Stop the war!Остановите войну!
for scientists:
default search action
Google
Google Scholar
Semantic Scholar
Internet Archive Scholar
CiteSeerX
ORCIDPeter Richtárik
Person information
- affiliation: King Abdullah University of Science and Technology (KAUST), Thuwal, Saudi Arabia
- affiliation (former): University of Edinburgh, UK
- affiliation (former): Moscow Institute of Physics and Technology (MIPT), Dolgoprudny, Russia
- affiliation (former, PhD 2007): Cornell University, Ithaca, NY, USA
Refine list
refinements active!
zoomed in on ?? of ?? records
view refined list in
export refined list as
showing all ?? records
2020 – today
- 2023
[j46]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): 91-106 (2023)- [j45]Ahmed Khaled, Peter Richtárik:
Better Theory for SGD in the Nonconvex World. Trans. Mach. Learn. Res. 2023 (2023) - [j44]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) - [c93]Xun Qian, Hanze Dong, Tong Zhang, Peter Richtárik:
Catalyst Acceleration of Error Compensated Methods Leads to Better Communication Complexity. AISTATS 2023: 615-649 - [c92]Michal Grudzien, Grigory Malinovsky, Peter Richtárik:
Can 5th Generation Local Training Methods Support Client Sampling? Yes! AISTATS 2023: 1055-1092 - [c91]Lukang Sun, Avetik G. Karagulyan, Peter Richtárik:
Convergence of Stein Variational Gradient Descent under a Weaker Smoothness Condition. AISTATS 2023: 3693-3717 - [c90]Laurent Condat, Peter Richtárik:
RandProx: Primal-Dual Optimization Algorithms with Randomized Proximal Updates. ICLR 2023 - [c89]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 - [c88]Alexander Tyurin, Peter Richtárik:
DASHA: Distributed Nonconvex Optimization with Communication Compression and Optimal Oracle Complexity. ICLR 2023 - [c87]Kaja Gruntkowska, Alexander Tyurin, Peter Richtárik:
EF21-P and Friends: Improved Theoretical Communication Complexity for Distributed Optimization with Bidirectional Compression. ICML 2023: 11761-11807 - [c86]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: 29563-29648 - [c85]Grigory Malinovsky, Alibek Sailanbayev, Peter Richtárik:
Random Reshuffling with Variance Reduction: New Analysis and Better Rates. UAI 2023: 1347-1357 - [i171]Konstantin Mishchenko, Slavomír Hanzely, Peter Richtárik:
Convergence of First-Order Algorithms for Meta-Learning with Moreau Envelopes. CoRR abs/2301.06806 (2023) - [i170]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 abs/2302.00999 (2023) - [i169]Grigory Malinovsky, Samuel Horváth, Konstantin Burlachenko, Peter Richtárik:
Federated Learning with Regularized Client Participation. CoRR abs/2302.03662 (2023) - [i168]Laurent Condat, Grigory Malinovsky, Peter Richtárik:
TAMUNA: Accelerated Federated Learning with Local Training and Partial Participation. CoRR abs/2302.09832 (2023) - [i167]Avetik G. Karagulyan, Peter Richtárik:
ELF: Federated Langevin Algorithms with Primal, Dual and Bidirectional Compression. CoRR abs/2303.04622 (2023) - [i166]Kai Yi, Laurent Condat, Peter Richtárik:
Explicit Personalization and Local Training: Double Communication Acceleration in Federated Learning. CoRR abs/2305.13170 (2023) - [i165]Ilyas Fatkhullin, Alexander Tyurin, Peter Richtárik:
Momentum Provably Improves Error Feedback! CoRR abs/2305.15155 (2023) - [i164]Peter Richtárik, Elnur Gasanov, Konstantin Burlachenko:
Error Feedback Shines when Features are Rare. CoRR abs/2305.15264 (2023) - [i163]Yury Demidovich, Grigory Malinovsky, Igor Sokolov, Peter Richtárik:
A Guide Through the Zoo of Biased SGD. CoRR abs/2305.16296 (2023) - [i162]Jihao Xin, Marco Canini, Peter Richtárik, Samuel Horváth:
Global-QSGD: Practical Floatless Quantization for Distributed Learning with Theoretical Guarantees. CoRR abs/2305.18627 (2023) - [i161]Sarit Khirirat, Eduard Gorbunov, Samuel Horváth, Rustem Islamov, Fakhri Karray, Peter Richtárik:
Clip21: Error Feedback for Gradient Clipping. CoRR abs/2305.18929 (2023) - [i160]Michal Grudzien, Grigory Malinovsky, Peter Richtárik:
Improving Accelerated Federated Learning with Compression and Importance Sampling. CoRR abs/2306.03240 (2023) - [i159]Rafal Szlendak, Elnur Gasanov, Peter Richtárik:
Understanding Progressive Training Through the Framework of Randomized Coordinate Descent. CoRR abs/2306.03626 (2023) - [i158]Egor Shulgin, Peter Richtárik:
Towards a Better Theoretical Understanding of Independent Subnetwork Training. CoRR abs/2306.16484 (2023) - 2022
- [j43]Aritra Dutta, El Houcine Bergou, Yunming Xiao, Marco Canini, Peter Richtárik:
Direct nonlinear acceleration. EURO J. Comput. Optim. 10: 100047 (2022) - [j42]Adil Salim, Laurent Condat, Konstantin Mishchenko, Peter Richtárik:
Dualize, Split, Randomize: Toward Fast Nonsmooth Optimization Algorithms. J. Optim. Theory Appl. 195(1): 102-130 (2022) - [j41]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): 1668-1704 (2022) - [j40]Samuel Horváth, Lihua Lei, Peter Richtárik, Michael I. Jordan:
Adaptivity of Stochastic Gradient Methods for Nonconvex Optimization. SIAM J. Math. Data Sci. 4(2): 634-648 (2022) - [j39]Wenlin Chen, Samuel Horváth, Peter Richtárik:
Optimal Client Sampling for Federated Learning. Trans. Mach. Learn. Res. 2022 (2022) - [j38]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) - [c84]Xun Qian, Rustem Islamov, Mher Safaryan, Peter Richtárik:
Basis Matters: Better Communication-Efficient Second Order Methods for Federated Learning. AISTATS 2022: 680-720 - [c83]Adil Salim, Laurent Condat, Dmitry Kovalev, Peter Richtárik:
An Optimal Algorithm for Strongly Convex Minimization under Affine Constraints. AISTATS 2022: 4482-4498 - [c82]Elnur Gasanov, Ahmed Khaled, Samuel Horváth, Peter Richtárik:
FLIX: A Simple and Communication-Efficient Alternative to Local Methods in Federated Learning. AISTATS 2022: 11374-11421 - [c81]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 - [c80]Konstantin Mishchenko, Bokun Wang, Dmitry Kovalev, Peter Richtárik:
IntSGD: Adaptive Floatless Compression of Stochastic Gradients. ICLR 2022 - [c79]Rafal Szlendak, Alexander Tyurin, Peter Richtárik:
Permutation Compressors for Provably Faster Distributed Nonconvex Optimization. ICLR 2022 - [c78]Konstantin Mishchenko, Ahmed Khaled, Peter Richtárik:
Proximal and Federated Random Reshuffling. ICML 2022: 15718-15749 - [c77]Konstantin Mishchenko, Grigory Malinovsky, Sebastian U. Stich, Peter Richtárik:
ProxSkip: Yes! Local Gradient Steps Provably Lead to Communication Acceleration! Finally! ICML 2022: 15750-15769 - [c76]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: 18596-18648 - [c75]Mher Safaryan, Rustem Islamov, Xun Qian, Peter Richtárik:
FedNL: Making Newton-Type Methods Applicable to Federated Learning. ICML 2022: 18959-19010 - [c74]Adil Salim, Lukang Sun, Peter Richtárik:
A Convergence Theory for SVGD in the Population Limit under Talagrand's Inequality T1. ICML 2022: 19139-19152 - [c73]Laurent Condat, Peter Richtárik:
MURANA: A Generic Framework for Stochastic Variance-Reduced Optimization. MSML 2022: 81-96 - [c72]Samuel Horváth, Chen-Yu Ho, Ludovit Horvath, Atal Narayan Sahu, Marco Canini, Peter Richtárik:
Natural Compression for Distributed Deep Learning. MSML 2022: 129-141 - [c71]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 - [c70]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 - [c69]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 - [c68]Dmitry Kovalev, Aleksandr Beznosikov, Abdurakhmon Sadiev, Michael Persiianov, Peter Richtárik, Alexander V. Gasnikov:
Optimal Algorithms for Decentralized Stochastic Variational Inequalities. NeurIPS 2022 - [c67]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 - [c66]Grigory Malinovsky, Kai Yi, Peter Richtárik:
Variance Reduced ProxSkip: Algorithm, Theory and Application to Federated Learning. NeurIPS 2022 - [c65]Abdurakhmon Sadiev, Dmitry Kovalev, Peter Richtárik:
Communication Acceleration of Local Gradient Methods via an Accelerated Primal-Dual Algorithm with an Inexact Prox. NeurIPS 2022 - [c64]Bokun Wang, Mher Safaryan, Peter Richtárik:
Theoretically Better and Numerically Faster Distributed Optimization with Smoothness-Aware Quantization Techniques. NeurIPS 2022 - [c63]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 - [c62]Egor Shulgin, Peter Richtárik:
Shifted compression framework: generalizations and improvements. UAI 2022: 1813-1823 - [i157]Grigory Malinovsky, Konstantin Mishchenko, Peter Richtárik:
Server-Side Stepsizes and Sampling Without Replacement Provably Help in Federated Optimization. CoRR abs/2201.11066 (2022) - [i156]Haoyu Zhao, Boyue Li, Zhize Li, Peter Richtárik, Yuejie Chi:
BEER: Fast O(1/T) Rate for Decentralized Nonconvex Optimization with Communication Compression. CoRR abs/2201.13320 (2022) - [i155]Peter Richtárik, Igor Sokolov, Ilyas Fatkhullin, Elnur Gasanov, Zhize Li, Eduard Gorbunov:
3PC: Three Point Compressors for Communication-Efficient Distributed Training and a Better Theory for Lazy Aggregation. CoRR abs/2202.00998 (2022) - [i154]Alexander Tyurin, Peter Richtárik:
DASHA: Distributed Nonconvex Optimization with Communication Compression, Optimal Oracle Complexity, and No Client Synchronization. CoRR abs/2202.01268 (2022) - [i153]Dmitry Kovalev, Aleksandr Beznosikov, Abdurakhmon Sadiev, Michael Persiianov, Peter Richtárik, Alexander V. Gasnikov:
Optimal Algorithms for Decentralized Stochastic Variational Inequalities. CoRR abs/2202.02771 (2022) - [i152]Konstantin Burlachenko, Samuel Horváth, Peter Richtárik:
FL_PyTorch: optimization research simulator for federated learning. CoRR abs/2202.03099 (2022) - [i151]Konstantin Mishchenko, Grigory Malinovsky, Sebastian U. Stich, Peter Richtárik:
ProxSkip: Yes! Local Gradient Steps Provably Lead to Communication Acceleration! Finally! CoRR abs/2202.09357 (2022) - [i150]Samuel Horváth, Maziar Sanjabi, Lin Xiao, Peter Richtárik, Michael G. Rabbat:
FedShuffle: Recipes for Better Use of Local Work in Federated Learning. CoRR abs/2204.13169 (2022) - [i149]Grigory Malinovsky, Peter Richtárik:
Federated Random Reshuffling with Compression and Variance Reduction. CoRR abs/2205.03914 (2022) - [i148]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 abs/2205.04180 (2022) - [i147]Alexander Tyurin, Peter Richtárik:
A Computation and Communication Efficient Method for Distributed Nonconvex Problems in the Partial Participation Setting. CoRR abs/2205.15580 (2022) - [i146]Lukang Sun, Avetik G. Karagulyan, Peter Richtárik:
Convergence of Stein Variational Gradient Descent under a Weaker Smoothness Condition. CoRR abs/2206.00508 (2022) - [i145]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 abs/2206.00529 (2022) - [i144]Lukang Sun, Adil Salim, Peter Richtárik:
Federated Learning with a Sampling Algorithm under Isoperimetry. CoRR abs/2206.00920 (2022) - [i143]Alexander Tyurin, Lukang Sun, Konstantin Burlachenko, Peter Richtárik:
Sharper Rates and Flexible Framework for Nonconvex SGD with Client and Data Sampling. CoRR abs/2206.02275 (2022) - [i142]Motasem Alfarra, Juan C. Pérez, Egor Shulgin, Peter Richtárik, Bernard Ghanem:
Certified Robustness in Federated Learning. CoRR abs/2206.02535 (2022) - [i141]Rustem Islamov, Xun Qian, Slavomír Hanzely, Mher Safaryan, Peter Richtárik:
Distributed Newton-Type Methods with Communication Compression and Bernoulli Aggregation. CoRR abs/2206.03588 (2022) - [i140]Abdurakhmon Sadiev, Grigory Malinovsky, Eduard Gorbunov, Igor Sokolov, Ahmed Khaled, Konstantin Burlachenko, Peter Richtárik:
Federated Optimization Algorithms with Random Reshuffling and Gradient Compression. CoRR abs/2206.07021 (2022) - [i139]Lukang Sun, Peter Richtárik:
A Note on the Convergence of Mirrored Stein Variational Gradient Descent under (L0, L1)-Smoothness Condition. CoRR abs/2206.09709 (2022) - [i138]Egor Shulgin, Peter Richtárik:
Shifted Compression Framework: Generalizations and Improvements. CoRR abs/2206.10452 (2022) - [i137]Abdurakhmon Sadiev, Dmitry Kovalev, Peter Richtárik:
Communication Acceleration of Local Gradient Methods via an Accelerated Primal-Dual Algorithm with Inexact Prox. CoRR abs/2207.03957 (2022) - [i136]Grigory Malinovsky, Kai Yi, Peter Richtárik:
Variance Reduced ProxSkip: Algorithm, Theory and Application to Federated Learning. CoRR abs/2207.04338 (2022) - [i135]Samuel Horváth, Konstantin Mishchenko, Peter Richtárik:
Adaptive Learning Rates for Faster Stochastic Gradient Methods. CoRR abs/2208.05287 (2022) - [i134]El Houcine Bergou, Konstantin Burlachenko, Aritra Dutta, Peter Richtárik:
Personalized Federated Learning with Communication Compression. CoRR abs/2209.05148 (2022) - [i133]Soumia Boucherouite, Grigory Malinovsky, Peter Richtárik, El Houcine Bergou:
Minibatch Stochastic Three Points Method for Unconstrained Smooth Minimization. CoRR abs/2209.07883 (2022) - [i132]Kaja Gruntkowska, Alexander Tyurin, Peter Richtárik:
EF21-P and Friends: Improved Theoretical Communication Complexity for Distributed Optimization with Bidirectional Compression. CoRR abs/2209.15218 (2022) - [i131]Lukang Sun, Peter Richtárik:
Improved Stein Variational Gradient Descent with Importance Weights. CoRR abs/2210.00462 (2022) - [i130]Laurent Condat, Ivan Agarský, Peter Richtárik:
Provably Doubly Accelerated Federated Learning: The First Theoretically Successful Combination of Local Training and Compressed Communication. CoRR abs/2210.13277 (2022) - [i129]Artavazd Maranjyan, Mher Safaryan, Peter Richtárik:
GradSkip: Communication-Accelerated Local Gradient Methods with Better Computational Complexity. CoRR abs/2210.16402 (2022) - [i128]Maksim Makarenko, Elnur Gasanov, Rustem Islamov, Abdurakhmon Sadiev, Peter Richtárik:
Adaptive Compression for Communication-Efficient Distributed Training. CoRR abs/2211.00188 (2022) - [i127]Michal Grudzien, Grigory Malinovsky, Peter Richtárik:
Can 5th} Generation Local Training Methods Support Client Sampling? Yes! CoRR abs/2212.14370 (2022) - 2021
- [j37]Filip Hanzely, Peter Richtárik, Lin Xiao:
Accelerated Bregman proximal gradient methods for relatively smooth convex optimization. Comput. Optim. Appl. 79(2): 405-440 (2021) - [j36]Filip Hanzely, Peter Richtárik:
Fastest rates for stochastic mirror descent methods. Comput. Optim. Appl. 79(3): 717-766 (2021) - [j35]Xun Qian, Zheng Qu, Peter Richtárik:
L-SVRG and L-Katyusha with Arbitrary Sampling. J. Mach. Learn. Res. 22: 112:1-112:47 (2021) - [j34]Robert M. Gower, Peter Richtárik, Francis R. Bach:
Stochastic quasi-gradient methods: variance reduction via Jacobian sketching. Math. Program. 188(1): 135-192 (2021) - [j33]Nicolas Loizou, Peter Richtárik:
Revisiting Randomized Gossip Algorithms: General Framework, Convergence Rates and Novel Block and Accelerated Protocols. IEEE Trans. Inf. Theory 67(12): 8300-8324 (2021) - [c61]Samuel Horváth, Aaron Klein, Peter Richtárik, Cédric Archambeau:
Hyperparameter Transfer Learning with Adaptive Complexity. AISTATS 2021: 1378-1386 - [c60]Eduard Gorbunov, Filip Hanzely, Peter Richtárik:
Local SGD: Unified Theory and New Efficient Methods. AISTATS 2021: 3556-3564 - [c59]Dmitry Kovalev, Anastasia Koloskova, Martin Jaggi, Peter Richtárik, Sebastian U. Stich:
A Linearly Convergent Algorithm for Decentralized Optimization: Sending Less Bits for Free! AISTATS 2021: 4087-4095 - [c58]Konstantin Burlachenko, Samuel Horváth, Peter Richtárik:
FL_PyTorch: optimization research simulator for federated learning. DistributedML@CoNEXT 2021: 1-7 - [c57]Samuel Horváth, Peter Richtárik:
A Better Alternative to Error Feedback for Communication-Efficient Distributed Learning. ICLR 2021 - [c56]Eduard Gorbunov, Konstantin Burlachenko, Zhize Li, Peter Richtárik:
MARINA: Faster Non-Convex Distributed Learning with Compression. ICML 2021: 3788-3798 - [c55]Rustem Islamov, Xun Qian, Peter Richtárik:
Distributed Second Order Methods with Fast Rates and Compressed Communication. ICML 2021: 4617-4628 - [c54]Dmitry Kovalev, Egor Shulgin, Peter Richtárik, Alexander Rogozin, Alexander V. Gasnikov:
ADOM: Accelerated Decentralized Optimization Method for Time-Varying Networks. ICML 2021: 5784-5793 - [c53]Zhize Li, Hongyan Bao, Xiangliang Zhang, Peter Richtárik:
PAGE: A Simple and Optimal Probabilistic Gradient Estimator for Nonconvex Optimization. ICML 2021: 6286-6295 - [c52]Mher Safaryan, Peter Richtárik:
Stochastic Sign Descent Methods: New Algorithms and Better Theory. ICML 2021: 9224-9234 - [c51]Peter Richtárik, Igor Sokolov, Ilyas Fatkhullin:
EF21: A New, Simpler, Theoretically Better, and Practically Faster Error Feedback. NeurIPS 2021: 4384-4396 - [c50]Zhize Li, Peter Richtárik:
CANITA: Faster Rates for Distributed Convex Optimization with Communication Compression. NeurIPS 2021: 13770-13781 - [c49]Dmitry Kovalev, Elnur Gasanov, Alexander V. Gasnikov, Peter Richtárik:
Lower Bounds and Optimal Algorithms for Smooth and Strongly Convex Decentralized Optimization Over Time-Varying Networks. NeurIPS 2021: 22325-22335 - [c48]Mher Safaryan, Filip Hanzely, Peter Richtárik:
Smoothness Matrices Beat Smoothness Constants: Better Communication Compression Techniques for Distributed Optimization. NeurIPS 2021: 25688-25702 - [c47]Xun Qian, Peter Richtárik, Tong Zhang:
Error Compensated Distributed SGD Can Be Accelerated. NeurIPS 2021: 30401-30413 - [c46]Amedeo Sapio, Marco Canini, Chen-Yu Ho, Jacob Nelson, Panos Kalnis, Changhoon Kim, Arvind Krishnamurthy, Masoud Moshref, Dan R. K. Ports, Peter Richtárik:
Scaling Distributed Machine Learning with In-Network Aggregation. NSDI 2021: 785-808 - [i126]Konstantin Mishchenko, Ahmed Khaled, Peter Richtárik:
Proximal and Federated Random Reshuffling. CoRR abs/2102.06704 (2021) - [i125]Rustem Islamov, Xun Qian, Peter Richtárik:
Distributed Second Order Methods with Fast Rates and Compressed Communication. CoRR abs/2102.07158 (2021) - [i124]Mher Safaryan, Filip Hanzely, Peter Richtárik:
Smoothness Matrices Beat Smoothness Constants: Better Communication Compression Techniques for Distributed Optimization. CoRR abs/2102.07245 (2021) - [i123]Eduard Gorbunov, Konstantin Burlachenko, Zhize Li, Peter Richtárik:
MARINA: Faster Non-Convex Distributed Learning with Compression. CoRR abs/2102.07845 (2021) - [i122]Konstantin Mishchenko, Bokun Wang, Dmitry Kovalev, Peter Richtárik:
IntSGD: Floatless Compression of Stochastic Gradients. CoRR abs/2102.08374 (2021) - [i121]Dmitry Kovalev, Egor Shulgin, Peter Richtárik, Alexander Rogozin, Alexander V. Gasnikov:
ADOM: Accelerated Decentralized Optimization Method for Time-Varying Networks. CoRR abs/2102.09234 (2021) - [i120]Zheng Shi, Nicolas Loizou, Peter Richtárik, Martin Takác:
AI-SARAH: Adaptive and Implicit Stochastic Recursive Gradient Methods. CoRR abs/2102.09700 (2021) - [i119]Samuel Horváth, Aaron Klein, Peter Richtárik, Cédric Archambeau:
Hyperparameter Transfer Learning with Adaptive Complexity. CoRR abs/2102.12810 (2021) - [i118]Zhize Li, Peter Richtárik:
ZeroSARAH: Efficient Nonconvex Finite-Sum Optimization with Zero Full Gradient Computation. CoRR abs/2103.01447 (2021) - [i117]Grigory Malinovsky, Alibek Sailanbayev, Peter Richtárik:
Random Reshuffling with Variance Reduction: New Analysis and Better Rates. CoRR abs/2104.09342 (2021) - [i116]Mher Safaryan, Rustem Islamov, Xun Qian, Peter Richtárik:
FedNL: Making Newton-Type Methods Applicable to Federated Learning. CoRR abs/2106.02969 (2021) - [i115]Laurent Condat, Peter Richtárik:
MURANA: A Generic Framework for Stochastic Variance-Reduced Optimization. CoRR abs/2106.03056 (2021) - [i114]