Generalized Power Method for Sparse Principal Component Analysis. - Michel Journée; Yurii E. Nesterov; Peter Richtárik; Rodolphe Sepulchre (J. Mach. Learn. Res., 2010)

Efficient Serial and Parallel Coordinate Descent Methods for Huge-Scale Truss Topology Design. - Peter Richtárik; Martin Takác 001 (OR, 2011)

Improved Algorithms for Convex Minimization in Relative Scale. - Peter Richtárik (SIAM J. Optim., 2011)

Optimal diagnostic tests for sporadic Creutzfeldt-Jakob disease based on support vector machine classification of RT-QuIC data - William Hulme; Peter Richtárik; Lynne McGuire; Alison Green (CoRR, 2012)

Alternating Maximization: Unifying Framework for 8 Sparse PCA Formulations and Efficient Parallel Codes - Peter Richtárik; Martin Takác 001; Selin Damla Ahipasaoglu (CoRR, 2012)

Approximate Level Method for Nonsmooth Convex Minimization. - Peter Richtárik (J. Optim. Theory Appl., 2012)

Mini-Batch Primal and Dual Methods for SVMs. - Martin Takác 001; Avleen Singh Bijral; Peter Richtárik; Nati Srebro (ICML (3), 2013)

Smooth minimization of nonsmooth functions with parallel coordinate descent methods. - Olivier Fercoq; Peter Richtárik (CoRR, 2013)

TOP-SPIN: TOPic discovery via Sparse Principal component INterference. - Martin Takác 001; Selin Damla Ahipasaoglu; Ngai-Man Cheung; Peter Richtárik (CoRR, 2013)

Fast distributed coordinate descent for non-strongly convex losses. - Olivier Fercoq; Zheng Qu 001; Peter Richtárik; Martin Takác 001 (MLSP, 2014)

mS2GD: Mini-Batch Semi-Stochastic Gradient Descent in the Proximal Setting. - Jakub Konecný; Jie Liu 036; Peter Richtárik; Martin Takác 001 (CoRR, 2014)

Simple Complexity Analysis of Direct Search. - Jakub Konecný; Peter Richtárik (CoRR, 2014)

Randomized Dual Coordinate Ascent with Arbitrary Sampling. - Zheng Qu 001; Peter Richtárik; Tong Zhang 001 (CoRR, 2014)

Inequality-Constrained Matrix Completion: Adding the Obvious Helps! - Martin Takác 001; Jakub Marecek; Peter Richtárik (CoRR, 2014)

Iteration complexity of randomized block-coordinate descent methods for minimizing a composite function. - Peter Richtárik; Martin Takác 001 (Math. Program., 2014)

Stochastic Dual Coordinate Ascent with Adaptive Probabilities. - Dominik Csiba; Zheng Qu 001; Peter Richtárik (ICML, 2015)

Adding vs. Averaging in Distributed Primal-Dual Optimization. - Chenxin Ma; Virginia Smith; Martin Jaggi; Michael I. Jordan; Peter Richtárik; Martin Takác 001 (ICML, 2015)

Quartz: Randomized Dual Coordinate Ascent with Arbitrary Sampling. - Zheng Qu 001; Peter Richtárik; Tong Zhang 001 (NIPS, 2015)

Primal Method for ERM with Flexible Mini-batching Schemes and Non-convex Losses. - Dominik Csiba; Peter Richtárik (CoRR, 2015)

Stochastic Dual Ascent for Solving Linear Systems. - Robert Mansel Gower; Peter Richtárik (CoRR, 2015)

Distributed Mini-Batch SDCA. - Martin Takác 001; Peter Richtárik; Nathan Srebro (CoRR, 2015)

Separable approximations and decomposition methods for the augmented Lagrangian. - Rachael Tappenden; Peter Richtárik; Burak Büke (Optim. Methods Softw., 2015)

Accelerated, Parallel, and Proximal Coordinate Descent. - Olivier Fercoq; Peter Richtárik (SIAM J. Optim., 2015)

Randomized Iterative Methods for Linear Systems. - Robert Mansel Gower; Peter Richtárik (SIAM J. Matrix Anal. Appl., 2015)

A new perspective on randomized gossip algorithms. - Nicolas Loizou; Peter Richtárik (GlobalSIP, 2016)

Stochastic Block BFGS: Squeezing More Curvature out of Data. - Robert M. Gower; Donald Goldfarb; Peter Richtárik (ICML, 2016)

SDNA: Stochastic Dual Newton Ascent for Empirical Risk Minimization. - Zheng Qu 001; Peter Richtárik; Martin Takác 001; Olivier Fercoq (ICML, 2016)

Even Faster Accelerated Coordinate Descent Using Non-Uniform Sampling. - Zeyuan Allen Zhu; Zheng Qu 001; Peter Richtárik; Yang Yuan (ICML, 2016)

Federated Optimization: Distributed Machine Learning for On-Device Intelligence. - Jakub Konecný; H. Brendan McMahan; Daniel Ramage; Peter Richtárik (CoRR, 2016)

Federated Learning: Strategies for Improving Communication Efficiency. - Jakub Konecný; H. Brendan McMahan; Felix X. Yu; Peter Richtárik; Ananda Theertha Suresh; Dave Bacon (CoRR, 2016)

AIDE: Fast and Communication Efficient Distributed Optimization. - Sashank J. Reddi; Jakub Konecný; Peter Richtárik; Barnabás Póczos; Alexander J. Smola (CoRR, 2016)

Distributed Coordinate Descent Method for Learning with Big Data. - Peter Richtárik; Martin Takác 001 (J. Mach. Learn. Res., 2016)

Inexact Coordinate Descent: Complexity and Preconditioning. - Rachael Tappenden; Peter Richtárik; Jacek Gondzio (J. Optim. Theory Appl., 2016)

Mini-Batch Semi-Stochastic Gradient Descent in the Proximal Setting. - Jakub Konecný; Jie Liu 036; Peter Richtárik; Martin Takác 001 (IEEE J. Sel. Top. Signal Process., 2016)

Parallel coordinate descent methods for big data optimization. - Peter Richtárik; Martin Takác 001 (Math. Program., 2016)

On optimal probabilities in stochastic coordinate descent methods. - Peter Richtárik; Martin Takác 001 (Optim. Lett., 2016)

Coordinate descent with arbitrary sampling I: algorithms and complexity. - Zheng Qu 001; Peter Richtárik (Optim. Methods Softw., 2016)

Coordinate descent with arbitrary sampling II: expected separable overapproximation. - Zheng Qu 001; Peter Richtárik (Optim. Methods Softw., 2016)

Optimization in High Dimensions via Accelerated, Parallel, and Proximal Coordinate Descent. - Olivier Fercoq; Peter Richtárik (SIAM Rev., 2016)

