Peter Richtárik

419 papers A* 86A 23B 2Misc 2Journal 288Unranked 18

Year	Rank	Type	Title / Venue / Authors
2026	J	`jnl`	BiCoLoR: Communication-Efficient Optimization with Bidirectional Compression and Local Training. CoRR Laurent Condat, Artavazd Maranjyan, Peter Richtárik
2026	J	`jnl`	Byzantine-Robust and Differentially Private Federated Optimization under Weaker Assumptions. CoRR Rustem Islamov, Grigory Malinovsky, Alexander Gaponov, Aurélien Lucchi, Peter Richtárik, Eduard Gorbunov
2025	J	`jnl`	A Novel Unified Parametric Assumption for Nonconvex Optimization. CoRR Artem Riabinin, Ahmed Khaled, Peter Richtárik
2025	A*	`conf`	ATA: Adaptive Task Allocation for Efficient Resource Management in Distributed Machine Learning. ICML Arto Maranjyan, El Mehdi Saad, Peter Richtárik, Francesco Orabona
2025	J	`jnl`	ATA: Adaptive Task Allocation for Efficient Resource Management in Distributed Machine Learning. CoRR Artavazd Maranjyan, El Mehdi Saad, Peter Richtárik, Francesco Orabona
2025	J	`jnl`	Bernoulli-LoRA: A Theoretical Framework for Randomized Low-Rank Adaptation. CoRR Igor Sokolov, Abdurakhmon Sadiev, Yury Demidovich, Fawaz S. Al-Qahtani, Peter Richtárik
2025	J	`jnl`	Better LMO-based Momentum Methods with Second-Order Information. CoRR Sarit Khirirat, Abdurakhmon Sadiev, Yury Demidovich, Peter Richtárik
2025	J	`jnl`	Beyond the Ideal: Analyzing the Inexact Muon Update. CoRR Egor Shulgin, Sultan AlRashed, Francesco Orabona, Peter Richtárik
2025	J	`jnl`	BurTorch: Revisiting Training from First Principles by Coupling Autodiff, Math Optimization, and Systems. CoRR Konstantin Burlachenko, Peter Richtárik
2025	—	`conf`	Collaborative Value Function Estimation Under Model Mismatch: A Federated Temporal Difference Analysis. ECML/PKDD (6) Ali Beikmohammadi, Sarit Khirirat, Peter Richtárik, Sindri Magnússon
2025	J	`jnl`	Collaborative Value Function Estimation Under Model Mismatch: A Federated Temporal Difference Analysis. CoRR Ali Beikmohammadi, Sarit Khirirat, Peter Richtárik, Sindri Magnússon
2025	J	`jnl`	Convergence Analysis of the PAGE Stochastic Algorithm for Weakly Convex Finite-Sum Optimization. CoRR Laurent Condat, Peter Richtárik
2025	A	`conf`	Correlated Quantization for Faster Nonconvex Distributed Optimization. UAI Andrei Panferov, Yury Demidovich, Ahmad Rammal, Peter Richtárik
2025	J	`jnl`	Distributed learning with compressed gradient differences. Optim. Methods Softw. Konstantin Mishchenko, Eduard Gorbunov, Martin Takác, Peter Richtárik
2025	J	`jnl`	Double Momentum and Error Feedback for Clipping with Fast Rates and Differential Privacy. CoRR Rustem Islamov, Samuel Horváth, Aurélien Lucchi, Peter Richtárik, Eduard Gorbunov
2025	J	`jnl`	Drop-Muon: Update Less, Converge Faster. CoRR Kaja Gruntkowska, Yassine Maziane, Zheng Qu, Peter Richtárik
2025	J	`jnl`	EF21 with Bells & Whistles: Six Algorithmic Extensions of Modern Error Feedback. J. Mach. Learn. Res. Ilyas Fatkhullin, Igor Sokolov, Eduard Gorbunov, Zhize Li, Peter Richtárik
2025	A	`conf`	ELF: Federated Langevin Algorithms with Primal, Dual and Bidirectional Compression. UAI Avetik G. Karagulyan, Peter Richtárik
2025	J	`jnl`	Error Feedback for Muon and Friends. CoRR Kaja Gruntkowska, Alexander Gaponov, Zhirayr Tovmasyan, Peter Richtárik
2025	J	`jnl`	Explicit Personalization and Local Training: Double Communication Acceleration in Federated Learning. Trans. Mach. Learn. Res. Kai Yi, Laurent Condat, Peter Richtárik
2025	J	`jnl`	FedComLoc: Communication-Efficient Distributed Training of Sparse and Quantized Models. Trans. Mach. Learn. Res. Kai Yi, Georg Meinhardt, Laurent Condat, Peter Richtárik
2025	J	`jnl`	First Provable Guarantees for Practical Private FL: Beyond Restrictive Assumptions. CoRR Egor Shulgin, Grigory Malinovsky, Sarit Khirirat, Peter Richtárik
2025	—	`conf`	Global-QSGD: Allreduce-Compatible Quantization for Distributed Learning with Theoretical Guarantees. EuroMLSys Jihao Xin, Marco Canini, Peter Richtárik, Samuel Horváth
2025	J	`jnl`	Gluon: Making Muon & Scion Great Again! (Bridging Theory and Practice of LMO-based Optimizers for LLMs). CoRR Artem Riabinin, Egor Shulgin, Kaja Gruntkowska, Peter Richtárik
2025	J	`jnl`	GradSkip: Communication-Accelerated Local Gradient Methods with Better Computational Complexity. Trans. Mach. Learn. Res. Arto Maranjyan, Mher Safaryan, Peter Richtárik
2025	—	`conf`	HIGGS: Pushing the Limits of Large Language Model Quantization via the Linearity Theorem. NAACL (Long Papers) Vladimir Malinovskii, Andrei Panferov, Ivan Ilin, Han Guo, Peter Richtárik, Dan Alistarh
2025	J	`jnl`	Improved Convergence in Parameter-Agnostic Error Feedback through Momentum. CoRR Abdurakhmon Sadiev, Yury Demidovich, Igor Sokolov, Grigory Malinovsky, Sarit Khirirat, Peter Richtárik
2025	A*	`conf`	LoCoDL: Communication-Efficient Distributed Learning with Local Training and Compression. ICLR Laurent Condat, Arto Maranjyan, Peter Richtárik
2025	A*	`conf`	MAST: model-agnostic sparsified training. ICLR Yury Demidovich, Grigory Malinovsky, Egor Shulgin, Peter Richtárik
2025	A*	`conf`	Methods for Convex (L0, L1)-Smooth Optimization: Clipping, Acceleration, and Adaptivity. ICLR Eduard Gorbunov, Nazarii Tupitsa, Sayantan Choudhury, Alen Aliev, Peter Richtárik, Samuel Horváth, Martin Takác
2025	A*	`conf`	Methods with Local Steps and Random Reshuffling for Generally Smooth Non-Convex Federated Optimization. ICLR Yury Demidovich, Petr Ostroukhov, Grigory Malinovsky, Samuel Horváth, Martin Takác, Peter Richtárik, Eduard Gorbunov
2025	A	`conf`	MindFlayer SGD: Efficient Parallel SGD in the Presence of Heterogeneous and Random Worker Compute Times. UAI Arto Maranjyan, Omar Shaikh Omar, Peter Richtárik
2025	J	`jnl`	Muon is Provably Faster with Momentum Variance Reduction. CoRR Xun Qian, Hussein Rammal, Dmitry Kovalev, Peter Richtárik
2025	J	`jnl`	Non-Euclidean Broximal Point Method: A Blueprint for Geometry-Aware Optimization. CoRR Kaja Gruntkowska, Peter Richtárik
2025	A	`conf`	Quantize Once, Train Fast: Allreduce-Compatible Compression with Provable Guarantees. ECAI Jihao Xin, Marco Canini, Peter Richtárik, Samuel Horváth
2025	J	`jnl`	Ringleader ASGD: The First Asynchronous SGD with Optimal Time Complexity under Data Heterogeneity. CoRR Artavazd Maranjyan, Peter Richtárik
2025	A*	`conf`	Ringmaster ASGD: The First Asynchronous SGD with Optimal Time Complexity. ICML Arto Maranjyan, Alexander Tyurin, Peter Richtárik
2025	J	`jnl`	Ringmaster ASGD: The First Asynchronous SGD with Optimal Time Complexity. CoRR Artavazd Maranjyan, Alexander Tyurin, Peter Richtárik
2025	J	`jnl`	Second-order Optimization under Heavy-Tailed Noise: Hessian Clipping and Sample Complexity Limits. CoRR Abdurakhmon Sadiev, Peter Richtárik, Ilyas Fatkhullin
2025	J	`jnl`	Smoothed Normalization for Efficient Distributed Private Optimization. CoRR Egor Shulgin, Sarit Khirirat, Peter Richtárik
2025	J	`jnl`	Symmetric Pruning of Large Language Models. CoRR Kai Yi, Peter Richtárik
2025	J	`jnl`	Thanos: A Block-wise Pruning Algorithm for Efficient Large Language Model Compression. CoRR Ivan Ilin, Peter Richtárik
