhang, Marco Canini, Peter Richtárik
Kimad: Adaptive Gradient Compression with Bandwidth Awareness. DistributedML@CoNEXT (2023)
Yury Demidovich, Grigory Malinovsky, Igor Sokolov, Peter Richtárik
A Guide Through the Zoo of Biased SGD. NeurIPS (2023)
Laurent Condat, Peter Richtárik
RandProx: Primal-Dual Optimization Algorithms with Randomized Proximal Updates. ICLR (2023)
Jihao Xin, Ivan Ilin, Shunkang Zhang, Marco Canini, Peter Richtárik
Kimad: Adaptive Gradient Compression with Bandwidth Awareness. CoRR (2023)
Ahmed Khaled, Peter Richtárik
Better Theory for SGD in the Nonconvex World. Trans. Mach. Learn. Res. 2023 (2023)
Alexander Tyurin, Peter Richtárik
2Direction: Theoretically Faster Distributed Training with Bidirectional Communication Compression. NeurIPS (2023)
Ahmed Khaled, Othmane Sebbouh, Nicolas Loizou, Robert M. Gower, Peter Richtárik
Unified Analysis of Stochastic Gradient Methods for Composite Convex and Smooth Optimization. J. Optim. Theory Appl. 199 (2) (2023)
Michal Grudzien, Grigory Malinovsky, Peter Richtárik
Improving Accelerated Federated Learning with Compression and Importance Sampling. CoRR (2023)
Zheng Shi, Abdurakhmon Sadiev, Nicolas Loizou, Peter Richtárik, Martin Takác
AI-SARAH: Adaptive and Implicit Stochastic Recursive Gradient Methods. Trans. Mach. Learn. Res. 2023 (2023)
Rafal Szlendak, Elnur Gasanov, Peter Richtárik
Understanding Progressive Training Through the Framework of Randomized Coordinate Descent. CoRR (2023)
Ilyas Fatkhullin, Alexander Tyurin, Peter Richtárik
Momentum Provably Improves Error Feedback! CoRR (2023)
Avetik G. Karagulyan, Peter Richtárik
ELF: Federated Langevin Algorithms with Primal, Dual and Bidirectional Compression. CoRR (2023)
Grigory Malinovsky, Peter Richtárik, Samuel Horváth, Eduard Gorbunov
Byzantine Robustness and Partial Participation Can Be Achieved Simultaneously: Just Clip Gradient Differences. CoRR (2023)
Abdurakhmon Sadiev, Marina Danilova, Eduard Gorbunov, Samuel Horváth, Gauthier Gidel, Pavel E. Dvurechensky, Alexander V. Gasnikov, Peter Richtárik
High-Probability Bounds for Stochastic Optimization and Variational Inequalities: the Case of Unbounded Variance. ICML (2023)
Egor Shulgin, Peter Richtárik
Towards a Better Theoretical Understanding of Independent Subnetwork Training. CoRR (2023)
Laurent Condat, Grigory Malinovsky, Peter Richtárik
TAMUNA: Accelerated Federated Learning with Local Training and Partial Participation. CoRR (2023)
Yury Demidovich, Grigory Malinovsky, Igor Sokolov, Peter Richtárik
A Guide Through the Zoo of Biased SGD. CoRR (2023)
Yury Demidovich, Grigory Malinovsky, Egor Shulgin, Peter Richtárik
MAST: Model-Agnostic Sparsified Training. CoRR (2023)
Michal Grudzien, Grigory Malinovsky, Peter Richtárik
Can 5th Generation Local Training Methods Support Client Sampling? Yes! AISTATS (2023)
Konstantin Burlachenko, Abdulmajeed Alrowithi, Fahad Ali Albalawi, Peter Richtárik
Federated Learning is Better with Non-Homomorphic Encryption. DistributedML@CoNEXT (2023)
Alexander Tyurin, Peter Richtárik
A Computation and Communication Efficient Method for Distributed Nonconvex Problems in the Partial Participation Setting. NeurIPS (2023)