A Batch-Incremental Video Background Estimation Model Using Weighted Low-Rank Approximation of Matrices. - Xin Li 022; Aritra Dutta; Peter Richtárik (ICCV Workshops, 2017)

Linearly convergent stochastic heavy ball method for minimizing generalization error. - Nicolas Loizou; Peter Richtárik (CoRR, 2017)

Matrix completion under interval uncertainty. - Jakub Marecek; Peter Richtárik; Martin Takác 001 (Eur. J. Oper. Res., 2017)

Semi-Stochastic Gradient Descent Methods. - Jakub Konecný; Peter Richtárik (Frontiers Appl. Math. Stat., 2017)

Semi-stochastic coordinate descent. - Jakub Konecný; Zheng Qu 001; Peter Richtárik (Optim. Methods Softw., 2017)

Distributed optimization with arbitrary local solvers. - Chenxin Ma; Jakub Konecný; Martin Jaggi; Virginia Smith; Michael I. Jordan; Peter Richtárik; Martin Takác 001 (Optim. Methods Softw., 2017)

Randomized Quasi-Newton Updates Are Linearly Convergent Matrix Inversion Algorithms. - Robert M. Gower; Peter Richtárik (SIAM J. Matrix Anal. Appl., 2017)

Accelerated Gossip via Stochastic Heavy Ball Method. - Nicolas Loizou; Peter Richtárik (Allerton, 2018)

Coordinate Descent Faceoff: Primal or Dual? - Dominik Csiba; Peter Richtárik (ALT, 2018)

Randomized Block Cubic Newton Method. - Nikita Doikov; Peter Richtárik (ICML, 2018)

SGD and Hogwild! Convergence Without the Bounded Gradients Assumption. - Lam M. Nguyen; Phuong Ha Nguyen; Marten van Dijk; Peter Richtárik; Katya Scheinberg; Martin Takác 001 (ICML, 2018)

Accelerated Stochastic Matrix Inversion: General Theory and Speeding up BFGS Rules for Faster Second-Order Optimization. - Robert M. Gower; Filip Hanzely; Peter Richtárik; Sebastian U. Stich (NeurIPS, 2018)

SEGA: Variance Reduction via Gradient Sketching. - Filip Hanzely; Konstantin Mishchenko; Peter Richtárik (NeurIPS, 2018)

Stochastic Spectral and Conjugate Descent Methods. - Dmitry Kovalev; Peter Richtárik; Eduard Gorbunov; Elnur Gasanov (NeurIPS, 2018)

Matrix Completion Under Interval Uncertainty: Highlights. - Jakub Marecek; Peter Richtárik; Martin Takác 001 (ECML/PKDD (3), 2018)

Weighted Low-Rank Approximation of Matrices and Background Modeling. - Aritra Dutta; Xin Li 022; Peter Richtárik (CoRR, 2018)

Randomized Distributed Mean Estimation: Accuracy vs. Communication. - Jakub Konecný; Peter Richtárik (Frontiers Appl. Math. Stat., 2018)

Importance Sampling for Minibatches. - Dominik Csiba; Peter Richtárik (J. Mach. Learn. Res., 2018)

On the complexity of parallel coordinate descent. - Rachael Tappenden; Martin Takác 001; Peter Richtárik (Optim. Methods Softw., 2018)

Stochastic Primal-Dual Hybrid Gradient Algorithm with Arbitrary Sampling and Imaging Applications. - Antonin Chambolle; Matthias J. Ehrhardt; Peter Richtárik; Carola-Bibiane Schönlieb (SIAM J. Optim., 2018)

A Nonconvex Projection Method for Robust PCA. - Aritra Dutta; Filip Hanzely; Peter Richtárik (AAAI, 2019)

Accelerated Coordinate Descent with Arbitrary Sampling and Best Rates for Minibatches. - Filip Hanzely; Peter Richtárik (AISTATS, 2019)

Provably Accelerated Randomized Gossip Algorithms. - Nicolas Loizou; Michael G. Rabbat; Peter Richtárik (ICASSP, 2019)

Nonconvex Variance Reduced Optimization with Arbitrary Sampling. - Samuel Horváth; Peter Richtárik (ICML, 2019)

SAGA with Arbitrary Sampling. - Xun Qian; Zheng Qu 001; Peter Richtárik (ICML, 2019)

SGD with Arbitrary Sampling: General Analysis and Improved Rates. - Xun Qian; Peter Richtárik; Robert M. Gower; Alibek Sailanbayev; Nicolas Loizou; Egor Shulgin (ICML, 2019)

RSN: Randomized Subspace Newton. - Robert M. Gower; Dmitry Kovalev; Felix Lieder; Peter Richtárik (NeurIPS, 2019)

Stochastic Proximal Langevin Algorithm: Potential Splitting and Nonasymptotic Rates. - Adil Salim; Dmitry Kovalev; Peter Richtárik (NeurIPS, 2019)

Stochastic Convolutional Sparse Coding. - Jinhui Xiong; Peter Richtárik; Wolfgang Heidrich (VMV, 2019)

Online and Batch Supervised Background Estimation Via L1 Regression. - Aritra Dutta; Peter Richtárik (WACV, 2019)

Distributed Learning with Compressed Gradient Differences. - Konstantin Mishchenko; Eduard Gorbunov; Martin Takác 001; Peter Richtárik (CoRR, 2019)

A Privacy Preserving Randomized Gossip Algorithm via Controlled Noise Insertion. - Filip Hanzely; Jakub Konecný; Nicolas Loizou; Peter Richtárik; Dmitry Grishchenko (CoRR, 2019)

SGD: General Analysis and Improved Rates. - Robert Mansel Gower; Nicolas Loizou; Xun Qian; Alibek Sailanbayev; Egor Shulgin; Peter Richtárik (CoRR, 2019)

99% of Parallel Optimization is Inevitably a Waste of Time. - Konstantin Mishchenko; Filip Hanzely; Peter Richtárik (CoRR, 2019)

One Method to Rule Them All: Variance Reduction for Data, Parameters and Many New Methods. - Filip Hanzely; Peter Richtárik (CoRR, 2019)

First Analysis of Local GD on Heterogeneous Data. - Ahmed Khaled 001; Konstantin Mishchenko; Peter Richtárik (CoRR, 2019)

Gradient Descent with Compressed Iterates. - Ahmed Khaled 001; Peter Richtárik (CoRR, 2019)

Better Communication Complexity for Local SGD. - Ahmed Khaled 001; Konstantin Mishchenko; Peter Richtárik (CoRR, 2019)

Stochastic Newton and Cubic Newton Methods with Simple Local Linear-Quadratic Rates. - Dmitry Kovalev; Konstantin Mishchenko; Peter Richtárik (CoRR, 2019)