2025	J	`jnl`	The Ball-Proximal (="Broximal") Point Method: a New Algorithm, Convergence Theory, and Applications. CoRR Kaja Gruntkowska, Hanmin Li, Aadi Rane, Peter Richtárik
2024	J	`jnl`	A Unified Theory of Stochastic Proximal Point Methods without Smoothness. CoRR Peter Richtárik, Abdurakhmon Sadiev, Yury Demidovich
2024	A*	`conf`	Byzantine Robustness and Partial Participation Can Be Achieved at Once: Just Clip Gradient Differences. NeurIPS Grigory Malinovsky, Peter Richtárik, Samuel Horváth, Eduard Gorbunov
2024	J	`jnl`	Cohort Squeeze: Beyond a Single Communication Round per Cohort in Cross-Device Federated Learning. CoRR Kai Yi, Timur Kharisov, Igor Sokolov, Peter Richtárik
2024	A	`conf`	Communication Compression for Byzantine Robust Learning: New Efficient Algorithms and Improved Rates. AISTATS Ahmad Rammal, Kaja Gruntkowska, Nikita Fedin, Eduard Gorbunov, Peter Richtárik
2024	J	`jnl`	Correlated Quantization for Faster Nonconvex Distributed Optimization. CoRR Andrei Panferov, Yury Demidovich, Ahmad Rammal, Peter Richtárik
2024	A*	`conf`	Det-CGD: Compressed Gradient Descent with Matrix Stepsizes for Non-Convex Optimization. ICLR Hanmin Li, Avetik G. Karagulyan, Peter Richtárik
2024	J	`jnl`	Differentially Private Random Block Coordinate Descent. CoRR Artavazd Maranjyan, Abdurakhmon Sadiev, Peter Richtárik
2024	A*	`conf`	Don't Compress Gradients in Random Reshuffling: Compress Gradient Differences. NeurIPS Abdurakhmon Sadiev, Grigory Malinovsky, Eduard Gorbunov, Igor Sokolov, Ahmed Khaled, Konstantin Burlachenko, Peter Richtárik
2024	A*	`conf`	Error Feedback Reloaded: From Quadratic to Arithmetic Mean of Smoothness Constants. ICLR Peter Richtárik, Elnur Gasanov, Konstantin Burlachenko
2024	J	`jnl`	Error Feedback Reloaded: From Quadratic to Arithmetic Mean of Smoothness Constants. CoRR Peter Richtárik, Elnur Gasanov, Konstantin Burlachenko
2024	J	`jnl`	Error Feedback under (L CoRR Sarit Khirirat, Abdurakhmon Sadiev, Artem Riabinin, Eduard Gorbunov, Peter Richtárik
2024	J	`jnl`	Faster Rates for Compressed Federated Learning with Client-Variance Reduction. SIAM J. Math. Data Sci. Haoyu Zhao, Konstantin Burlachenko, Zhize Li, Peter Richtárik
2024	J	`jnl`	FedComLoc: Communication-Efficient Distributed Training of Sparse and Quantized Models. CoRR Kai Yi, Georg Meinhardt, Laurent Condat, Peter Richtárik
2024	A*	`conf`	FedP3: Federated Personalized and Privacy-friendly Network Pruning under Model Heterogeneity. ICLR Kai Yi, Nidham Gazagnadou, Peter Richtárik, Lingjuan Lyu
2024	J	`jnl`	FedP3: Federated Personalized and Privacy-friendly Network Pruning under Model Heterogeneity. CoRR Kai Yi, Nidham Gazagnadou, Peter Richtárik, Lingjuan Lyu
2024	J	`jnl`	Federated Sampling with Langevin Algorithm under Isoperimetry. Trans. Mach. Learn. Res. Lukang Sun, Adil Salim, Peter Richtárik
2024	A*	`conf`	Freya PAGE: First Optimal Time Complexity for Large-Scale Nonconvex Finite-Sum Optimization with Heterogeneous Asynchronous Computations. NeurIPS Alexander Tyurin, Kaja Gruntkowska, Peter Richtárik
2024	J	`jnl`	Freya PAGE: First Optimal Time Complexity for Large-Scale Nonconvex Finite-Sum Optimization with Heterogeneous Asynchronous Computations. CoRR Alexander Tyurin, Kaja Gruntkowska, Peter Richtárik
2024	A*	`conf`	High-Probability Convergence for Composite and Distributed Stochastic Minimization and Variational Inequalities with Heavy-Tailed Noise. ICML Eduard Gorbunov, Abdurakhmon Sadiev, Marina Danilova, Samuel Horváth, Gauthier Gidel, Pavel E. Dvurechensky, Alexander V. Gasnikov, Peter Richtárik
2024	A*	`conf`	Improving the Worst-Case Bidirectional Communication Complexity for Nonconvex Distributed Optimization under Function Similarity. NeurIPS Kaja Gruntkowska, Alexander Tyurin, Peter Richtárik
2024	J	`jnl`	Improving the Worst-Case Bidirectional Communication Complexity for Nonconvex Distributed Optimization under Function Similarity. CoRR Kaja Gruntkowska, Alexander Tyurin, Peter Richtárik
2024	J	`jnl`	LoCoDL: Communication-Efficient Distributed Learning with Local Training and Compression. CoRR Laurent Condat, Artavazd Maranjyan, Peter Richtárik
2024	J	`jnl`	MARINA-P: Superior Performance in Non-smooth Federated Optimization with Adaptive Stepsizes. CoRR Igor Sokolov, Peter Richtárik
2024	J	`jnl`	Methods for Convex (L CoRR Eduard Gorbunov, Nazarii Tupitsa, Sayantan Choudhury, Alen Aliev, Peter Richtárik, Samuel Horváth, Martin Takác
2024	J	`jnl`	Methods with Local Steps and Random Reshuffling for Generally Smooth Non-Convex Federated Optimization. CoRR Yury Demidovich, Petr Ostroukhov, Grigory Malinovsky, Samuel Horváth, Martin Takác, Peter Richtárik, Eduard Gorbunov
2024	A*	`conf`	MicroAdam: Accurate Adaptive Optimization with Low Space Overhead and Provable Convergence. NeurIPS Ionut-Vlad Modoranu, Mher Safaryan, Grigory Malinovsky, Eldar Kurtic, Thomas Robert, Peter Richtárik, Dan Alistarh
2024	J	`jnl`	MicroAdam: Accurate Adaptive Optimization with Low Space Overhead and Provable Convergence. CoRR Ionut-Vlad Modoranu, Mher Safaryan, Grigory Malinovsky, Eldar Kurtic, Thomas Robert, Peter Richtárik, Dan Alistarh
2024	J	`jnl`	MindFlayer: Efficient Asynchronous Parallel SGD in the Presence of Heterogeneous and Random Worker Compute Times. CoRR Artavazd Maranjyan, Omar Shaikh Omar, Peter Richtárik
2024	A*	`conf`	Minibatch Stochastic Three Points Method for Unconstrained Smooth Minimization. AAAI Soumia Boucherouite, Grigory Malinovsky, Peter Richtárik, El Houcine Bergou
2024	J	`jnl`	On the Convergence of DP-SGD with Adaptive Clipping. CoRR Egor Shulgin, Peter Richtárik
2024	J	`jnl`	On the Convergence of FedProx with Extrapolation and Inexact Prox. CoRR Hanmin Li, Peter Richtárik
2024	A*	`conf`	On the Optimal Time Complexities in Decentralized Stochastic Asynchronous Optimization. NeurIPS Alexander Tyurin, Peter Richtárik
2024	A*	`conf`	PV-Tuning: Beyond Straight-Through Estimation for Extreme LLM Compression. NeurIPS Vladimir Malinovskii, Denis Mazur, Ivan Ilin, Denis Kuznedelev, Konstantin Burlachenko, Kai Yi, Dan Alistarh, Peter Richtárik
2024	J	`jnl`	PV-Tuning: Beyond Straight-Through Estimation for Extreme LLM Compression. CoRR Vladimir Malinovskii, Denis Mazur, Ivan Ilin, Denis Kuznedelev, Konstantin Burlachenko, Kai Yi, Dan Alistarh, Peter Richtárik
2024	J	`jnl`	Prune at the Clients, Not the Server: Accelerated Sparse Training in Federated Learning. CoRR Georg Meinhardt, Kai Yi, Laurent Condat, Peter Richtárik
2024	J	`jnl`	Pushing the Limits of Large Language Model Quantization via the Linearity Theorem. CoRR Vladimir Malinovskii, Andrei Panferov, Ivan Ilin, Han Guo, Peter Richtárik, Dan Alistarh
2024	J	`jnl`	Randomized Asymmetric Chain of LoRA: The First Meaningful Theoretical Framework for Low-Rank Adaptation. CoRR Grigory Malinovsky, Umberto Michieli, Hasan Abed Al Kader Hammoud, Taha Ceritli, Hayder Elesedy, Mete Ozay, Peter Richtárik
2024	J	`jnl`	SPAM: Stochastic Proximal Point Method with Momentum Variance Reduction for Non-convex Cross-Device Federated Learning. CoRR Avetik G. Karagulyan, Egor Shulgin, Abdurakhmon Sadiev, Peter Richtárik