Grigory Malinovsky, Alibek Sailanbayev, Peter Richtárik
Random Reshuffling with Variance Reduction: New Analysis and Better Rates. UAI (2023)
Grigory Malinovsky, Konstantin Mishchenko, Peter Richtárik
Server-Side Stepsizes and Sampling Without Replacement Provably Help in Federated Optimization. DistributedML@CoNEXT (2023)
Alexander Tyurin, Peter Richtárik
DASHA: Distributed Nonconvex Optimization with Communication Compression and Optimal Oracle Complexity. ICLR (2023)
Konstantin Mishchenko, Slavomír Hanzely, Peter Richtárik
Convergence of First-Order Algorithms for Meta-Learning with Moreau Envelopes. CoRR (2023)
Konstantin Burlachenko, Abdulmajeed Alrowithi, Fahad Ali Albalawi, Peter Richtárik
Federated Learning is Better with Non-Homomorphic Encryption. CoRR (2023)
Ilyas Fatkhullin, Alexander Tyurin, Peter Richtárik
Momentum Provably Improves Error Feedback! NeurIPS (2023)
Kai Yi, Laurent Condat, Peter Richtárik
Explicit Personalization and Local Training: Double Communication Acceleration in Federated Learning. CoRR (2023)
Xun Qian, Hanze Dong, Tong Zhang, Peter Richtárik
Catalyst Acceleration of Error Compensated Methods Leads to Better Communication Complexity. AISTATS (2023)
Eduard Gorbunov, Abdurakhmon Sadiev, Marina Danilova, Samuel Horváth, Gauthier Gidel, Pavel E. Dvurechensky, Alexander V. Gasnikov, Peter Richtárik
High-Probability Convergence for Composite and Distributed Stochastic Minimization and Variational Inequalities with Heavy-Tailed Noise. CoRR (2023)
Peter Richtárik, Elnur Gasanov, Konstantin Burlachenko
Error Feedback Shines when Features are Rare. CoRR (2023)
Kaja Gruntkowska, Alexander Tyurin, Peter Richtárik
EF21-P and Friends: Improved Theoretical Communication Complexity for Distributed Optimization with Bidirectional Compression. ICML (2023)
Grigory Malinovsky, Samuel Horváth, Konstantin Burlachenko, Peter Richtárik
Federated Learning with Regularized Client Participation. CoRR (2023)
Lukang Sun, Avetik G. Karagulyan, Peter Richtárik
Convergence of Stein Variational Gradient Descent under a Weaker Smoothness Condition. AISTATS (2023)
Jihao Xin, Marco Canini, Peter Richtárik, Samuel Horváth
Global-QSGD: Practical Floatless Quantization for Distributed Learning with Theoretical Guarantees. CoRR (2023)
Samuel Horváth, Dmitry Kovalev, Konstantin Mishchenko, Peter Richtárik, Sebastian U. Stich
Stochastic distributed learning with gradient quantization and double-variance reduction. Optim. Methods Softw. 38 (1) (2023)
Abdurakhmon Sadiev, Marina Danilova, Eduard Gorbunov, Samuel Horváth, Gauthier Gidel, Pavel E. Dvurechensky, Alexander V. Gasnikov, Peter Richtárik
High-Probability Bounds for Stochastic Optimization and Variational Inequalities: the Case of Unbounded Variance. CoRR (2023)
Ahmad Rammal, Kaja Gruntkowska, Nikita Fedin, Eduard Gorbunov, Peter Richtárik
Communication Compression for Byzantine Robust Learning: New Efficient Algorithms and Improved Rates. CoRR (2023)
Sarit Khirirat, Eduard Gorbunov, Samuel Horváth, Rustem Islamov, Fakhri Karray, Peter Richtárik
Clip21: Error Feedback for Gradient Clipping. CoRR (2023)