Distributed Fixed Point Methods with Compressed Iterates. - Sélim Chraibi; Ahmed Khaled 001; Dmitry Kovalev; Peter Richtárik; Adil Salim; Martin Takác 001 (CoRR, 2019)

New Convergence Aspects of Stochastic Gradient Algorithms. - Lam M. Nguyen; Phuong Ha Nguyen; Peter Richtárik; Katya Scheinberg; Martin Takác 001; Marten van Dijk (J. Mach. Learn. Res., 2019)

Randomized Projection Methods for Convex Feasibility: Conditioning and Convergence Rates. - Ion Necoara; Peter Richtárik; Andrei Patrascu (SIAM J. Optim., 2019)

A Stochastic Derivative-Free Optimization Method with Importance Sampling: Theory and Learning to Control. - Adel Bibi; El Houcine Bergou; Ozan Sener; Bernard Ghanem; Peter Richtárik (AAAI, 2020)

Tighter Theory for Local SGD on Identical and Heterogeneous Data. - Ahmed Khaled 001; Konstantin Mishchenko; Peter Richtárik (AISTATS, 2020)

A Unified Theory of SGD: Variance Reduction, Sampling, Quantization and Coordinate Descent. - Eduard Gorbunov; Filip Hanzely; Peter Richtárik (AISTATS, 2020)

Revisiting Stochastic Extragradient. - Konstantin Mishchenko; Dmitry Kovalev; Egor Shulgin; Peter Richtárik; Yura Malitsky (AISTATS, 2020)

Don't Jump Through Hoops and Remove Those Loops: SVRG and Katyusha are Better Without the Outer Loop. - Dmitry Kovalev; Samuel Horváth; Peter Richtárik (ALT, 2020)

A Stochastic Derivative Free Optimization Method with Momentum. - Eduard Gorbunov; Adel Bibi; Ozan Sener; El Houcine Bergou; Peter Richtárik (ICLR, 2020)

Stochastic Subspace Cubic Newton Method. - Filip Hanzely; Nikita Doikov; Yurii E. Nesterov; Peter Richtárik (ICML, 2020)

Variance Reduced Coordinate Descent with Acceleration: New Method With a Surprising Application to Finite-Sum Problems. - Filip Hanzely; Dmitry Kovalev; Peter Richtárik (ICML, 2020)

Acceleration for Compressed Gradient Descent in Distributed and Federated Optimization. - Zhize Li; Dmitry Kovalev; Xun Qian; Peter Richtárik (ICML, 2020)

From Local SGD to Local Fixed-Point Methods for Federated Learning. - Grigory Malinovskiy; Dmitry Kovalev; Elnur Gasanov; Laurent Condat; Peter Richtárik (ICML, 2020)

Linearly Converging Error Compensated SGD. - Eduard Gorbunov; Dmitry Kovalev; Dmitry Makarenko; Peter Richtárik (NeurIPS, 2020)

Lower Bounds and Optimal Algorithms for Personalized Federated Learning. - Filip Hanzely; Slavomír Hanzely; Samuel Horváth; Peter Richtárik (NeurIPS, 2020)

Optimal and Practical Algorithms for Smooth and Strongly Convex Decentralized Optimization. - Dmitry Kovalev; Adil Salim; Peter Richtárik (NeurIPS, 2020)

Random Reshuffling: Simple Analysis with Vast Improvements. - Konstantin Mishchenko; Ahmed Khaled 001; Peter Richtárik (NeurIPS, 2020)

Primal Dual Interpretation of the Proximal Stochastic Gradient Langevin Algorithm. - Adil Salim; Peter Richtárik (NeurIPS, 2020)

99% of Worker-Master Communication in Distributed Optimization Is Not Needed. - Konstantin Mishchenko; Filip Hanzely; Peter Richtárik (UAI, 2020)

Momentum and stochastic momentum for stochastic gradient, Newton, proximal point and subspace descent methods. - Nicolas Loizou; Peter Richtárik (Comput. Optim. Appl., 2020)

Federated Learning of a Mixture of Global and Local Models. - Filip Hanzely; Peter Richtárik (CoRR, 2020)

Uncertainty Principle for Communication Compression in Distributed and Federated Learning and the Search for an Optimal Compressor. - Mher Safaryan; Egor Shulgin; Peter Richtárik (CoRR, 2020)

Fast Linear Convergence of Randomized BFGS. - Dmitry Kovalev; Robert M. Gower; Peter Richtárik; Alexander Rogozin (CoRR, 2020)

On the Convergence Analysis of Asynchronous SGD for Solving Consistent Linear Systems. - Atal Narayan Sahu; Aritra Dutta; Aashutosh Tiwari; Peter Richtárik (CoRR, 2020)

Dualize, Split, Randomize: Fast Nonsmooth Optimization Algorithms. - Adil Salim; Laurent Condat; Konstantin Mishchenko; Peter Richtárik (CoRR, 2020)

Adaptive Learning of the Optimal Mini-Batch Size of SGD. - Motasem Alfarra; Slavomír Hanzely; Alyazeed Albasyoni; Bernard Ghanem; Peter Richtárik (CoRR, 2020)

A Unified Analysis of Stochastic Gradient Methods for Nonconvex Federated Optimization. - Zhize Li; Peter Richtárik (CoRR, 2020)

Distributed Proximal Splitting Algorithms with Rates and Acceleration. - Laurent Condat; Grigory Malinovsky; Peter Richtárik (CoRR, 2020)

Optimal Gradient Compression for Distributed and Federated Learning. - Alyazeed Albasyoni; Mher Safaryan; Laurent Condat; Peter Richtárik (CoRR, 2020)

Variance-Reduced Methods for Machine Learning. - Robert M. Gower; Mark Schmidt 001; Francis R. Bach; Peter Richtárik (Proc. IEEE, 2020)

Stochastic Three Points Method for Unconstrained Smooth Minimization. - El Houcine Bergou; Eduard Gorbunov; Peter Richtárik (SIAM J. Optim., 2020)

Stochastic Reformulations of Linear Systems: Algorithms and Convergence Theory. - Peter Richtárik; Martin Takác 001 (SIAM J. Matrix Anal. Appl., 2020)

Convergence Analysis of Inexact Randomized Iterative Methods. - Nicolas Loizou; Peter Richtárik (SIAM J. Sci. Comput., 2020)

Best Pair Formulation & Accelerated Scheme for Non-Convex Principal Component Pursuit. - Aritra Dutta; Filip Hanzely; Jingwei Liang; Peter Richtárik (IEEE Trans. Signal Process., 2020)

Local SGD: Unified Theory and New Efficient Methods. - Eduard Gorbunov; Filip Hanzely; Peter Richtárik (AISTATS, 2021)