2024	A*	`conf`	Shadowheart SGD: Distributed Asynchronous SGD with Optimal Time Complexity Under Arbitrary Computation and Communication Heterogeneity. NeurIPS Alexander Tyurin, Marta Pozzi, Ivan Ilin, Peter Richtárik
2024	J	`jnl`	Shadowheart SGD: Distributed Asynchronous SGD with Optimal Time Complexity Under Arbitrary Computation and Communication Heterogeneity. CoRR Alexander Tyurin, Marta Pozzi, Ivan Ilin, Peter Richtárik
2024	J	`jnl`	Streamlining in the Riemannian Realm: Efficient Riemannian Optimization with Loopless Variance Reduction. CoRR Yury Demidovich, Grigory Malinovsky, Peter Richtárik
2024	A*	`conf`	The Power of Extrapolation in Federated Learning. NeurIPS Hanmin Li, Kirill Acharya, Peter Richtárik
2024	J	`jnl`	Tighter Performance Theory of FedExProx. CoRR Wojciech Anyszka, Kaja Gruntkowska, Alexander Tyurin, Peter Richtárik
2024	A*	`conf`	Towards a Better Theoretical Understanding of Independent Subnetwork Training. ICML Egor Shulgin, Peter Richtárik
2024	A	`conf`	Understanding Progressive Training Through the Framework of Randomized Coordinate Descent. AISTATS Rafal Szlendak, Elnur Gasanov, Peter Richtárik
2024	J	`jnl`	Unlocking FedNL: Self-Contained Compute-Optimized Implementation. CoRR Konstantin Burlachenko, Peter Richtárik
2023	A*	`conf`	2Direction: Theoretically Faster Distributed Training with Bidirectional Communication Compression. NeurIPS Alexander Tyurin, Peter Richtárik
2023	A*	`conf`	A Computation and Communication Efficient Method for Distributed Nonconvex Problems in the Partial Participation Setting. NeurIPS Alexander Tyurin, Peter Richtárik
2023	A*	`conf`	A Guide Through the Zoo of Biased SGD. NeurIPS Yury Demidovich, Grigory Malinovsky, Igor Sokolov, Peter Richtárik
2023	J	`jnl`	A Guide Through the Zoo of Biased SGD. CoRR Yury Demidovich, Grigory Malinovsky, Igor Sokolov, Peter Richtárik
2023	J	`jnl`	AI-SARAH: Adaptive and Implicit Stochastic Recursive Gradient Methods. Trans. Mach. Learn. Res. Zheng Shi, Abdurakhmon Sadiev, Nicolas Loizou, Peter Richtárik, Martin Takác
2023	J	`jnl`	Adaptive Compression for Communication-Efficient Distributed Training. Trans. Mach. Learn. Res. Maksim Makarenko, Elnur Gasanov, Abdurakhmon Sadiev, Rustem Islamov, Peter Richtárik
2023	J	`jnl`	Better Theory for SGD in the Nonconvex World. Trans. Mach. Learn. Res. Ahmed Khaled, Peter Richtárik
2023	J	`jnl`	Byzantine Robustness and Partial Participation Can Be Achieved Simultaneously: Just Clip Gradient Differences. CoRR Grigory Malinovsky, Peter Richtárik, Samuel Horváth, Eduard Gorbunov
2023	A	`conf`	Can 5th Generation Local Training Methods Support Client Sampling? Yes! AISTATS Michal Grudzien, Grigory Malinovsky, Peter Richtárik
2023	A	`conf`	Catalyst Acceleration of Error Compensated Methods Leads to Better Communication Complexity. AISTATS Xun Qian, Hanze Dong, Tong Zhang, Peter Richtárik
2023	J	`jnl`	Clip21: Error Feedback for Gradient Clipping. CoRR Sarit Khirirat, Eduard Gorbunov, Samuel Horváth, Rustem Islamov, Fakhri Karray, Peter Richtárik
2023	J	`jnl`	Communication Compression for Byzantine Robust Learning: New Efficient Algorithms and Improved Rates. CoRR Ahmad Rammal, Kaja Gruntkowska, Nikita Fedin, Eduard Gorbunov, Peter Richtárik
2023	J	`jnl`	Convergence of First-Order Algorithms for Meta-Learning with Moreau Envelopes. CoRR Konstantin Mishchenko, Slavomír Hanzely, Peter Richtárik
2023	A	`conf`	Convergence of Stein Variational Gradient Descent under a Weaker Smoothness Condition. AISTATS Lukang Sun, Avetik G. Karagulyan, Peter Richtárik
2023	A*	`conf`	DASHA: Distributed Nonconvex Optimization with Communication Compression and Optimal Oracle Complexity. ICLR Alexander Tyurin, Peter Richtárik
2023	J	`jnl`	Distributed Newton-Type Methods with Communication Compression and Bernoulli Aggregation. Trans. Mach. Learn. Res. Rustem Islamov, Xun Qian, Slavomír Hanzely, Mher Safaryan, Peter Richtárik
2023	A*	`conf`	EF21-P and Friends: Improved Theoretical Communication Complexity for Distributed Optimization with Bidirectional Compression. ICML Kaja Gruntkowska, Alexander Tyurin, Peter Richtárik
2023	J	`jnl`	ELF: Federated Langevin Algorithms with Primal, Dual and Bidirectional Compression. CoRR Avetik G. Karagulyan, Peter Richtárik
2023	J	`jnl`	Error Feedback Shines when Features are Rare. CoRR Peter Richtárik, Elnur Gasanov, Konstantin Burlachenko
2023	J	`jnl`	Explicit Personalization and Local Training: Double Communication Acceleration in Federated Learning. CoRR Kai Yi, Laurent Condat, Peter Richtárik
2023	—	`conf`	Federated Learning is Better with Non-Homomorphic Encryption. DistributedML@CoNEXT Konstantin Burlachenko, Abdulmajeed Alrowithi, Fahad Ali Albalawi, Peter Richtárik
2023	J	`jnl`	Federated Learning is Better with Non-Homomorphic Encryption. CoRR Konstantin Burlachenko, Abdulmajeed Alrowithi, Fahad Ali Albalawi, Peter Richtárik
2023	J	`jnl`	Federated Learning with Regularized Client Participation. CoRR Grigory Malinovsky, Samuel Horváth, Konstantin Burlachenko, Peter Richtárik
2023	J	`jnl`	Global-QSGD: Practical Floatless Quantization for Distributed Learning with Theoretical Guarantees. CoRR Jihao Xin, Marco Canini, Peter Richtárik, Samuel Horváth
2023	A*	`conf`	High-Probability Bounds for Stochastic Optimization and Variational Inequalities: the Case of Unbounded Variance. ICML Abdurakhmon Sadiev, Marina Danilova, Eduard Gorbunov, Samuel Horváth, Gauthier Gidel, Pavel E. Dvurechensky, Alexander V. Gasnikov, Peter Richtárik
2023	J	`jnl`	High-Probability Bounds for Stochastic Optimization and Variational Inequalities: the Case of Unbounded Variance. CoRR Abdurakhmon Sadiev, Marina Danilova, Eduard Gorbunov, Samuel Horváth, Gauthier Gidel, Pavel E. Dvurechensky, Alexander V. Gasnikov, Peter Richtárik
2023	J	`jnl`	High-Probability Convergence for Composite and Distributed Stochastic Minimization and Variational Inequalities with Heavy-Tailed Noise. CoRR Eduard Gorbunov, Abdurakhmon Sadiev, Marina Danilova, Samuel Horváth, Gauthier Gidel, Pavel E. Dvurechensky, Alexander V. Gasnikov, Peter Richtárik
2023	J	`jnl`	Improving Accelerated Federated Learning with Compression and Importance Sampling. CoRR Michal Grudzien, Grigory Malinovsky, Peter Richtárik
2023	—	`conf`	Kimad: Adaptive Gradient Compression with Bandwidth Awareness. DistributedML@CoNEXT Jihao Xin, Ivan Ilin, Shunkang Zhang, Marco Canini, Peter Richtárik
2023	J	`jnl`	Kimad: Adaptive Gradient Compression with Bandwidth Awareness. CoRR Jihao Xin, Ivan Ilin, Shunkang Zhang, Marco Canini, Peter Richtárik
2023	J	`jnl`	MAST: Model-Agnostic Sparsified Training. CoRR Yury Demidovich, Grigory Malinovsky, Egor Shulgin, Peter Richtárik
2023	A*	`conf`	Momentum Provably Improves Error Feedback! NeurIPS Ilyas Fatkhullin, Alexander Tyurin, Peter Richtárik
2023	J	`jnl`	Momentum Provably Improves Error Feedback! CoRR Ilyas Fatkhullin, Alexander Tyurin, Peter Richtárik
2023	J	`jnl`	On Biased Compression for Distributed Learning. J. Mach. Learn. Res. Aleksandr Beznosikov, Samuel Horváth, Peter Richtárik, Mher Safaryan
2023	A*	`conf`	Optimal Time Complexities of Parallel Stochastic Optimization Methods Under a Fixed Computation Model. NeurIPS Alexander Tyurin, Peter Richtárik