Eduard Gorbunov, Samuel Horváth, Peter Richtárik, Gauthier Gidel
Variance Reduction is an Antidote to Byzantines: Better Rates, Weaker Assumptions and Communication Compression as a Cherry on the Top. ICLR (2023)
Grigory Malinovsky, Kai Yi, Peter Richtárik
Variance Reduced ProxSkip: Algorithm, Theory and Application to Federated Learning. NeurIPS (2022)
Egor Shulgin, Peter Richtárik
Shifted Compression Framework: Generalizations and Improvements. CoRR (2022)
Laurent Condat, Kai Yi, Peter Richtárik
EF-BV: A Unified Theory of Error Feedback and Variance Reduction Mechanisms for Biased and Unbiased Compression in Distributed Optimization. CoRR (2022)
Haoyu Zhao, Boyue Li, Zhize Li, Peter Richtárik, Yuejie Chi
BEER: Fast $O(1/T)$ Rate for Decentralized Nonconvex Optimization with Communication Compression. NeurIPS (2022)
Wenlin Chen, Samuel Horváth, Peter Richtárik
Optimal Client Sampling for Federated Learning. Trans. Mach. Learn. Res. 2022 (2022)
Samuel Horváth, Chen-Yu Ho, Ludovit Horvath, Atal Narayan Sahu, Marco Canini, Peter Richtárik
Natural Compression for Distributed Deep Learning. MSML (2022)
Adil Salim, Laurent Condat, Konstantin Mishchenko, Peter Richtárik
Dualize, Split, Randomize: Toward Fast Nonsmooth Optimization Algorithms. J. Optim. Theory Appl. 195 (1) (2022)
Egor Shulgin, Peter Richtárik
Shifted compression framework: generalizations and improvements. UAI (2022)
Konstantin Mishchenko, Ahmed Khaled, Peter Richtárik
Proximal and Federated Random Reshuffling. ICML (2022)
Aleksandr Beznosikov, Peter Richtárik, Michael Diskin, Max Ryabinin, Alexander V. Gasnikov
Distributed Methods with Compressed Communication for Solving Variational Inequalities, with Theoretical Guarantees. NeurIPS (2022)
Soumia Boucherouite, Grigory Malinovsky, Peter Richtárik, El Houcine Bergou
Minibatch Stochastic Three Points Method for Unconstrained Smooth Minimization. CoRR (2022)
Albert S. Berahas, Majid Jahani, Peter Richtárik, Martin Takác
Quasi-Newton methods for machine learning: forget the past, just sample. Optim. Methods Softw. 37 (5) (2022)
Samuel Horváth, Maziar Sanjabi, Lin Xiao, Peter Richtárik, Michael G. Rabbat
FedShuffle: Recipes for Better Use of Local Work in Federated Learning. Trans. Mach. Learn. Res. 2022 (2022)
Abdurakhmon Sadiev, Dmitry Kovalev, Peter Richtárik
Communication Acceleration of Local Gradient Methods via an Accelerated Primal-Dual Algorithm with Inexact Prox. CoRR (2022)
Elnur Gasanov, Ahmed Khaled, Samuel Horváth, Peter Richtárik
FLIX: A Simple and Communication-Efficient Alternative to Local Methods in Federated Learning. AISTATS (2022)
Motasem Alfarra, Juan C. Pérez, Egor Shulgin, Peter Richtárik, Bernard Ghanem
Certified Robustness in Federated Learning. CoRR (2022)
Konstantin Mishchenko, Grigory Malinovsky, Sebastian Stich, Peter Richtárik
ProxSkip: Yes! Local Gradient Steps Provably Lead to Communication Acceleration! Finally! CoRR (2022)
Bokun Wang, Mher Safaryan, Peter Richtárik
Theoretically Better and Numerically Faster Distributed Optimization with Smoothness-Aware Quantization Techniques. NeurIPS (2022)