Hyperparameter Transfer Learning with Adaptive Complexity. - Samuel Horváth; Aaron Klein; Peter Richtárik; Cédric Archambeau (AISTATS, 2021)

A Linearly Convergent Algorithm for Decentralized Optimization: Sending Less Bits for Free! - Dmitry Kovalev; Anastasia Koloskova; Martin Jaggi; Peter Richtárik; Sebastian U. Stich (AISTATS, 2021)

FL_PyTorch: optimization research simulator for federated learning. - Konstantin Burlachenko; Samuel Horváth; Peter Richtárik (DistributedML@CoNEXT, 2021)

A Better Alternative to Error Feedback for Communication-Efficient Distributed Learning. - Samuel Horváth; Peter Richtárik (ICLR, 2021)

MARINA: Faster Non-Convex Distributed Learning with Compression. - Eduard Gorbunov; Konstantin Burlachenko; Zhize Li; Peter Richtárik (ICML, 2021)

Distributed Second Order Methods with Fast Rates and Compressed Communication. - Rustem Islamov; Xun Qian; Peter Richtárik (ICML, 2021)

ADOM: Accelerated Decentralized Optimization Method for Time-Varying Networks. - Dmitry Kovalev; Egor Shulgin; Peter Richtárik; Alexander Rogozin; Alexander V. Gasnikov (ICML, 2021)

PAGE: A Simple and Optimal Probabilistic Gradient Estimator for Nonconvex Optimization. - Zhize Li; Hongyan Bao; Xiangliang Zhang 001; Peter Richtárik (ICML, 2021)

Stochastic Sign Descent Methods: New Algorithms and Better Theory. - Mher Safaryan; Peter Richtárik (ICML, 2021)

Lower Bounds and Optimal Algorithms for Smooth and Strongly Convex Decentralized Optimization Over Time-Varying Networks. - Dmitry Kovalev; Elnur Gasanov; Alexander V. Gasnikov; Peter Richtárik (NeurIPS, 2021)

CANITA: Faster Rates for Distributed Convex Optimization with Communication Compression. - Zhize Li; Peter Richtárik (NeurIPS, 2021)

Error Compensated Distributed SGD Can Be Accelerated. - Xun Qian; Peter Richtárik; Tong Zhang 001 (NeurIPS, 2021)

EF21: A New, Simpler, Theoretically Better, and Practically Faster Error Feedback. - Peter Richtárik; Igor Sokolov 001; Ilyas Fatkhullin (NeurIPS, 2021)

Smoothness Matrices Beat Smoothness Constants: Better Communication Compression Techniques for Distributed Optimization. - Mher Safaryan; Filip Hanzely; Peter Richtárik (NeurIPS, 2021)

Scaling Distributed Machine Learning with In-Network Aggregation. - Amedeo Sapio; Marco Canini; Chen-Yu Ho 001; Jacob Nelson 001; Panos Kalnis; Changhoon Kim; Arvind Krishnamurthy; Masoud Moshref; Dan R. K. Ports; Peter Richtárik (NSDI, 2021)

Fastest rates for stochastic mirror descent methods. - Filip Hanzely; Peter Richtárik (Comput. Optim. Appl., 2021)

Accelerated Bregman proximal gradient methods for relatively smooth convex optimization. - Filip Hanzely; Peter Richtárik; Lin Xiao 003 (Comput. Optim. Appl., 2021)

IntSGD: Floatless Compression of Stochastic Gradients. - Konstantin Mishchenko; Bokun Wang; Dmitry Kovalev; Peter Richtárik (CoRR, 2021)

AI-SARAH: Adaptive and Implicit Stochastic Recursive Gradient Methods. - Zheng Shi; Nicolas Loizou; Peter Richtárik; Martin Takác 001 (CoRR, 2021)

ZeroSARAH: Efficient Nonconvex Finite-Sum Optimization with Zero Full Gradient Computation. - Zhize Li; Peter Richtárik (CoRR, 2021)

Complexity Analysis of Stein Variational Gradient Descent Under Talagrand's Inequality T1. - Adil Salim; Lukang Sun; Peter Richtárik (CoRR, 2021)

Smoothness-Aware Quantization Techniques. - Bokun Wang; Mher Safaryan; Peter Richtárik (CoRR, 2021)

A Field Guide to Federated Optimization. - Jianyu Wang; Zachary Charles; Zheng Xu 002; Gauri Joshi; H. Brendan McMahan; Blaise Agüera y Arcas; Maruan Al-Shedivat; Galen Andrew; Salman Avestimehr; Katharine Daly; Deepesh Data; Suhas N. Diggavi; et al. (CoRR, 2021)

FedPAGE: A Fast Local Stochastic Gradient Method for Communication-Efficient Federated Learning. - Haoyu Zhao; Zhize Li; Peter Richtárik (CoRR, 2021)

EF21 with Bells & Whistles: Practical Algorithmic Extensions of Modern Error Feedback. - Ilyas Fatkhullin; Igor Sokolov 001; Eduard Gorbunov; Zhize Li; Peter Richtárik (CoRR, 2021)

L-SVRG and L-Katyusha with Arbitrary Sampling. - Xun Qian; Zheng Qu 001; Peter Richtárik (J. Mach. Learn. Res., 2021)

Stochastic quasi-gradient methods: variance reduction via Jacobian sketching. - Robert M. Gower; Peter Richtárik; Francis R. Bach (Math. Program., 2021)

Revisiting Randomized Gossip Algorithms: General Framework, Convergence Rates and Novel Block and Accelerated Protocols. - Nicolas Loizou; Peter Richtárik (IEEE Trans. Inf. Theory, 2021)

FLIX: A Simple and Communication-Efficient Alternative to Local Methods in Federated Learning. - Elnur Gasanov; Ahmed Khaled 001; Samuel Horváth; Peter Richtárik (AISTATS, 2022)

Basis Matters: Better Communication-Efficient Second Order Methods for Federated Learning. - Xun Qian; Rustem Islamov; Mher Safaryan; Peter Richtárik (AISTATS, 2022)

An Optimal Algorithm for Strongly Convex Minimization under Affine Constraints. - Adil Salim; Laurent Condat; Dmitry Kovalev; Peter Richtárik (AISTATS, 2022)

Doubly Adaptive Scaled Algorithm for Machine Learning Using Second-Order Information. - Majid Jahani; Sergey Rusakov 001; Zheng Shi; Peter Richtárik; Michael W. Mahoney; Martin Takác 001 (ICLR, 2022)

IntSGD: Adaptive Floatless Compression of Stochastic Gradients. - Konstantin Mishchenko; Bokun Wang; Dmitry Kovalev; Peter Richtárik (ICLR, 2022)