2023	J	`jnl`	Personalized Federated Learning with Communication Compression. Trans. Mach. Learn. Res. El Houcine Bergou, Konstantin Burlachenko, Aritra Dutta, Peter Richtárik
2023	A*	`conf`	RandProx: Primal-Dual Optimization Algorithms with Randomized Proximal Updates. ICLR Laurent Condat, Peter Richtárik
2023	A	`conf`	Random Reshuffling with Variance Reduction: New Analysis and Better Rates. UAI Grigory Malinovsky, Alibek Sailanbayev, Peter Richtárik
2023	—	`conf`	Server-Side Stepsizes and Sampling Without Replacement Provably Help in Federated Optimization. DistributedML@CoNEXT Grigory Malinovsky, Konstantin Mishchenko, Peter Richtárik
2023	J	`jnl`	Sharper Rates and Flexible Framework for Nonconvex SGD with Client and Data Sampling. Trans. Mach. Learn. Res. Alexander Tyurin, Lukang Sun, Konstantin Burlachenko, Peter Richtárik
2023	J	`jnl`	Stochastic distributed learning with gradient quantization and double-variance reduction. Optim. Methods Softw. Samuel Horváth, Dmitry Kovalev, Konstantin Mishchenko, Peter Richtárik, Sebastian U. Stich
2023	J	`jnl`	TAMUNA: Accelerated Federated Learning with Local Training and Partial Participation. CoRR Laurent Condat, Grigory Malinovsky, Peter Richtárik
2023	J	`jnl`	Towards a Better Theoretical Understanding of Independent Subnetwork Training. CoRR Egor Shulgin, Peter Richtárik
2023	J	`jnl`	Understanding Progressive Training Through the Framework of Randomized Coordinate Descent. CoRR Rafal Szlendak, Elnur Gasanov, Peter Richtárik
2023	J	`jnl`	Unified Analysis of Stochastic Gradient Methods for Composite Convex and Smooth Optimization. J. Optim. Theory Appl. Ahmed Khaled, Othmane Sebbouh, Nicolas Loizou, Robert M. Gower, Peter Richtárik
2023	A*	`conf`	Variance Reduction is an Antidote to Byzantines: Better Rates, Weaker Assumptions and Communication Compression as a Cherry on the Top. ICLR Eduard Gorbunov, Samuel Horváth, Peter Richtárik, Gauthier Gidel
2022	A*	`conf`	3PC: Three Point Compressors for Communication-Efficient Distributed Training and a Better Theory for Lazy Aggregation. ICML Peter Richtárik, Igor Sokolov, Elnur Gasanov, Ilyas Fatkhullin, Zhize Li, Eduard Gorbunov
2022	J	`jnl`	3PC: Three Point Compressors for Communication-Efficient Distributed Training and a Better Theory for Lazy Aggregation. CoRR Peter Richtárik, Igor Sokolov, Ilyas Fatkhullin, Elnur Gasanov, Zhize Li, Eduard Gorbunov
2022	J	`jnl`	A Computation and Communication Efficient Method for Distributed Nonconvex Problems in the Partial Participation Setting. CoRR Alexander Tyurin, Peter Richtárik
2022	A*	`conf`	A Convergence Theory for SVGD in the Population Limit under Talagrand's Inequality T1. ICML Adil Salim, Lukang Sun, Peter Richtárik
2022	A*	`conf`	A Damped Newton Method Achieves Global $\mathcal O \left(\frac{1}{k^2}\right)$ and Local Quadratic Convergence Rate. NeurIPS Slavomír Hanzely, Dmitry Kamzolov, Dmitry Pasechnyuk, Alexander V. Gasnikov, Peter Richtárik, Martin Takác
2022	J	`jnl`	A Note on the Convergence of Mirrored Stein Variational Gradient Descent under (L CoRR Lukang Sun, Peter Richtárik
2022	A*	`conf`	Accelerated Primal-Dual Gradient Method for Smooth and Convex-Concave Saddle-Point Problems with Bilinear Coupling. NeurIPS Dmitry Kovalev, Alexander V. Gasnikov, Peter Richtárik
2022	J	`jnl`	Adaptive Compression for Communication-Efficient Distributed Training. CoRR Maksim Makarenko, Elnur Gasanov, Rustem Islamov, Abdurakhmon Sadiev, Peter Richtárik
2022	J	`jnl`	Adaptive Learning Rates for Faster Stochastic Gradient Methods. CoRR Samuel Horváth, Konstantin Mishchenko, Peter Richtárik
2022	J	`jnl`	Adaptivity of Stochastic Gradient Methods for Nonconvex Optimization. SIAM J. Math. Data Sci. Samuel Horváth, Lihua Lei, Peter Richtárik, Michael I. Jordan
2022	A	`conf`	An Optimal Algorithm for Strongly Convex Minimization under Affine Constraints. AISTATS Adil Salim, Laurent Condat, Dmitry Kovalev, Peter Richtárik
2022	A*	`conf`	BEER: Fast $O(1/T)$ Rate for Decentralized Nonconvex Optimization with Communication Compression. NeurIPS Haoyu Zhao, Boyue Li, Zhize Li, Peter Richtárik, Yuejie Chi
2022	J	`jnl`	BEER: Fast O(1/T) Rate for Decentralized Nonconvex Optimization with Communication Compression. CoRR Haoyu Zhao, Boyue Li, Zhize Li, Peter Richtárik, Yuejie Chi
2022	A	`conf`	Basis Matters: Better Communication-Efficient Second Order Methods for Federated Learning. AISTATS Xun Qian, Rustem Islamov, Mher Safaryan, Peter Richtárik
2022	J	`jnl`	Can 5 CoRR Michal Grudzien, Grigory Malinovsky, Peter Richtárik
2022	J	`jnl`	Certified Robustness in Federated Learning. CoRR Motasem Alfarra, Juan C. Pérez, Egor Shulgin, Peter Richtárik, Bernard Ghanem
2022	J	`jnl`	Communication Acceleration of Local Gradient Methods via an Accelerated Primal-Dual Algorithm with Inexact Prox. CoRR Abdurakhmon Sadiev, Dmitry Kovalev, Peter Richtárik
2022	A*	`conf`	Communication Acceleration of Local Gradient Methods via an Accelerated Primal-Dual Algorithm with an Inexact Prox. NeurIPS Abdurakhmon Sadiev, Dmitry Kovalev, Peter Richtárik
2022	J	`jnl`	Convergence of Stein Variational Gradient Descent under a Weaker Smoothness Condition. CoRR Lukang Sun, Avetik G. Karagulyan, Peter Richtárik
2022	J	`jnl`	DASHA: Distributed Nonconvex Optimization with Communication Compression, Optimal Oracle Complexity, and No Client Synchronization. CoRR Alexander Tyurin, Peter Richtárik
2022	J	`jnl`	Direct nonlinear acceleration. EURO J. Comput. Optim. Aritra Dutta, El Houcine Bergou, Yunming Xiao, Marco Canini, Peter Richtárik
2022	A*	`conf`	Distributed Methods with Compressed Communication for Solving Variational Inequalities, with Theoretical Guarantees. NeurIPS Aleksandr Beznosikov, Peter Richtárik, Michael Diskin, Max Ryabinin, Alexander V. Gasnikov
2022	J	`jnl`	Distributed Newton-Type Methods with Communication Compression and Bernoulli Aggregation. CoRR Rustem Islamov, Xun Qian, Slavomír Hanzely, Mher Safaryan, Peter Richtárik
2022	A*	`conf`	Doubly Adaptive Scaled Algorithm for Machine Learning Using Second-Order Information. ICLR Majid Jahani, Sergey Rusakov, Zheng Shi, Peter Richtárik, Michael W. Mahoney, Martin Takác
2022	J	`jnl`	Dualize, Split, Randomize: Toward Fast Nonsmooth Optimization Algorithms. J. Optim. Theory Appl. Adil Salim, Laurent Condat, Konstantin Mishchenko, Peter Richtárik
2022	A*	`conf`	EF-BV: A Unified Theory of Error Feedback and Variance Reduction Mechanisms for Biased and Unbiased Compression in Distributed Optimization. NeurIPS Laurent Condat, Kai Yi, Peter Richtárik
2022	J	`jnl`	EF-BV: A Unified Theory of Error Feedback and Variance Reduction Mechanisms for Biased and Unbiased Compression in Distributed Optimization. CoRR Laurent Condat, Kai Yi, Peter Richtárik
2022	J	`jnl`	EF21-P and Friends: Improved Theoretical Communication Complexity for Distributed Optimization with Bidirectional Compression. CoRR Kaja Gruntkowska, Alexander Tyurin, Peter Richtárik
2022	A	`conf`	FLIX: A Simple and Communication-Efficient Alternative to Local Methods in Federated Learning. AISTATS Elnur Gasanov, Ahmed Khaled, Samuel Horváth, Peter Richtárik