Grigory Malinovsky, Kai Yi, Peter Richtárik
Variance Reduced ProxSkip: Algorithm, Theory and Application to Federated Learning. CoRR (2022)
Artavazd Maranjyan, Mher Safaryan, Peter Richtárik
GradSkip: Communication-Accelerated Local Gradient Methods with Better Computational Complexity. CoRR (2022)
Konstantin Mishchenko, Bokun Wang, Dmitry Kovalev, Peter Richtárik
IntSGD: Adaptive Floatless Compression of Stochastic Gradients. ICLR (2022)
Rafal Szlendak, Alexander Tyurin, Peter Richtárik
Permutation Compressors for Provably Faster Distributed Nonconvex Optimization. ICLR (2022)
Laurent Condat, Peter Richtárik
MURANA: A Generic Framework for Stochastic Variance-Reduced Optimization. MSML (2022)
Majid Jahani, Sergey Rusakov, Zheng Shi, Peter Richtárik, Michael W. Mahoney, Martin Takác
Doubly Adaptive Scaled Algorithm for Machine Learning Using Second-Order Information. ICLR (2022)
Peter Richtárik, Igor Sokolov, Ilyas Fatkhullin, Elnur Gasanov, Zhize Li, Eduard A. Gorbunov
3PC: Three Point Compressors for Communication-Efficient Distributed Training and a Better Theory for Lazy Aggregation. CoRR (2022)
Aritra Dutta, El Houcine Bergou, Yunming Xiao, Marco Canini, Peter Richtárik
Direct nonlinear acceleration. EURO J. Comput. Optim. 10 (2022)
Slavomír Hanzely, Dmitry Kamzolov, Dmitry Pasechnyuk, Alexander V. Gasnikov, Peter Richtárik, Martin Takác
A Damped Newton Method Achieves Global $\mathcal O \left(\frac{1}{k^2}\right)$ and Local Quadratic Convergence Rate. NeurIPS (2022)
Laurent Condat, Kai Yi, Peter Richtárik
EF-BV: A Unified Theory of Error Feedback and Variance Reduction Mechanisms for Biased and Unbiased Compression in Distributed Optimization. NeurIPS (2022)
Haoyu Zhao, Boyue Li, Zhize Li, Peter Richtárik, Yuejie Chi
BEER: Fast O(1/T) Rate for Decentralized Nonconvex Optimization with Communication Compression. CoRR (2022)
Dmitry Kovalev, Alexander V. Gasnikov, Peter Richtárik
Accelerated Primal-Dual Gradient Method for Smooth and Convex-Concave Saddle-Point Problems with Bilinear Coupling. NeurIPS (2022)
Adil Salim, Laurent Condat, Dmitry Kovalev, Peter Richtárik
An Optimal Algorithm for Strongly Convex Minimization under Affine Constraints. AISTATS (2022)
Lukang Sun, Adil Salim, Peter Richtárik
Federated Learning with a Sampling Algorithm under Isoperimetry. CoRR (2022)
Adil Salim, Lukang Sun, Peter Richtárik
A Convergence Theory for SVGD in the Population Limit under Talagrand's Inequality T1. ICML (2022)
Abdurakhmon Sadiev, Dmitry Kovalev, Peter Richtárik
Communication Acceleration of Local Gradient Methods via an Accelerated Primal-Dual Algorithm with an Inexact Prox. NeurIPS (2022)
Xun Qian, Rustem Islamov, Mher Safaryan, Peter Richtárik
Basis Matters: Better Communication-Efficient Second Order Methods for Federated Learning. AISTATS (2022)
Michal Grudzien, Grigory Malinovsky, Peter Richtárik
Generation Local Training Methods Support Client Sampling? Yes! CoRR (2022)
Dmitry Kovalev, Aleksandr Beznosikov, Abdurakhmon Sadiev, Michael Persiianov, Peter Richtárik, Alexander V. Gasnikov
Optimal Algorithms for Decentralized Stochastic Variational Inequalities. NeurIPS (2022)