Permutation Compressors for Provably Faster Distributed Nonconvex Optimization. - Rafal Szlendak; Alexander Tyurin; Peter Richtárik (ICLR, 2022)

Proximal and Federated Random Reshuffling. - Konstantin Mishchenko; Ahmed Khaled 001; Peter Richtárik (ICML, 2022)

ProxSkip: Yes! Local Gradient Steps Provably Lead to Communication Acceleration! Finally! - Konstantin Mishchenko; Grigory Malinovsky; Sebastian U. Stich; Peter Richtárik (ICML, 2022)

3PC: Three Point Compressors for Communication-Efficient Distributed Training and a Better Theory for Lazy Aggregation. - Peter Richtárik; Igor Sokolov 001; Elnur Gasanov; Ilyas Fatkhullin; Zhize Li; Eduard Gorbunov (ICML, 2022)

FedNL: Making Newton-Type Methods Applicable to Federated Learning. - Mher Safaryan; Rustem Islamov; Xun Qian; Peter Richtárik (ICML, 2022)

A Convergence Theory for SVGD in the Population Limit under Talagrand's Inequality T1. - Adil Salim; Lukang Sun; Peter Richtárik (ICML, 2022)

MURANA: A Generic Framework for Stochastic Variance-Reduced Optimization. - Laurent Condat; Peter Richtárik (MSML, 2022)

Natural Compression for Distributed Deep Learning. - Samuel Horváth; Chen-Yu Ho 001; Ludovit Horvath; Atal Narayan Sahu; Marco Canini; Peter Richtárik (MSML, 2022)

Distributed Methods with Compressed Communication for Solving Variational Inequalities, with Theoretical Guarantees. - Aleksandr Beznosikov; Peter Richtárik; Michael Diskin; Max Ryabinin; Alexander V. Gasnikov (NeurIPS, 2022)

EF-BV: A Unified Theory of Error Feedback and Variance Reduction Mechanisms for Biased and Unbiased Compression in Distributed Optimization. - Laurent Condat; Kai Yi; Peter Richtárik (NeurIPS, 2022)

A Damped Newton Method Achieves Global $ mathcal O left( frac{1}{k^2} right)$ and Local Quadratic Convergence Rate. - Slavomír Hanzely; Dmitry Kamzolov; Dmitry Pasechnyuk; Alexander V. Gasnikov; Peter Richtárik; Martin Takác 001 (NeurIPS, 2022)

Optimal Algorithms for Decentralized Stochastic Variational Inequalities. - Dmitry Kovalev; Aleksandr Beznosikov; Abdurakhmon Sadiev; Michael Persiianov; Peter Richtárik; Alexander V. Gasnikov (NeurIPS, 2022)

Accelerated Primal-Dual Gradient Method for Smooth and Convex-Concave Saddle-Point Problems with Bilinear Coupling. - Dmitry Kovalev; Alexander V. Gasnikov; Peter Richtárik (NeurIPS, 2022)

Variance Reduced ProxSkip: Algorithm, Theory and Application to Federated Learning. - Grigory Malinovsky; Kai Yi; Peter Richtárik (NeurIPS, 2022)

Communication Acceleration of Local Gradient Methods via an Accelerated Primal-Dual Algorithm with an Inexact Prox. - Abdurakhmon Sadiev; Dmitry Kovalev; Peter Richtárik (NeurIPS, 2022)

Theoretically Better and Numerically Faster Distributed Optimization with Smoothness-Aware Quantization Techniques. - Bokun Wang; Mher Safaryan; Peter Richtárik (NeurIPS, 2022)

BEER: Fast $O(1/T)$ Rate for Decentralized Nonconvex Optimization with Communication Compression. - Haoyu Zhao; Boyue Li; Zhize Li; Peter Richtárik; Yuejie Chi (NeurIPS, 2022)

Shifted compression framework: generalizations and improvements. - Egor Shulgin; Peter Richtárik (UAI, 2022)

DASHA: Distributed Nonconvex Optimization with Communication Compression, Optimal Oracle Complexity, and No Client Synchronization. - Alexander Tyurin; Peter Richtárik (CoRR, 2022)

Federated Random Reshuffling with Compression and Variance Reduction. - Grigory Malinovsky; Peter Richtárik (CoRR, 2022)

Federated Learning with a Sampling Algorithm under Isoperimetry. - Lukang Sun; Adil Salim; Peter Richtárik (CoRR, 2022)

Certified Robustness in Federated Learning. - Motasem Alfarra; Juan C. Pérez; Egor Shulgin; Peter Richtárik; Bernard Ghanem (CoRR, 2022)

Federated Optimization Algorithms with Random Reshuffling and Gradient Compression. - Abdurakhmon Sadiev; Grigory Malinovsky; Eduard Gorbunov; Igor Sokolov 001; Ahmed Khaled 001; Konstantin Burlachenko; Peter Richtárik (CoRR, 2022)

A Note on the Convergence of Mirrored Stein Variational Gradient Descent under (L0, L1)-Smoothness Condition. - Lukang Sun; Peter Richtárik (CoRR, 2022)

Communication Acceleration of Local Gradient Methods via an Accelerated Primal-Dual Algorithm with Inexact Prox. - Abdurakhmon Sadiev; Dmitry Kovalev; Peter Richtárik (CoRR, 2022)

Adaptive Learning Rates for Faster Stochastic Gradient Methods. - Samuel Horváth; Konstantin Mishchenko; Peter Richtárik (CoRR, 2022)

Improved Stein Variational Gradient Descent with Importance Weights. - Lukang Sun; Peter Richtárik (CoRR, 2022)

Provably Doubly Accelerated Federated Learning: The First Theoretically Successful Combination of Local Training and Compressed Communication. - Laurent Condat; Ivan Agarský; Peter Richtárik (CoRR, 2022)

Can 5th Generation Local Training Methods Support Client Sampling? Yes! - Michal Grudzien; Grigory Malinovsky; Peter Richtárik (CoRR, 2022)

Direct nonlinear acceleration. - Aritra Dutta; El Houcine Bergou; Yunming Xiao; Marco Canini; Peter Richtárik (EURO J. Comput. Optim., 2022)

Dualize, Split, Randomize: Toward Fast Nonsmooth Optimization Algorithms. - Adil Salim; Laurent Condat; Konstantin Mishchenko; Peter Richtárik (J. Optim. Theory Appl., 2022)

Quasi-Newton methods for machine learning: forget the past, just sample. - Albert S. Berahas; Majid Jahani; Peter Richtárik; Martin Takác 001 (Optim. Methods Softw., 2022)

Adaptivity of Stochastic Gradient Methods for Nonconvex Optimization. - Samuel Horváth; Lihua Lei; Peter Richtárik; Michael I. Jordan (SIAM J. Math. Data Sci., 2022)