2022	J	`jnl`	FL_PyTorch: optimization research simulator for federated learning. CoRR Konstantin Burlachenko, Samuel Horváth, Peter Richtárik
2022	A*	`conf`	FedNL: Making Newton-Type Methods Applicable to Federated Learning. ICML Mher Safaryan, Rustem Islamov, Xun Qian, Peter Richtárik
2022	J	`jnl`	FedShuffle: Recipes for Better Use of Local Work in Federated Learning. CoRR Samuel Horváth, Maziar Sanjabi, Lin Xiao, Peter Richtárik, Michael G. Rabbat
2022	J	`jnl`	FedShuffle: Recipes for Better Use of Local Work in Federated Learning. Trans. Mach. Learn. Res. Samuel Horváth, Maziar Sanjabi, Lin Xiao, Peter Richtárik, Michael G. Rabbat
2022	J	`jnl`	Federated Learning with a Sampling Algorithm under Isoperimetry. CoRR Lukang Sun, Adil Salim, Peter Richtárik
2022	J	`jnl`	Federated Optimization Algorithms with Random Reshuffling and Gradient Compression. CoRR Abdurakhmon Sadiev, Grigory Malinovsky, Eduard Gorbunov, Igor Sokolov, Ahmed Khaled, Konstantin Burlachenko, Peter Richtárik
2022	J	`jnl`	Federated Random Reshuffling with Compression and Variance Reduction. CoRR Grigory Malinovsky, Peter Richtárik
2022	J	`jnl`	GradSkip: Communication-Accelerated Local Gradient Methods with Better Computational Complexity. CoRR Artavazd Maranjyan, Mher Safaryan, Peter Richtárik
2022	J	`jnl`	Improved Stein Variational Gradient Descent with Importance Weights. CoRR Lukang Sun, Peter Richtárik
2022	A*	`conf`	IntSGD: Adaptive Floatless Compression of Stochastic Gradients. ICLR Konstantin Mishchenko, Bokun Wang, Dmitry Kovalev, Peter Richtárik
2022	—	`conf`	MURANA: A Generic Framework for Stochastic Variance-Reduced Optimization. MSML Laurent Condat, Peter Richtárik
2022	J	`jnl`	Minibatch Stochastic Three Points Method for Unconstrained Smooth Minimization. CoRR Soumia Boucherouite, Grigory Malinovsky, Peter Richtárik, El Houcine Bergou
2022	—	`conf`	Natural Compression for Distributed Deep Learning. MSML Samuel Horváth, Chen-Yu Ho, Ludovit Horvath, Atal Narayan Sahu, Marco Canini, Peter Richtárik
2022	A*	`conf`	Optimal Algorithms for Decentralized Stochastic Variational Inequalities. NeurIPS Dmitry Kovalev, Aleksandr Beznosikov, Abdurakhmon Sadiev, Michael Persiianov, Peter Richtárik, Alexander V. Gasnikov
2022	J	`jnl`	Optimal Algorithms for Decentralized Stochastic Variational Inequalities. CoRR Dmitry Kovalev, Aleksandr Beznosikov, Abdurakhmon Sadiev, Michael Persiianov, Peter Richtárik, Alexander V. Gasnikov
2022	J	`jnl`	Optimal Client Sampling for Federated Learning. Trans. Mach. Learn. Res. Wenlin Chen, Samuel Horváth, Peter Richtárik
2022	A*	`conf`	Permutation Compressors for Provably Faster Distributed Nonconvex Optimization. ICLR Rafal Szlendak, Alexander Tyurin, Peter Richtárik
2022	J	`jnl`	Personalized Federated Learning with Communication Compression. CoRR El Houcine Bergou, Konstantin Burlachenko, Aritra Dutta, Peter Richtárik
2022	J	`jnl`	Provably Doubly Accelerated Federated Learning: The First Theoretically Successful Combination of Local Training and Compressed Communication. CoRR Laurent Condat, Ivan Agarský, Peter Richtárik
2022	A*	`conf`	ProxSkip: Yes! Local Gradient Steps Provably Lead to Communication Acceleration! Finally! ICML Konstantin Mishchenko, Grigory Malinovsky, Sebastian U. Stich, Peter Richtárik
2022	J	`jnl`	ProxSkip: Yes! Local Gradient Steps Provably Lead to Communication Acceleration! Finally! CoRR Konstantin Mishchenko, Grigory Malinovsky, Sebastian U. Stich, Peter Richtárik
2022	A*	`conf`	Proximal and Federated Random Reshuffling. ICML Konstantin Mishchenko, Ahmed Khaled, Peter Richtárik
2022	J	`jnl`	Quasi-Newton methods for machine learning: forget the past, just sample. Optim. Methods Softw. Albert S. Berahas, Majid Jahani, Peter Richtárik, Martin Takác
2022	J	`jnl`	Server-Side Stepsizes and Sampling Without Replacement Provably Help in Federated Optimization. CoRR Grigory Malinovsky, Konstantin Mishchenko, Peter Richtárik
2022	J	`jnl`	Sharper Rates and Flexible Framework for Nonconvex SGD with Client and Data Sampling. CoRR Alexander Tyurin, Lukang Sun, Konstantin Burlachenko, Peter Richtárik
2022	J	`jnl`	Shifted Compression Framework: Generalizations and Improvements. CoRR Egor Shulgin, Peter Richtárik
2022	A	`conf`	Shifted compression framework: generalizations and improvements. UAI Egor Shulgin, Peter Richtárik
2022	A*	`conf`	Theoretically Better and Numerically Faster Distributed Optimization with Smoothness-Aware Quantization Techniques. NeurIPS Bokun Wang, Mher Safaryan, Peter Richtárik
2022	A*	`conf`	Variance Reduced ProxSkip: Algorithm, Theory and Application to Federated Learning. NeurIPS Grigory Malinovsky, Kai Yi, Peter Richtárik
2022	J	`jnl`	Variance Reduced ProxSkip: Algorithm, Theory and Application to Federated Learning. CoRR Grigory Malinovsky, Kai Yi, Peter Richtárik
2022	J	`jnl`	Variance Reduction is an Antidote to Byzantines: Better Rates, Weaker Assumptions and Communication Compression as a Cherry on the Top. CoRR Eduard Gorbunov, Samuel Horváth, Peter Richtárik, Gauthier Gidel
2021	A*	`conf`	A Better Alternative to Error Feedback for Communication-Efficient Distributed Learning. ICLR Samuel Horváth, Peter Richtárik
2021	J	`jnl`	A Field Guide to Federated Optimization. CoRR 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
2021	A	`conf`	A Linearly Convergent Algorithm for Decentralized Optimization: Sending Less Bits for Free! AISTATS Dmitry Kovalev, Anastasia Koloskova, Martin Jaggi, Peter Richtárik, Sebastian U. Stich
2021	A*	`conf`	ADOM: Accelerated Decentralized Optimization Method for Time-Varying Networks. ICML Dmitry Kovalev, Egor Shulgin, Peter Richtárik, Alexander Rogozin, Alexander V. Gasnikov
2021	J	`jnl`	ADOM: Accelerated Decentralized Optimization Method for Time-Varying Networks. CoRR Dmitry Kovalev, Egor Shulgin, Peter Richtárik, Alexander Rogozin, Alexander V. Gasnikov
2021	J	`jnl`	AI-SARAH: Adaptive and Implicit Stochastic Recursive Gradient Methods. CoRR Zheng Shi, Nicolas Loizou, Peter Richtárik, Martin Takác
2021	J	`jnl`	Accelerated Bregman proximal gradient methods for relatively smooth convex optimization. Comput. Optim. Appl. Filip Hanzely, Peter Richtárik, Lin Xiao
2021	J	`jnl`	Accelerated Primal-Dual Gradient Method for Smooth and Convex-Concave Saddle-Point Problems with Bilinear Coupling. CoRR Dmitry Kovalev, Alexander V. Gasnikov, Peter Richtárik
2021	J	`jnl`	Basis Matters: Better Communication-Efficient Second Order Methods for Federated Learning. CoRR Xun Qian, Rustem Islamov, Mher Safaryan, Peter Richtárik
2021	A*	`conf`	CANITA: Faster Rates for Distributed Convex Optimization with Communication Compression. NeurIPS Zhize Li, Peter Richtárik
2021	J	`jnl`	CANITA: Faster Rates for Distributed Convex Optimization with Communication Compression. CoRR Zhize Li, Peter Richtárik
2021	J	`jnl`	Complexity Analysis of Stein Variational Gradient Descent Under Talagrand's Inequality T1. CoRR Adil Salim, Lukang Sun, Peter Richtárik