Samuel Horváth, Maziar Sanjabi, Lin Xiao, Peter Richtárik, Michael Rabbat
FedShuffle: Recipes for Better Use of Local Work in Federated Learning. CoRR (2022)
Samuel Horváth, Konstantin Mishchenko, Peter Richtárik
Adaptive Learning Rates for Faster Stochastic Gradient Methods. CoRR (2022)
Konstantin Burlachenko, Samuel Horváth, Peter Richtárik
FL_PyTorch: optimization research simulator for federated learning. CoRR (2022)
Alexander Tyurin, Peter Richtárik
DASHA: Distributed Nonconvex Optimization with Communication Compression, Optimal Oracle Complexity, and No Client Synchronization. CoRR (2022)
Eduard Gorbunov, Samuel Horváth, Peter Richtárik, Gauthier Gidel
Variance Reduction is an Antidote to Byzantines: Better Rates, Weaker Assumptions and Communication Compression as a Cherry on the Top. CoRR (2022)
Kaja Gruntkowska, Alexander Tyurin, Peter Richtárik
EF21-P and Friends: Improved Theoretical Communication Complexity for Distributed Optimization with Bidirectional Compression. CoRR (2022)
Konstantin Mishchenko, Grigory Malinovsky, Sebastian Stich, Peter Richtárik
ProxSkip: Yes! Local Gradient Steps Provably Lead to Communication Acceleration! Finally! ICML (2022)
Laurent Condat, Ivan Agarský, Peter Richtárik
Provably Doubly Accelerated Federated Learning: The First Theoretically Successful Combination of Local Training and Compressed Communication. CoRR (2022)
Grigory Malinovsky, Konstantin Mishchenko, Peter Richtárik
Server-Side Stepsizes and Sampling Without Replacement Provably Help in Federated Optimization. CoRR (2022)
Abdurakhmon Sadiev, Grigory Malinovsky, Eduard Gorbunov, Igor Sokolov, Ahmed Khaled, Konstantin Burlachenko, Peter Richtárik
Federated Optimization Algorithms with Random Reshuffling and Gradient Compression. CoRR (2022)
Lukang Sun, Avetik G. Karagulyan, Peter Richtárik
Convergence of Stein Variational Gradient Descent under a Weaker Smoothness Condition. CoRR (2022)
Maksim Makarenko, Elnur Gasanov, Rustem Islamov, Abdurakhmon Sadiev, Peter Richtárik
Adaptive Compression for Communication-Efficient Distributed Training. CoRR (2022)
Lukang Sun, Peter Richtárik
Improved Stein Variational Gradient Descent with Importance Weights. CoRR (2022)
Mher Safaryan, Rustem Islamov, Xun Qian, Peter Richtárik
FedNL: Making Newton-Type Methods Applicable to Federated Learning. ICML (2022)
Lukang Sun, Peter Richtárik
)-Smoothness Condition. CoRR (2022)
Samuel Horváth, Lihua Lei, Peter Richtárik, Michael I. Jordan
Adaptivity of Stochastic Gradient Methods for Nonconvex Optimization. SIAM J. Math. Data Sci. 4 (2) (2022)
Dmitry Kovalev, Aleksandr Beznosikov, Abdurakhmon Sadiev, Michael Persiianov, Peter Richtárik, Alexander V. Gasnikov
Optimal Algorithms for Decentralized Stochastic Variational Inequalities. CoRR (2022)
Peter Richtárik, Igor Sokolov, Elnur Gasanov, Ilyas Fatkhullin, Zhize Li, Eduard Gorbunov
3PC: Three Point Compressors for Communication-Efficient Distributed Training and a Better Theory for Lazy Aggregation. ICML (2022)

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Publication Activity (10 Years)

Top Topics

Top Venues

Peter Richtárik