Optimal Client Sampling for Federated Learning. - Wenlin Chen; Samuel Horváth; Peter Richtárik (Trans. Mach. Learn. Res., 2022)

FedShuffle: Recipes for Better Use of Local Work in Federated Learning. - Samuel Horváth; Maziar Sanjabi; Lin Xiao 003; Peter Richtárik; Michael G. Rabbat (Trans. Mach. Learn. Res., 2022)

Can 5th Generation Local Training Methods Support Client Sampling? Yes! - Michal Grudzien; Grigory Malinovsky; Peter Richtárik (AISTATS, 2023)

Catalyst Acceleration of Error Compensated Methods Leads to Better Communication Complexity. - Xun Qian; Hanze Dong; Tong Zhang 001; Peter Richtárik (AISTATS, 2023)

Convergence of Stein Variational Gradient Descent under a Weaker Smoothness Condition. - Lukang Sun; Avetik G. Karagulyan; Peter Richtárik (AISTATS, 2023)

Federated Learning is Better with Non-Homomorphic Encryption. - Konstantin Burlachenko; Abdulmajeed Alrowithi; Fahad Ali Albalawi; Peter Richtárik (DistributedML@CoNEXT, 2023)

Server-Side Stepsizes and Sampling Without Replacement Provably Help in Federated Optimization. - Grigory Malinovsky; Konstantin Mishchenko; Peter Richtárik (DistributedML@CoNEXT, 2023)

Kimad: Adaptive Gradient Compression with Bandwidth Awareness. - Jihao Xin; Ivan Ilin; Shunkang Zhang; Marco Canini; Peter Richtárik (DistributedML@CoNEXT, 2023)

RandProx: Primal-Dual Optimization Algorithms with Randomized Proximal Updates. - Laurent Condat; Peter Richtárik (ICLR, 2023)

Variance Reduction is an Antidote to Byzantines: Better Rates, Weaker Assumptions and Communication Compression as a Cherry on the Top. - Eduard Gorbunov; Samuel Horváth; Peter Richtárik; Gauthier Gidel (ICLR, 2023)

DASHA: Distributed Nonconvex Optimization with Communication Compression and Optimal Oracle Complexity. - Alexander Tyurin; Peter Richtárik (ICLR, 2023)

EF21-P and Friends: Improved Theoretical Communication Complexity for Distributed Optimization with Bidirectional Compression. - Kaja Gruntkowska; Alexander Tyurin; Peter Richtárik (ICML, 2023)

High-Probability Bounds for Stochastic Optimization and Variational Inequalities: the Case of Unbounded Variance. - Abdurakhmon Sadiev; Marina Danilova; Eduard Gorbunov; Samuel Horváth; Gauthier Gidel; Pavel E. Dvurechensky; Alexander V. Gasnikov; Peter Richtárik (ICML, 2023)

A Guide Through the Zoo of Biased SGD. - Yury Demidovich; Grigory Malinovsky; Igor Sokolov 001; Peter Richtárik (NeurIPS, 2023)

Momentum Provably Improves Error Feedback! - Ilyas Fatkhullin; Alexander Tyurin; Peter Richtárik (NeurIPS, 2023)

2Direction: Theoretically Faster Distributed Training with Bidirectional Communication Compression. - Alexander Tyurin; Peter Richtárik (NeurIPS, 2023)

Optimal Time Complexities of Parallel Stochastic Optimization Methods Under a Fixed Computation Model. - Alexander Tyurin; Peter Richtárik (NeurIPS, 2023)

A Computation and Communication Efficient Method for Distributed Nonconvex Problems in the Partial Participation Setting. - Alexander Tyurin; Peter Richtárik (NeurIPS, 2023)

Random Reshuffling with Variance Reduction: New Analysis and Better Rates. - Grigory Malinovsky; Alibek Sailanbayev; Peter Richtárik (UAI, 2023)

Convergence of First-Order Algorithms for Meta-Learning with Moreau Envelopes. - Konstantin Mishchenko; Slavomír Hanzely; Peter Richtárik (CoRR, 2023)

Federated Learning with Regularized Client Participation. - Grigory Malinovsky; Samuel Horváth; Konstantin Burlachenko; Peter Richtárik (CoRR, 2023)

TAMUNA: Accelerated Federated Learning with Local Training and Partial Participation. - Laurent Condat; Grigory Malinovsky; Peter Richtárik (CoRR, 2023)

Error Feedback Shines when Features are Rare. - Peter Richtárik; Elnur Gasanov; Konstantin Burlachenko (CoRR, 2023)

Global-QSGD: Practical Floatless Quantization for Distributed Learning with Theoretical Guarantees. - Jihao Xin; Marco Canini; Peter Richtárik; Samuel Horváth (CoRR, 2023)

Clip21: Error Feedback for Gradient Clipping. - Sarit Khirirat; Eduard Gorbunov; Samuel Horváth; Rustem Islamov; Fakhri Karray; Peter Richtárik (CoRR, 2023)

Improving Accelerated Federated Learning with Compression and Importance Sampling. - Michal Grudzien; Grigory Malinovsky; Peter Richtárik (CoRR, 2023)

Byzantine Robustness and Partial Participation Can Be Achieved Simultaneously: Just Clip Gradient Differences. - Grigory Malinovsky; Peter Richtárik; Samuel Horváth; Eduard Gorbunov (CoRR, 2023)

On Biased Compression for Distributed Learning. - Aleksandr Beznosikov; Samuel Horváth; Peter Richtárik; Mher Safaryan (J. Mach. Learn. Res., 2023)

Personalized Federated Learning with Communication Compression. - El Houcine Bergou; Konstantin Burlachenko; Aritra Dutta; Peter Richtárik (Trans. Mach. Learn. Res., 2023)

Distributed Newton-Type Methods with Communication Compression and Bernoulli Aggregation. - Rustem Islamov; Xun Qian; Slavomír Hanzely; Mher Safaryan; Peter Richtárik (Trans. Mach. Learn. Res., 2023)

Better Theory for SGD in the Nonconvex World. - Ahmed Khaled 001; Peter Richtárik (Trans. Mach. Learn. Res., 2023)

Adaptive Compression for Communication-Efficient Distributed Training. - Maksim Makarenko; Elnur Gasanov; Abdurakhmon Sadiev; Rustem Islamov; Peter Richtárik (Trans. Mach. Learn. Res., 2023)

AI-SARAH: Adaptive and Implicit Stochastic Recursive Gradient Methods. - Zheng Shi; Abdurakhmon Sadiev; Nicolas Loizou; Peter Richtárik; Martin Takác 001 (Trans. Mach. Learn. Res., 2023)