2021	J	`jnl`	Distributed Methods with Compressed Communication for Solving Variational Inequalities, with Theoretical Guarantees. CoRR Aleksandr Beznosikov, Peter Richtárik, Michael Diskin, Max Ryabinin, Alexander V. Gasnikov
2021	A*	`conf`	Distributed Second Order Methods with Fast Rates and Compressed Communication. ICML Rustem Islamov, Xun Qian, Peter Richtárik
2021	J	`jnl`	Distributed Second Order Methods with Fast Rates and Compressed Communication. CoRR Rustem Islamov, Xun Qian, Peter Richtárik
2021	J	`jnl`	Doubly Adaptive Scaled Algorithm for Machine Learning Using Second-Order Information. CoRR Majid Jahani, Sergey Rusakov, Zheng Shi, Peter Richtárik, Michael W. Mahoney, Martin Takác
2021	J	`jnl`	EF21 with Bells & Whistles: Practical Algorithmic Extensions of Modern Error Feedback. CoRR Ilyas Fatkhullin, Igor Sokolov, Eduard Gorbunov, Zhize Li, Peter Richtárik
2021	A*	`conf`	EF21: A New, Simpler, Theoretically Better, and Practically Faster Error Feedback. NeurIPS Peter Richtárik, Igor Sokolov, Ilyas Fatkhullin
2021	J	`jnl`	EF21: A New, Simpler, Theoretically Better, and Practically Faster Error Feedback. CoRR Peter Richtárik, Igor Sokolov, Ilyas Fatkhullin
2021	A*	`conf`	Error Compensated Distributed SGD Can Be Accelerated. NeurIPS Xun Qian, Peter Richtárik, Tong Zhang
2021	J	`jnl`	FLIX: A Simple and Communication-Efficient Alternative to Local Methods in Federated Learning. CoRR Elnur Gasanov, Ahmed Khaled, Samuel Horváth, Peter Richtárik
2021	—	`conf`	FL_PyTorch: optimization research simulator for federated learning. DistributedML@CoNEXT Konstantin Burlachenko, Samuel Horváth, Peter Richtárik
2021	J	`jnl`	Faster Rates for Compressed Federated Learning with Client-Variance Reduction. CoRR Haoyu Zhao, Konstantin Burlachenko, Zhize Li, Peter Richtárik
2021	J	`jnl`	Fastest rates for stochastic mirror descent methods. Comput. Optim. Appl. Filip Hanzely, Peter Richtárik
2021	J	`jnl`	FedNL: Making Newton-Type Methods Applicable to Federated Learning. CoRR Mher Safaryan, Rustem Islamov, Xun Qian, Peter Richtárik
2021	J	`jnl`	FedPAGE: A Fast Local Stochastic Gradient Method for Communication-Efficient Federated Learning. CoRR Haoyu Zhao, Zhize Li, Peter Richtárik
2021	A	`conf`	Hyperparameter Transfer Learning with Adaptive Complexity. AISTATS Samuel Horváth, Aaron Klein, Peter Richtárik, Cédric Archambeau
2021	J	`jnl`	Hyperparameter Transfer Learning with Adaptive Complexity. CoRR Samuel Horváth, Aaron Klein, Peter Richtárik, Cédric Archambeau
2021	J	`jnl`	IntSGD: Floatless Compression of Stochastic Gradients. CoRR Konstantin Mishchenko, Bokun Wang, Dmitry Kovalev, Peter Richtárik
2021	J	`jnl`	L-SVRG and L-Katyusha with Arbitrary Sampling. J. Mach. Learn. Res. Xun Qian, Zheng Qu, Peter Richtárik
2021	A	`conf`	Local SGD: Unified Theory and New Efficient Methods. AISTATS Eduard Gorbunov, Filip Hanzely, Peter Richtárik
2021	A*	`conf`	Lower Bounds and Optimal Algorithms for Smooth and Strongly Convex Decentralized Optimization Over Time-Varying Networks. NeurIPS Dmitry Kovalev, Elnur Gasanov, Alexander V. Gasnikov, Peter Richtárik
2021	J	`jnl`	Lower Bounds and Optimal Algorithms for Smooth and Strongly Convex Decentralized Optimization Over Time-Varying Networks. CoRR Dmitry Kovalev, Elnur Gasanov, Peter Richtárik, Alexander V. Gasnikov
2021	A*	`conf`	MARINA: Faster Non-Convex Distributed Learning with Compression. ICML Eduard Gorbunov, Konstantin Burlachenko, Zhize Li, Peter Richtárik
2021	J	`jnl`	MARINA: Faster Non-Convex Distributed Learning with Compression. CoRR Eduard Gorbunov, Konstantin Burlachenko, Zhize Li, Peter Richtárik
2021	J	`jnl`	MURANA: A Generic Framework for Stochastic Variance-Reduced Optimization. CoRR Laurent Condat, Peter Richtárik
2021	A*	`conf`	PAGE: A Simple and Optimal Probabilistic Gradient Estimator for Nonconvex Optimization. ICML Zhize Li, Hongyan Bao, Xiangliang Zhang, Peter Richtárik
2021	J	`jnl`	Permutation Compressors for Provably Faster Distributed Nonconvex Optimization. CoRR Rafal Szlendak, Alexander Tyurin, Peter Richtárik
2021	J	`jnl`	Proximal and Federated Random Reshuffling. CoRR Konstantin Mishchenko, Ahmed Khaled, Peter Richtárik
2021	J	`jnl`	Random Reshuffling with Variance Reduction: New Analysis and Better Rates. CoRR Grigory Malinovsky, Alibek Sailanbayev, Peter Richtárik
2021	J	`jnl`	Revisiting Randomized Gossip Algorithms: General Framework, Convergence Rates and Novel Block and Accelerated Protocols. IEEE Trans. Inf. Theory Nicolas Loizou, Peter Richtárik
2021	Misc	`conf`	Scaling Distributed Machine Learning with In-Network Aggregation. NSDI Amedeo Sapio, Marco Canini, Chen-Yu Ho, Jacob Nelson, Panos Kalnis, Changhoon Kim, Arvind Krishnamurthy, Masoud Moshref, Dan R. K. Ports, Peter Richtárik
2021	A*	`conf`	Smoothness Matrices Beat Smoothness Constants: Better Communication Compression Techniques for Distributed Optimization. NeurIPS Mher Safaryan, Filip Hanzely, Peter Richtárik
2021	J	`jnl`	Smoothness Matrices Beat Smoothness Constants: Better Communication Compression Techniques for Distributed Optimization. CoRR Mher Safaryan, Filip Hanzely, Peter Richtárik
2021	J	`jnl`	Smoothness-Aware Quantization Techniques. CoRR Bokun Wang, Mher Safaryan, Peter Richtárik
2021	A*	`conf`	Stochastic Sign Descent Methods: New Algorithms and Better Theory. ICML Mher Safaryan, Peter Richtárik
2021	J	`jnl`	Stochastic quasi-gradient methods: variance reduction via Jacobian sketching. Math. Program. Robert M. Gower, Peter Richtárik, Francis R. Bach
2021	J	`jnl`	ZeroSARAH: Efficient Nonconvex Finite-Sum Optimization with Zero Full Gradient Computation. CoRR Zhize Li, Peter Richtárik
2020	A	`conf`	99% of Worker-Master Communication in Distributed Optimization Is Not Needed. UAI Konstantin Mishchenko, Filip Hanzely, Peter Richtárik
2020	J	`jnl`	A Better Alternative to Error Feedback for Communication-Efficient Distributed Learning. CoRR Samuel Horváth, Peter Richtárik
2020	J	`jnl`	A Linearly Convergent Algorithm for Decentralized Optimization: Sending Less Bits for Free! CoRR Dmitry Kovalev, Anastasia Koloskova, Martin Jaggi, Peter Richtárik, Sebastian U. Stich
2020	A*	`conf`	A Stochastic Derivative Free Optimization Method with Momentum. ICLR Eduard Gorbunov, Adel Bibi, Ozan Sener, El Houcine Bergou, Peter Richtárik
2020	A*	`conf`	A Stochastic Derivative-Free Optimization Method with Importance Sampling: Theory and Learning to Control. AAAI Adel Bibi, El Houcine Bergou, Ozan Sener, Bernard Ghanem, Peter Richtárik
2020	J	`jnl`	A Unified Analysis of Stochastic Gradient Methods for Nonconvex Federated Optimization. CoRR Zhize Li, Peter Richtárik
2020	A	`conf`	A Unified Theory of SGD: Variance Reduction, Sampling, Quantization and Coordinate Descent. AISTATS Eduard Gorbunov, Filip Hanzely, Peter Richtárik
2020	A*	`conf`	Acceleration for Compressed Gradient Descent in Distributed and Federated Optimization. ICML Zhize Li, Dmitry Kovalev, Xun Qian, Peter Richtárik
2020	J	`jnl`	Acceleration for Compressed Gradient Descent in Distributed and Federated Optimization. CoRR Zhize Li, Dmitry Kovalev, Xun Qian, Peter Richtárik
