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2020 – today
- 2022
[i136]Grigory Malinovsky, Konstantin Mishchenko, Peter Richtárik:
Server-Side Stepsizes and Sampling Without Replacement Provably Help in Federated Optimization. CoRR abs/2201.11066 (2022)- [i135]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) - [i134]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) - [i133]Alexander Tyurin, Peter Richtárik:
DASHA: Distributed Nonconvex Optimization with Communication Compression, Optimal Oracle Complexity, and No Client Synchronization. CoRR abs/2202.01268 (2022) - [i132]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) - [i131]Konstantin Burlachenko, Samuel Horváth
, Peter Richtárik:
FL_PyTorch: optimization research simulator for federated learning. CoRR abs/2202.03099 (2022) - [i130]Konstantin Mishchenko, Grigory Malinovsky, Sebastian Stich, Peter Richtárik:
ProxSkip: Yes! Local Gradient Steps Provably Lead to Communication Acceleration! Finally! CoRR abs/2202.09357 (2022) - [i129]Samuel Horváth, Maziar Sanjabi, Lin Xiao, Peter Richtárik, Michael Rabbat:
FedShuffle: Recipes for Better Use of Local Work in Federated Learning. CoRR abs/2204.13169 (2022) - [i128]Grigory Malinovsky, Peter Richtárik:
Federated Random Reshuffling with Compression and Variance Reduction. CoRR abs/2205.03914 (2022) - [i127]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) - 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]Adil Salim, Lukang Sun, Peter Richtárik:
Complexity Analysis of Stein Variational Gradient Descent Under Talagrand's Inequality T1. CoRR abs/2106.03076 (2021) - [i113]Bokun Wang, Mher Safaryan, Peter Richtárik:
Smoothness-Aware Quantization Techniques. CoRR abs/2106.03524 (2021) - [i112]Dmitry Kovalev, Elnur Gasanov, Peter Richtárik, Alexander V. Gasnikov:
Lower Bounds and Optimal Algorithms for Smooth and Strongly Convex Decentralized Optimization Over Time-Varying Networks. CoRR abs/2106.04469 (2021) - [i111]Peter Richtárik, Igor Sokolov, Ilyas Fatkhullin:
EF21: A New, Simpler, Theoretically Better, and Practically Faster Error Feedback. CoRR abs/2106.05203 (2021) - [i110]Jianyu Wang, Zachary Charles, Zheng Xu, Gauri Joshi, H. Brendan McMahan, Blaise Agüera y Arcas, Maruan Al-Shedivat, Galen Andrew, Salman Avestimehr, Katharine Daly, Deepesh Data, Suhas N. Diggavi, Hubert Eichner, Advait Gadhikar, Zachary Garrett, Antonious M. Girgis, Filip Hanzely, Andrew Hard, Chaoyang He, Samuel Horváth, Zhouyuan Huo, Alex Ingerman, Martin Jaggi, Tara Javidi, Peter Kairouz, Satyen Kale, Sai Praneeth Karimireddy, Jakub Konecný, Sanmi Koyejo, Tian Li, Luyang Liu, Mehryar Mohri, Hang Qi, Sashank J. Reddi, Peter Richtárik, Karan Singhal, Virginia Smith, Mahdi Soltanolkotabi, Weikang Song, Ananda Theertha Suresh, Sebastian U. Stich, Ameet Talwalkar, Hongyi Wang, Blake E. Woodworth, Shanshan Wu, Felix X. Yu, Honglin Yuan, Manzil Zaheer, Mi Zhang, Tong Zhang, Chunxiang Zheng, Chen Zhu, Wennan Zhu:
A Field Guide to Federated Optimization. CoRR abs/2107.06917 (2021) - [i109]Zhize Li, Peter Richtárik:
CANITA: Faster Rates for Distributed Convex Optimization with Communication Compression. CoRR abs/2107.09461 (2021) - [i108]Haoyu Zhao, Zhize Li, Peter Richtárik:
FedPAGE: A Fast Local Stochastic Gradient Method for Communication-Efficient Federated Learning. CoRR abs/2108.04755 (2021) - [i107]Majid Jahani, Sergey Rusakov, Zheng Shi, Peter Richtárik, Michael W. Mahoney, Martin Takác:
Doubly Adaptive Scaled Algorithm for Machine Learning Using Second-Order Information. CoRR abs/2109.05198 (2021) - [i106]Ilyas Fatkhullin, Igor Sokolov, Eduard Gorbunov, Zhize Li, Peter Richtárik:
EF21 with Bells & Whistles: Practical Algorithmic Extensions of Modern Error Feedback. CoRR abs/2110.03294 (2021) - [i105]Rafal Szlendak, Alexander Tyurin, Peter Richtárik:
Permutation Compressors for Provably Faster Distributed Nonconvex Optimization. CoRR abs/2110.03300 (2021) - [i104]Aleksandr Beznosikov, Peter Richtárik, Michael Diskin, Max Ryabinin, Alexander V. Gasnikov:
Distributed Methods with Compressed Communication for Solving Variational Inequalities, with Theoretical Guarantees. CoRR abs/2110.03313 (2021) - [i103]Xun Qian, Rustem Islamov, Mher Safaryan, Peter Richtárik:
Basis Matters: Better Communication-Efficient Second Order Methods for Federated Learning. CoRR abs/2111.01847 (2021) - [i102]Elnur Gasanov, Ahmed Khaled, Samuel Horváth, Peter Richtárik:
FLIX: A Simple and Communication-Efficient Alternative to Local Methods in Federated Learning. CoRR abs/2111.11556 (2021) - [i101]Haoyu Zhao, Konstantin Burlachenko, Zhize Li, Peter Richtárik:
Faster Rates for Compressed Federated Learning with Client-Variance Reduction. CoRR abs/2112.13097 (2021) - [i100]Dmitry Kovalev, Alexander V. Gasnikov, Peter Richtárik:
Accelerated Primal-Dual Gradient Method for Smooth and Convex-Concave Saddle-Point Problems with Bilinear Coupling. CoRR abs/2112.15199 (2021) - 2020
- [j32]Nicolas Loizou, Peter Richtárik:
Momentum and stochastic momentum for stochastic gradient, Newton, proximal point and subspace descent methods. Comput. Optim. Appl. 77(3): 653-710 (2020) - [j31]Robert M. Gower, Mark Schmidt, Francis R. Bach, Peter Richtárik:
Variance-Reduced Methods for Machine Learning. Proc. IEEE 108(11): 1968-1983 (2020) - [j30]El Houcine Bergou, Eduard Gorbunov, Peter Richtárik:
Stochastic Three Points Method for Unconstrained Smooth Minimization. SIAM J. Optim. 30(4): 2726-2749 (2020) - [j29]Peter Richtárik, Martin Takác:
Stochastic Reformulations of Linear Systems: Algorithms and Convergence Theory. SIAM J. Matrix Anal. Appl. 41(2): 487-524 (2020) - [j28]Nicolas Loizou, Peter Richtárik:
Convergence Analysis of Inexact Randomized Iterative Methods. SIAM J. Sci. Comput. 42(6): A3979-A4016 (2020) - [j27]Aritra Dutta, Filip Hanzely, Jingwei Liang, Peter Richtárik:
Best Pair Formulation & Accelerated Scheme for Non-Convex Principal Component Pursuit. IEEE Trans. Signal Process. 68: 6128-6141 (2020) - [c45]Adel Bibi, El Houcine Bergou, Ozan Sener, Bernard Ghanem, Peter Richtárik:
A Stochastic Derivative-Free Optimization Method with Importance Sampling: Theory and Learning to Control. AAAI 2020: 3275-3282 - [c44]Eduard Gorbunov, Filip Hanzely, Peter Richtárik:
A Unified Theory of SGD: Variance Reduction, Sampling, Quantization and Coordinate Descent. AISTATS 2020: 680-690 - [c43]Ahmed Khaled, Konstantin Mishchenko, Peter Richtárik:
Tighter Theory for Local SGD on Identical and Heterogeneous Data. AISTATS 2020: 4519-4529 - [c42]Konstantin Mishchenko, Dmitry Kovalev, Egor Shulgin, Peter Richtárik, Yura Malitsky:
Revisiting Stochastic Extragradient. AISTATS 2020: 4573-4582 - [c41]Dmitry Kovalev, Samuel Horváth, Peter Richtárik:
Don't Jump Through Hoops and Remove Those Loops: SVRG and Katyusha are Better Without the Outer Loop. ALT 2020: 451-467 - [c40]Eduard Gorbunov, Adel Bibi, Ozan Sener, El Houcine Bergou, Peter Richtárik:
A Stochastic Derivative Free Optimization Method with Momentum. ICLR 2020 - [c39]Filip Hanzely, Nikita Doikov, Yurii E. Nesterov, Peter Richtárik:
Stochastic Subspace Cubic Newton Method. ICML 2020: 4027-4038 - [c38]Filip Hanzely, Dmitry Kovalev, Peter Richtárik:
Variance Reduced Coordinate Descent with Acceleration: New Method With a Surprising Application to Finite-Sum Problems. ICML 2020: 4039-4048 - [c37]Zhize Li, Dmitry Kovalev, Xun Qian, Peter Richtárik:
Acceleration for Compressed Gradient Descent in Distributed and Federated Optimization. ICML 2020: 5895-5904 - [c36]Grigory Malinovskiy, Dmitry Kovalev, Elnur Gasanov, Laurent Condat, Peter Richtárik:
From Local SGD to Local Fixed-Point Methods for Federated Learning. ICML 2020: 6692-6701 - [c35]Eduard Gorbunov, Dmitry Kovalev, Dmitry Makarenko, Peter Richtárik:
Linearly Converging Error Compensated SGD. NeurIPS 2020 - [c34]Filip Hanzely, Slavomír Hanzely, Samuel Horváth, Peter Richtárik:
Lower Bounds and Optimal Algorithms for Personalized Federated Learning. NeurIPS 2020 - [c33]Dmitry Kovalev, Adil Salim, Peter Richtárik:
Optimal and Practical Algorithms for Smooth and Strongly Convex Decentralized Optimization. NeurIPS 2020 - [c32]Konstantin Mishchenko, Ahmed Khaled, Peter Richtárik:
Random Reshuffling: Simple Analysis with Vast Improvements. NeurIPS 2020 - [c31]Adil Salim, Peter Richtárik:
Primal Dual Interpretation of the Proximal Stochastic Gradient Langevin Algorithm. NeurIPS 2020 - [c30]Konstantin Mishchenko, Filip Hanzely, Peter Richtárik:
99% of Worker-Master Communication in Distributed Optimization Is Not Needed. UAI 2020: 979-988 - [i99]Ahmed Khaled, Peter Richtárik:
Better Theory for SGD in the Nonconvex World. CoRR abs/2002.03329 (2020) - [i98]Filip Hanzely, Dmitry Kovalev, Peter Richtárik:
Variance Reduced Coordinate Descent with Acceleration: New Method With a Surprising Application to Finite-Sum Problems. CoRR abs/2002.04670 (2020) - [i97]Samuel Horváth, Lihua Lei, Peter Richtárik, Michael I. Jordan:
Adaptivity of Stochastic Gradient Methods for Nonconvex Optimization. CoRR abs/2002.05359 (2020) - [i96]Filip Hanzely, Peter Richtárik:
Federated Learning of a Mixture of Global and Local Models. CoRR abs/2002.05516 (2020) - [i95]Mher Safaryan, Egor Shulgin, Peter Richtárik:
Uncertainty Principle for Communication Compression in Distributed and Federated Learning and the Search for an Optimal Compressor. CoRR abs/2002.08958 (2020) - [i94]Filip Hanzely, Nikita Doikov, Peter Richtárik, Yurii E. Nesterov:
Stochastic Subspace Cubic Newton Method. CoRR abs/2002.09526 (2020) - [i93]Dmitry Kovalev, Robert M. Gower, Peter Richtárik, Alexander Rogozin:
Fast Linear Convergence of Randomized BFGS. CoRR abs/2002.11337 (2020) - [i92]Zhize Li, Dmitry Kovalev, Xun Qian, Peter Richtárik:
Acceleration for Compressed Gradient Descent in Distributed and Federated Optimization. CoRR abs/2002.11364 (2020) - [i91]Aleksandr Beznosikov, Samuel Horváth, Peter Richtárik, Mher Safaryan:
On Biased Compression for Distributed Learning. CoRR abs/2002.12410 (2020) - [i90]Grigory Malinovsky, Dmitry Kovalev, Elnur Gasanov, Laurent Condat, Peter Richtárik:
From Local SGD to Local Fixed Point Methods for Federated Learning. CoRR abs/2004.01442 (2020) - [i89]Atal Narayan Sahu, Aritra Dutta, Aashutosh Tiwari, Peter Richtárik:
On the Convergence Analysis of Asynchronous SGD for Solving Consistent Linear Systems. CoRR abs/2004.02163 (2020) - [i88]Adil Salim, Laurent Condat, Konstantin Mishchenko, Peter Richtárik:
Dualize, Split, Randomize: Fast Nonsmooth Optimization Algorithms. CoRR abs/2004.02635 (2020) - [i87]Motasem Alfarra, Slavomír Hanzely, Alyazeed Albasyoni, Bernard Ghanem, Peter Richtárik:
Adaptive Learning of the Optimal Mini-Batch Size of SGD. CoRR abs/2005.01097 (2020) - [i86]Konstantin Mishchenko, Ahmed Khaled, Peter Richtárik:
Random Reshuffling: Simple Analysis with Vast Improvements. CoRR abs/2006.05988 (2020) - [i85]Zhize Li, Peter Richtárik:
A Unified Analysis of Stochastic Gradient Methods for Nonconvex Federated Optimization. CoRR abs/2006.07013 (2020) - [i84]Adil Salim, Peter Richtárik:
Primal Dual Interpretation of the Proximal Stochastic Gradient Langevin Algorithm. CoRR abs/2006.09270 (2020) - [i83]Samuel Horváth, Peter Richtárik:
A Better Alternative to Error Feedback for Communication-Efficient Distributed Learning. CoRR abs/2006.11077 (2020) - [i82]Ahmed Khaled, Othmane Sebbouh, Nicolas Loizou, Robert M. Gower, Peter Richtárik:
Unified Analysis of Stochastic Gradient Methods for Composite Convex and Smooth Optimization. CoRR abs/2006.11573 (2020) - [i81]Zhize Li, Hongyan Bao, Xiangliang Zhang, Peter Richtárik:
PAGE: A Simple and Optimal Probabilistic Gradient Estimator for Nonconvex Optimization. CoRR abs/2008.10898 (2020) - [i80]Robert M. Gower, Mark Schmidt, Francis R. Bach, Peter Richtárik:
Variance-Reduced Methods for Machine Learning. CoRR abs/2010.00892 (2020) - [i79]Laurent Condat, Grigory Malinovsky, Peter Richtárik:
Distributed Proximal Splitting Algorithms with Rates and Acceleration. CoRR abs/2010.00952 (2020) - [i78]Filip Hanzely, Slavomír Hanzely, Samuel Horváth, Peter Richtárik:
Lower Bounds and Optimal Algorithms for Personalized Federated Learning. CoRR abs/2010.02372 (2020) - [i77]Alyazeed Albasyoni, Mher Safaryan, Laurent Condat, Peter Richtárik:
Optimal Gradient Compression for Distributed and Federated Learning. CoRR abs/2010.03246 (2020) - [i76]Eduard Gorbunov, Dmitry Kovalev, Dmitry Makarenko, Peter Richtárik:
Linearly Converging Error Compensated SGD. CoRR abs/2010.12292 (2020) - [i75]Wenlin Chen, Samuel Horváth, Peter Richtárik:
Optimal Client Sampling for Federated Learning. CoRR abs/2010.13723 (2020) - [i74]Dmitry Kovalev, Anastasia Koloskova, Martin Jaggi, Peter Richtárik, Sebastian U. Stich:
A Linearly Convergent Algorithm for Decentralized Optimization: Sending Less Bits for Free! CoRR abs/2011.01697 (2020) - [i73]Eduard Gorbunov, Filip Hanzely, Peter Richtárik:
Local SGD: Unified Theory and New Efficient Methods. CoRR abs/2011.02828 (2020)
2010 – 2019
- 2019
- [j26]Lam M. Nguyen, Phuong Ha Nguyen, Peter Richtárik, Katya Scheinberg, Martin Takác, Marten van Dijk:
New Convergence Aspects of Stochastic Gradient Algorithms. J. Mach. Learn. Res. 20: 176:1-176:49 (2019) - [j25]Ion Necoara, Peter Richtárik, Andrei Patrascu:
Randomized Projection Methods for Convex Feasibility: Conditioning and Convergence Rates. SIAM J. Optim. 29(4): 2814-2852 (2019) - [c29]Aritra Dutta, Filip Hanzely, Peter Richtárik:
A Nonconvex Projection Method for Robust PCA. AAAI 2019: 1468-1476 - [c28]Filip Hanzely, Peter Richtárik:
Accelerated Coordinate Descent with Arbitrary Sampling and Best Rates for Minibatches. AISTATS 2019: 304-312 - [c27]Nicolas Loizou, Michael G. Rabbat, Peter Richtárik:
Provably Accelerated Randomized Gossip Algorithms. ICASSP 2019: 7505-7509 - [c26]Samuel Horváth, Peter Richtárik:
Nonconvex Variance Reduced Optimization with Arbitrary Sampling. ICML 2019: 2781-2789 - [c25]Xun Qian, Zheng Qu, Peter Richtárik:
SAGA with Arbitrary Sampling. ICML 2019: 5190-5199 - [c24]Xun Qian, Peter Richtárik, Robert M. Gower, Alibek Sailanbayev, Nicolas Loizou, Egor Shulgin:
SGD with Arbitrary Sampling: General Analysis and Improved Rates. ICML 2019: 5200-5209 - [c23]Robert M. Gower, Dmitry Kovalev, Felix Lieder, Peter Richtárik:
RSN: Randomized Subspace Newton. NeurIPS 2019: 614-623 - [c22]Adil Salim, Dmitry Kovalev, Peter Richtárik:
Stochastic Proximal Langevin Algorithm: Potential Splitting and Nonasymptotic Rates. NeurIPS 2019: 6649-6661 - [c21]Jinhui Xiong, Peter Richtárik, Wolfgang Heidrich:
Stochastic Convolutional Sparse Coding. VMV 2019: 47-54 - [c20]Aritra Dutta, Peter Richtárik:
Online and Batch Supervised Background Estimation Via L1 Regression. WACV 2019: 541-550 - [i72]Xun Qian, Zheng Qu, Peter Richtárik:
SAGA with Arbitrary Sampling. CoRR abs/1901.08669 (2019) - [i71]Dmitry Kovalev, Samuel Horváth, Peter Richtárik:
Don't Jump Through Hoops and Remove Those Loops: SVRG and Katyusha are Better Without the Outer Loop. CoRR abs/1901.08689 (2019) - [i70]Konstantin Mishchenko, Eduard Gorbunov, Martin Takác, Peter Richtárik:
Distributed Learning with Compressed Gradient Differences. CoRR abs/1901.09269 (2019) - [i69]Filip Hanzely, Jakub Konecný, Nicolas Loizou, Peter Richtárik, Dmitry Grishchenko:
A Privacy Preserving Randomized Gossip Algorithm via Controlled Noise Insertion. CoRR abs/1901.09367 (2019) - [i68]Robert Mansel Gower, Nicolas Loizou, Xun Qian, Alibek Sailanbayev, Egor Shulgin, Peter Richtárik:
SGD: General Analysis and Improved Rates. CoRR abs/1901.09401 (2019) - [i67]