Sharper Rates and Flexible Framework for Nonconvex SGD with Client and Data Sampling. - Alexander Tyurin; Lukang Sun; Konstantin Burlachenko; Peter Richtárik (Trans. Mach. Learn. Res., 2023)

Stochastic distributed learning with gradient quantization and double-variance reduction. - Samuel Horváth; Dmitry Kovalev; Konstantin Mishchenko; Peter Richtárik; Sebastian U. Stich (Optim. Methods Softw., 2023)

Unified Analysis of Stochastic Gradient Methods for Composite Convex and Smooth Optimization. - Ahmed Khaled 001; Othmane Sebbouh; Nicolas Loizou; Robert M. Gower; Peter Richtárik (J. Optim. Theory Appl., 2023)

Minibatch Stochastic Three Points Method for Unconstrained Smooth Minimization. - Soumia Boucherouite; Grigory Malinovsky; Peter Richtárik; El Houcine Bergou (AAAI, 2024)

Communication Compression for Byzantine Robust Learning: New Efficient Algorithms and Improved Rates. - Ahmad Rammal; Kaja Gruntkowska; Nikita Fedin; Eduard Gorbunov; Peter Richtárik (AISTATS, 2024)

Understanding Progressive Training Through the Framework of Randomized Coordinate Descent. - Rafal Szlendak; Elnur Gasanov; Peter Richtárik (AISTATS, 2024)

Det-CGD: Compressed Gradient Descent with Matrix Stepsizes for Non-Convex Optimization. - Hanmin Li; Avetik G. Karagulyan; Peter Richtárik (ICLR, 2024)

Error Feedback Reloaded: From Quadratic to Arithmetic Mean of Smoothness Constants. - Peter Richtárik; Elnur Gasanov; Konstantin Burlachenko (ICLR, 2024)

FedP3: Federated Personalized and Privacy-friendly Network Pruning under Model Heterogeneity. - Kai Yi; Nidham Gazagnadou; Peter Richtárik; Lingjuan Lyu (ICLR, 2024)

High-Probability Convergence for Composite and Distributed Stochastic Minimization and Variational Inequalities with Heavy-Tailed Noise. - Eduard Gorbunov; Abdurakhmon Sadiev; Marina Danilova; Samuel Horváth; Gauthier Gidel; Pavel E. Dvurechensky; Alexander V. Gasnikov; Peter Richtárik (ICML, 2024)

Towards a Better Theoretical Understanding of Independent Subnetwork Training. - Egor Shulgin; Peter Richtárik (ICML, 2024)

Improving the Worst-Case Bidirectional Communication Complexity for Nonconvex Distributed Optimization under Function Similarity. - Kaja Gruntkowska; Alexander Tyurin; Peter Richtárik (NeurIPS, 2024)

The Power of Extrapolation in Federated Learning. - Hanmin Li; Kirill Acharya; Peter Richtárik (NeurIPS, 2024)

PV-Tuning: Beyond Straight-Through Estimation for Extreme LLM Compression. - Vladimir Malinovskii; Denis Mazur; Ivan Ilin; Denis Kuznedelev; Konstantin Burlachenko; Kai Yi; Dan Alistarh; Peter Richtárik (NeurIPS, 2024)

Byzantine Robustness and Partial Participation Can Be Achieved at Once: Just Clip Gradient Differences. - Grigory Malinovsky; Peter Richtárik; Samuel Horváth; Eduard Gorbunov (NeurIPS, 2024)

MicroAdam: Accurate Adaptive Optimization with Low Space Overhead and Provable Convergence. - Ionut-Vlad Modoranu; Mher Safaryan; Grigory Malinovsky; Eldar Kurtic; Thomas Robert 007; Peter Richtárik; Dan Alistarh (NeurIPS, 2024)

Don't Compress Gradients in Random Reshuffling: Compress Gradient Differences. - Abdurakhmon Sadiev; Grigory Malinovsky; Eduard Gorbunov; Igor Sokolov 001; Ahmed Khaled 001; Konstantin Burlachenko; Peter Richtárik (NeurIPS, 2024)

Freya PAGE: First Optimal Time Complexity for Large-Scale Nonconvex Finite-Sum Optimization with Heterogeneous Asynchronous Computations. - Alexander Tyurin; Kaja Gruntkowska; Peter Richtárik (NeurIPS, 2024)

Shadowheart SGD: Distributed Asynchronous SGD with Optimal Time Complexity Under Arbitrary Computation and Communication Heterogeneity. - Alexander Tyurin; Marta Pozzi; Ivan Ilin; Peter Richtárik (NeurIPS, 2024)

On the Optimal Time Complexities in Decentralized Stochastic Asynchronous Optimization. - Alexander Tyurin; Peter Richtárik (NeurIPS, 2024)

Streamlining in the Riemannian Realm: Efficient Riemannian Optimization with Loopless Variance Reduction. - Yury Demidovich; Grigory Malinovsky; Peter Richtárik (CoRR, 2024)

FedComLoc: Communication-Efficient Distributed Training of Sparse and Quantized Models. - Kai Yi; Georg Meinhardt; Laurent Condat; Peter Richtárik (CoRR, 2024)

A Unified Theory of Stochastic Proximal Point Methods without Smoothness. - Peter Richtárik; Abdurakhmon Sadiev; Yury Demidovich (CoRR, 2024)

SPAM: Stochastic Proximal Point Method with Momentum Variance Reduction for Non-convex Cross-Device Federated Learning. - Avetik G. Karagulyan; Egor Shulgin; Abdurakhmon Sadiev; Peter Richtárik (CoRR, 2024)

Prune at the Clients, Not the Server: Accelerated Sparse Training in Federated Learning. - Georg Meinhardt; Kai Yi; Laurent Condat; Peter Richtárik (CoRR, 2024)

Cohort Squeeze: Beyond a Single Communication Round per Cohort in Cross-Device Federated Learning. - Kai Yi; Timur Kharisov; Igor Sokolov 001; Peter Richtárik (CoRR, 2024)

Methods for Convex (L0,L1)-Smooth Optimization: Clipping, Acceleration, and Adaptivity. - Eduard Gorbunov; Nazarii Tupitsa; Sayantan Choudhury; Alen Aliev; Peter Richtárik; Samuel Horváth; Martin Takác 001 (CoRR, 2024)

On the Convergence of FedProx with Extrapolation and Inexact Prox. - Hanmin Li; Peter Richtárik (CoRR, 2024)

MindFlayer: Efficient Asynchronous Parallel SGD in the Presence of Heterogeneous and Random Worker Compute Times. - Artavazd Maranjyan; Omar Shaikh Omar; Peter Richtárik (CoRR, 2024)