2020	J	`jnl`	Adaptive Learning of the Optimal Mini-Batch Size of SGD. CoRR Motasem Alfarra, Slavomír Hanzely, Alyazeed Albasyoni, Bernard Ghanem, Peter Richtárik
2020	J	`jnl`	Adaptivity of Stochastic Gradient Methods for Nonconvex Optimization. CoRR Samuel Horváth, Lihua Lei, Peter Richtárik, Michael I. Jordan
2020	J	`jnl`	Best Pair Formulation & Accelerated Scheme for Non-Convex Principal Component Pursuit. IEEE Trans. Signal Process. Aritra Dutta, Filip Hanzely, Jingwei Liang, Peter Richtárik
2020	J	`jnl`	Better Theory for SGD in the Nonconvex World. CoRR Ahmed Khaled, Peter Richtárik
2020	J	`jnl`	Convergence Analysis of Inexact Randomized Iterative Methods. SIAM J. Sci. Comput. Nicolas Loizou, Peter Richtárik
2020	J	`jnl`	Distributed Proximal Splitting Algorithms with Rates and Acceleration. CoRR Laurent Condat, Grigory Malinovsky, Peter Richtárik
2020	B	`conf`	Don't Jump Through Hoops and Remove Those Loops: SVRG and Katyusha are Better Without the Outer Loop. ALT Dmitry Kovalev, Samuel Horváth, Peter Richtárik
2020	J	`jnl`	Dualize, Split, Randomize: Fast Nonsmooth Optimization Algorithms. CoRR Adil Salim, Laurent Condat, Konstantin Mishchenko, Peter Richtárik
2020	J	`jnl`	Fast Linear Convergence of Randomized BFGS. CoRR Dmitry Kovalev, Robert M. Gower, Peter Richtárik, Alexander Rogozin
2020	J	`jnl`	Federated Learning of a Mixture of Global and Local Models. CoRR Filip Hanzely, Peter Richtárik
2020	J	`jnl`	From Local SGD to Local Fixed Point Methods for Federated Learning. CoRR Grigory Malinovsky, Dmitry Kovalev, Elnur Gasanov, Laurent Condat, Peter Richtárik
2020	A*	`conf`	From Local SGD to Local Fixed-Point Methods for Federated Learning. ICML Grigory Malinovskiy, Dmitry Kovalev, Elnur Gasanov, Laurent Condat, Peter Richtárik
2020	A*	`conf`	Linearly Converging Error Compensated SGD. NeurIPS Eduard Gorbunov, Dmitry Kovalev, Dmitry Makarenko, Peter Richtárik
2020	J	`jnl`	Linearly Converging Error Compensated SGD. CoRR Eduard Gorbunov, Dmitry Kovalev, Dmitry Makarenko, Peter Richtárik
2020	J	`jnl`	Local SGD: Unified Theory and New Efficient Methods. CoRR Eduard Gorbunov, Filip Hanzely, Peter Richtárik
2020	A*	`conf`	Lower Bounds and Optimal Algorithms for Personalized Federated Learning. NeurIPS Filip Hanzely, Slavomír Hanzely, Samuel Horváth, Peter Richtárik
2020	J	`jnl`	Lower Bounds and Optimal Algorithms for Personalized Federated Learning. CoRR Filip Hanzely, Slavomír Hanzely, Samuel Horváth, Peter Richtárik
2020	J	`jnl`	Momentum and stochastic momentum for stochastic gradient, Newton, proximal point and subspace descent methods. Comput. Optim. Appl. Nicolas Loizou, Peter Richtárik
2020	J	`jnl`	On Biased Compression for Distributed Learning. CoRR Aleksandr Beznosikov, Samuel Horváth, Peter Richtárik, Mher Safaryan
2020	J	`jnl`	On the Convergence Analysis of Asynchronous SGD for Solving Consistent Linear Systems. CoRR Atal Narayan Sahu, Aritra Dutta, Aashutosh Tiwari, Peter Richtárik
2020	J	`jnl`	Optimal Client Sampling for Federated Learning. CoRR Wenlin Chen, Samuel Horváth, Peter Richtárik
2020	J	`jnl`	Optimal Gradient Compression for Distributed and Federated Learning. CoRR Alyazeed Albasyoni, Mher Safaryan, Laurent Condat, Peter Richtárik
2020	A*	`conf`	Optimal and Practical Algorithms for Smooth and Strongly Convex Decentralized Optimization. NeurIPS Dmitry Kovalev, Adil Salim, Peter Richtárik
2020	J	`jnl`	PAGE: A Simple and Optimal Probabilistic Gradient Estimator for Nonconvex Optimization. CoRR Zhize Li, Hongyan Bao, Xiangliang Zhang, Peter Richtárik
2020	A*	`conf`	Primal Dual Interpretation of the Proximal Stochastic Gradient Langevin Algorithm. NeurIPS Adil Salim, Peter Richtárik
2020	J	`jnl`	Primal Dual Interpretation of the Proximal Stochastic Gradient Langevin Algorithm. CoRR Adil Salim, Peter Richtárik
2020	A*	`conf`	Random Reshuffling: Simple Analysis with Vast Improvements. NeurIPS Konstantin Mishchenko, Ahmed Khaled, Peter Richtárik
2020	J	`jnl`	Random Reshuffling: Simple Analysis with Vast Improvements. CoRR Konstantin Mishchenko, Ahmed Khaled, Peter Richtárik
2020	A	`conf`	Revisiting Stochastic Extragradient. AISTATS Konstantin Mishchenko, Dmitry Kovalev, Egor Shulgin, Peter Richtárik, Yura Malitsky
2020	J	`jnl`	Stochastic Reformulations of Linear Systems: Algorithms and Convergence Theory. SIAM J. Matrix Anal. Appl. Peter Richtárik, Martin Takác
2020	A*	`conf`	Stochastic Subspace Cubic Newton Method. ICML Filip Hanzely, Nikita Doikov, Yurii E. Nesterov, Peter Richtárik
2020	J	`jnl`	Stochastic Subspace Cubic Newton Method. CoRR Filip Hanzely, Nikita Doikov, Peter Richtárik, Yurii E. Nesterov
2020	J	`jnl`	Stochastic Three Points Method for Unconstrained Smooth Minimization. SIAM J. Optim. El Houcine Bergou, Eduard Gorbunov, Peter Richtárik
2020	A	`conf`	Tighter Theory for Local SGD on Identical and Heterogeneous Data. AISTATS Ahmed Khaled, Konstantin Mishchenko, Peter Richtárik
2020	J	`jnl`	Uncertainty Principle for Communication Compression in Distributed and Federated Learning and the Search for an Optimal Compressor. CoRR Mher Safaryan, Egor Shulgin, Peter Richtárik
2020	J	`jnl`	Unified Analysis of Stochastic Gradient Methods for Composite Convex and Smooth Optimization. CoRR Ahmed Khaled, Othmane Sebbouh, Nicolas Loizou, Robert M. Gower, Peter Richtárik
2020	A*	`conf`	Variance Reduced Coordinate Descent with Acceleration: New Method With a Surprising Application to Finite-Sum Problems. ICML Filip Hanzely, Dmitry Kovalev, Peter Richtárik
2020	J	`jnl`	Variance Reduced Coordinate Descent with Acceleration: New Method With a Surprising Application to Finite-Sum Problems. CoRR Filip Hanzely, Dmitry Kovalev, Peter Richtárik
2020	J	`jnl`	Variance-Reduced Methods for Machine Learning. CoRR Robert M. Gower, Mark Schmidt, Francis R. Bach, Peter Richtárik
2020	J	`jnl`	Variance-Reduced Methods for Machine Learning. Proc. IEEE Robert M. Gower, Mark Schmidt, Francis R. Bach, Peter Richtárik
2019	J	`jnl`	99% of Parallel Optimization is Inevitably a Waste of Time. CoRR Konstantin Mishchenko, Filip Hanzely, Peter Richtárik
2019	A*	`conf`	A Nonconvex Projection Method for Robust PCA. AAAI Aritra Dutta, Filip Hanzely, Peter Richtárik
2019	J	`jnl`	A Privacy Preserving Randomized Gossip Algorithm via Controlled Noise Insertion. CoRR Filip Hanzely, Jakub Konecný, Nicolas Loizou, Peter Richtárik, Dmitry Grishchenko
2019	J	`jnl`	A Unified Theory of SGD: Variance Reduction, Sampling, Quantization and Coordinate Descent. CoRR Eduard Gorbunov, Filip Hanzely, Peter Richtárik
2019	A	`conf`	Accelerated Coordinate Descent with Arbitrary Sampling and Best Rates for Minibatches. AISTATS Filip Hanzely, Peter Richtárik
2019	J	`jnl`	Best Pair Formulation & Accelerated Scheme for Non-convex Principal Component Pursuit. CoRR Aritra Dutta, Filip Hanzely, Jingwei Liang, Peter Richtárik
2019	J	`jnl`	Better Communication Complexity for Local SGD. CoRR Ahmed Khaled, Konstantin Mishchenko, Peter Richtárik
2019	J	`jnl`	Convergence Analysis of Inexact Randomized Iterative Methods. CoRR Nicolas Loizou, Peter Richtárik
2019	J	`jnl`	Direct Nonlinear Acceleration. CoRR Aritra Dutta, El Houcine Bergou, Yunming Xiao, Marco Canini, Peter Richtárik