Randomized Asymmetric Chain of LoRA: The First Meaningful Theoretical Framework for Low-Rank Adaptation. - Grigory Malinovsky; Umberto Michieli; Hasan Abed Al Kader Hammoud; Taha Ceritli; Hayder Elesedy; Mete Ozay; Peter Richtárik (CoRR, 2024)

Unlocking FedNL: Self-Contained Compute-Optimized Implementation. - Konstantin Burlachenko; Peter Richtárik (CoRR, 2024)

Tighter Performance Theory of FedExProx. - Wojciech Anyszka; Kaja Gruntkowska; Alexander Tyurin; Peter Richtárik (CoRR, 2024)

Error Feedback under (L0,L1)-Smoothness: Normalization and Momentum. - Sarit Khirirat; Abdurakhmon Sadiev; Artem Riabinin; Eduard Gorbunov; Peter Richtárik (CoRR, 2024)

Pushing the Limits of Large Language Model Quantization via the Linearity Theorem. - Vladimir Malinovskii; Andrei Panferov; Ivan Ilin; Han Guo; Peter Richtárik; Dan Alistarh (CoRR, 2024)

Differentially Private Random Block Coordinate Descent. - Artavazd Maranjyan; Abdurakhmon Sadiev; Peter Richtárik (CoRR, 2024)

MARINA-P: Superior Performance in Non-smooth Federated Optimization with Adaptive Stepsizes. - Igor Sokolov 001; Peter Richtárik (CoRR, 2024)

On the Convergence of DP-SGD with Adaptive Clipping. - Egor Shulgin; Peter Richtárik (CoRR, 2024)

Federated Sampling with Langevin Algorithm under Isoperimetry. - Lukang Sun; Adil Salim; Peter Richtárik (Trans. Mach. Learn. Res., 2024)

Faster Rates for Compressed Federated Learning with Client-Variance Reduction. - Haoyu Zhao; Konstantin Burlachenko; Zhize Li; Peter Richtárik (SIAM J. Math. Data Sci., 2024)

Global-QSGD: Allreduce-Compatible Quantization for Distributed Learning with Theoretical Guarantees. - Jihao Xin; Marco Canini; Peter Richtárik; Samuel Horváth (EuroMLSys, 2025)

LoCoDL: Communication-Efficient Distributed Learning with Local Training and Compression. - Laurent Condat; Arto Maranjyan; Peter Richtárik (ICLR, 2025)

MAST: model-agnostic sparsified training. - Yury Demidovich; Grigory Malinovsky; Egor Shulgin; Peter Richtárik (ICLR, 2025)

Methods with Local Steps and Random Reshuffling for Generally Smooth Non-Convex Federated Optimization. - Yury Demidovich; Petr Ostroukhov; Grigory Malinovsky; Samuel Horváth; Martin Takác 001; Peter Richtárik; Eduard Gorbunov (ICLR, 2025)

Methods for Convex (L0, L1)-Smooth Optimization: Clipping, Acceleration, and Adaptivity. - Eduard Gorbunov; Nazarii Tupitsa; Sayantan Choudhury; Alen Aliev; Peter Richtárik; Samuel Horváth; Martin Takác 001 (ICLR, 2025)

HIGGS: Pushing the Limits of Large Language Model Quantization via the Linearity Theorem. - Vladimir Malinovskii; Andrei Panferov; Ivan Ilin; Han Guo; Peter Richtárik; Dan Alistarh (NAACL (Long Papers), 2025)

ELF: Federated Langevin Algorithms with Primal, Dual and Bidirectional Compression. - Avetik G. Karagulyan; Peter Richtárik (UAI, 2025)

MindFlayer SGD: Efficient Parallel SGD in the Presence of Heterogeneous and Random Worker Compute Times. - Arto Maranjyan; Omar Shaikh Omar; Peter Richtárik (UAI, 2025)

Correlated Quantization for Faster Nonconvex Distributed Optimization. - Andrei Panferov; Yury Demidovich; Ahmad Rammal; Peter Richtárik (UAI, 2025)

GradSkip: Communication-Accelerated Local Gradient Methods with Better Computational Complexity. - Arto Maranjyan; Mher Safaryan; Peter Richtárik (Trans. Mach. Learn. Res., 2025)

Explicit Personalization and Local Training: Double Communication Acceleration in Federated Learning. - Kai Yi; Laurent Condat; Peter Richtárik (Trans. Mach. Learn. Res., 2025)

Ringmaster ASGD: The First Asynchronous SGD with Optimal Time Complexity. - Artavazd Maranjyan; Alexander Tyurin; Peter Richtárik (CoRR, 2025)

Symmetric Pruning of Large Language Models. - Kai Yi; Peter Richtárik (CoRR, 2025)

ATA: Adaptive Task Allocation for Efficient Resource Management in Distributed Machine Learning. - Artavazd Maranjyan; El Mehdi Saad; Peter Richtárik; Francesco Orabona (CoRR, 2025)

The Ball-Proximal (="Broximal") Point Method: a New Algorithm, Convergence Theory, and Applications. - Kaja Gruntkowska; Hanmin Li; Aadi Rane; Peter Richtárik (CoRR, 2025)

Double Momentum and Error Feedback for Clipping with Fast Rates and Differential Privacy. - Rustem Islamov; Samuel Horváth; Aurélien Lucchi; Peter Richtárik; Eduard Gorbunov (CoRR, 2025)

A Novel Unified Parametric Assumption for Nonconvex Optimization. - Artem Riabinin; Ahmed Khaled 001; Peter Richtárik (CoRR, 2025)

Smoothed Normalization for Efficient Distributed Private Optimization. - Egor Shulgin; Sarit Khirirat; Peter Richtárik (CoRR, 2025)

BurTorch: Revisiting Training from First Principles by Coupling Autodiff, Math Optimization, and Systems. - Konstantin Burlachenko; Peter Richtárik (CoRR, 2025)

Collaborative Value Function Estimation Under Model Mismatch: A Federated Temporal Difference Analysis. - Ali Beikmohammadi; Sarit Khirirat; Peter Richtárik; Sindri Magnússon (CoRR, 2025)

Thanos: A Block-wise Pruning Algorithm for Efficient Large Language Model Compression. - Ivan Ilin; Peter Richtárik (CoRR, 2025)

Gluon: Making Muon & Scion Great Again! (Bridging Theory and Practice of LMO-based Optimizers for LLMs). - Artem Riabinin; Egor Shulgin; Kaja Gruntkowska; Peter Richtárik (CoRR, 2025)

Bernoulli-LoRA: A Theoretical Framework for Randomized Low-Rank Adaptation. - Igor Sokolov 001; Abdurakhmon Sadiev; Yury Demidovich; Fawaz S. Al-Qahtani; Peter Richtárik (CoRR, 2025)