2019	J	`jnl`	Distributed Fixed Point Methods with Compressed Iterates. CoRR Sélim Chraibi, Ahmed Khaled, Dmitry Kovalev, Peter Richtárik, Adil Salim, Martin Takác
2019	J	`jnl`	Distributed Learning with Compressed Gradient Differences. CoRR Konstantin Mishchenko, Eduard Gorbunov, Martin Takác, Peter Richtárik
2019	J	`jnl`	Don't Jump Through Hoops and Remove Those Loops: SVRG and Katyusha are Better Without the Outer Loop. CoRR Dmitry Kovalev, Samuel Horváth, Peter Richtárik
2019	J	`jnl`	First Analysis of Local GD on Heterogeneous Data. CoRR Ahmed Khaled, Konstantin Mishchenko, Peter Richtárik
2019	J	`jnl`	Gradient Descent with Compressed Iterates. CoRR Ahmed Khaled, Peter Richtárik
2019	J	`jnl`	Natural Compression for Distributed Deep Learning. CoRR Samuel Horváth, Chen-Yu Ho, Ludovit Horvath, Atal Narayan Sahu, Marco Canini, Peter Richtárik
2019	J	`jnl`	New Convergence Aspects of Stochastic Gradient Algorithms. J. Mach. Learn. Res. Lam M. Nguyen, Phuong Ha Nguyen, Peter Richtárik, Katya Scheinberg, Martin Takác, Marten van Dijk
2019	A*	`conf`	Nonconvex Variance Reduced Optimization with Arbitrary Sampling. ICML Samuel Horváth, Peter Richtárik
2019	J	`jnl`	One Method to Rule Them All: Variance Reduction for Data, Parameters and Many New Methods. CoRR Filip Hanzely, Peter Richtárik
2019	A	`conf`	Online and Batch Supervised Background Estimation Via L1 Regression. WACV Aritra Dutta, Peter Richtárik
2019	Misc	`conf`	Provably Accelerated Randomized Gossip Algorithms. ICASSP Nicolas Loizou, Michael G. Rabbat, Peter Richtárik
2019	A*	`conf`	RSN: Randomized Subspace Newton. NeurIPS Robert M. Gower, Dmitry Kovalev, Felix Lieder, Peter Richtárik
2019	J	`jnl`	Randomized Projection Methods for Convex Feasibility: Conditioning and Convergence Rates. SIAM J. Optim. Ion Necoara, Peter Richtárik, Andrei Patrascu
2019	J	`jnl`	Revisiting Randomized Gossip Algorithms: General Framework, Convergence Rates and Novel Block and Accelerated Protocols. CoRR Nicolas Loizou, Peter Richtárik
2019	J	`jnl`	Revisiting Stochastic Extragradient. CoRR Konstantin Mishchenko, Dmitry Kovalev, Egor Shulgin, Peter Richtárik, Yura Malitsky
2019	A*	`conf`	SAGA with Arbitrary Sampling. ICML Xun Qian, Zheng Qu, Peter Richtárik
2019	J	`jnl`	SAGA with Arbitrary Sampling. CoRR Xun Qian, Zheng Qu, Peter Richtárik
2019	A*	`conf`	SGD with Arbitrary Sampling: General Analysis and Improved Rates. ICML Xun Qian, Peter Richtárik, Robert M. Gower, Alibek Sailanbayev, Nicolas Loizou, Egor Shulgin
2019	J	`jnl`	SGD: General Analysis and Improved Rates. CoRR Robert Mansel Gower, Nicolas Loizou, Xun Qian, Alibek Sailanbayev, Egor Shulgin, Peter Richtárik
2019	J	`jnl`	Scaling Distributed Machine Learning with In-Network Aggregation. CoRR Amedeo Sapio, Marco Canini, Chen-Yu Ho, Jacob Nelson, Panos Kalnis, Changhoon Kim, Arvind Krishnamurthy, Masoud Moshref, Dan R. K. Ports, Peter Richtárik
2019	—	`conf`	Stochastic Convolutional Sparse Coding. VMV Jinhui Xiong, Peter Richtárik, Wolfgang Heidrich
2019	J	`jnl`	Stochastic Convolutional Sparse Coding. CoRR Jinhui Xiong, Peter Richtárik, Wolfgang Heidrich
2019	J	`jnl`	Stochastic Newton and Cubic Newton Methods with Simple Local Linear-Quadratic Rates. CoRR Dmitry Kovalev, Konstantin Mishchenko, Peter Richtárik
2019	A*	`conf`	Stochastic Proximal Langevin Algorithm: Potential Splitting and Nonasymptotic Rates. NeurIPS Adil Salim, Dmitry Kovalev, Peter Richtárik
2019	J	`jnl`	Stochastic Proximal Langevin Algorithm: Potential Splitting and Nonasymptotic Rates. CoRR Adil Salim, Dmitry Kovalev, Peter Richtárik
2018	J	`jnl`	A Nonconvex Projection Method for Robust PCA. CoRR Aritra Dutta, Filip Hanzely, Peter Richtárik
2018	—	`conf`	Accelerated Gossip via Stochastic Heavy Ball Method. Allerton Nicolas Loizou, Peter Richtárik
2018	J	`jnl`	Accelerated Gossip via Stochastic Heavy Ball Method. CoRR Nicolas Loizou, Peter Richtárik
2018	A*	`conf`	Accelerated Stochastic Matrix Inversion: General Theory and Speeding up BFGS Rules for Faster Second-Order Optimization. NeurIPS Robert M. Gower, Filip Hanzely, Peter Richtárik, Sebastian U. Stich
2018	J	`jnl`	Accelerated Stochastic Matrix Inversion: General Theory and Speeding up BFGS Rules for Faster Second-Order Optimization. CoRR Robert M. Gower, Filip Hanzely, Peter Richtárik, Sebastian U. Stich
2018	B	`conf`	Coordinate Descent Faceoff: Primal or Dual? ALT Dominik Csiba, Peter Richtárik
2018	J	`jnl`	Fastest Rates for Stochastic Mirror Descent Methods. CoRR Filip Hanzely, Peter Richtárik
2018	J	`jnl`	Importance Sampling for Minibatches. J. Mach. Learn. Res. Dominik Csiba, Peter Richtárik
2018	—	`conf`	Matrix Completion Under Interval Uncertainty: Highlights. ECML/PKDD (3) Jakub Marecek, Peter Richtárik, Martin Takác
2018	J	`jnl`	New Convergence Aspects of Stochastic Gradient Algorithms. CoRR Lam M. Nguyen, Phuong Ha Nguyen, Peter Richtárik, Katya Scheinberg, Martin Takác, Marten van Dijk
2018	J	`jnl`	On the complexity of parallel coordinate descent. Optim. Methods Softw. Rachael Tappenden, Martin Takác, Peter Richtárik
2018	J	`jnl`	Provably Accelerated Randomized Gossip Algorithms. CoRR Nicolas Loizou, Michael G. Rabbat, Peter Richtárik
2018	A*	`conf`	Randomized Block Cubic Newton Method. ICML Nikita Doikov, Peter Richtárik
2018	J	`jnl`	Randomized Distributed Mean Estimation: Accuracy vs. Communication. Frontiers Appl. Math. Stat. Jakub Konecný, Peter Richtárik
2018	A*	`conf`	SEGA: Variance Reduction via Gradient Sketching. NeurIPS Filip Hanzely, Konstantin Mishchenko, Peter Richtárik
2018	J	`jnl`	SEGA: Variance Reduction via Gradient Sketching. CoRR Filip Hanzely, Konstantin Mishchenko, Peter Richtárik
2018	A*	`conf`	SGD and Hogwild! Convergence Without the Bounded Gradients Assumption. ICML Lam M. Nguyen, Phuong Ha Nguyen, Marten van Dijk, Peter Richtárik, Katya Scheinberg, Martin Takác
2018	J	`jnl`	SGD and Hogwild! Convergence Without the Bounded Gradients Assumption. CoRR Lam M. Nguyen, Phuong Ha Nguyen, Marten van Dijk, Peter Richtárik, Katya Scheinberg, Martin Takác
2018	J	`jnl`	Stochastic Primal-Dual Hybrid Gradient Algorithm with Arbitrary Sampling and Imaging Applications. SIAM J. Optim. Antonin Chambolle, Matthias J. Ehrhardt, Peter Richtárik, Carola-Bibiane Schönlieb
2018	A*	`conf`	Stochastic Spectral and Conjugate Descent Methods. NeurIPS Dmitry Kovalev, Peter Richtárik, Eduard Gorbunov, Elnur Gasanov
2018	J	`jnl`	Weighted Low-Rank Approximation of Matrices and Background Modeling. CoRR Aritra Dutta, Xin Li, Peter Richtárik
2017	—	`conf`	A Batch-Incremental Video Background Estimation Model Using Weighted Low-Rank Approximation of Matrices. ICCV Workshops Xin Li, Aritra Dutta, Peter Richtárik
2017	J	`jnl`	A Batch-Incremental Video Background Estimation Model using Weighted Low-Rank Approximation of Matrices. CoRR Aritra Dutta, Xin Li, Peter Richtárik
2017	J	`jnl`	Distributed optimization with arbitrary local solvers. Optim. Methods Softw. Chenxin Ma, Jakub Konecný, Martin Jaggi, Virginia Smith, Michael I. Jordan, Peter Richtárik, Martin Takác
2017	J	`jnl`	Linearly convergent stochastic heavy ball method for minimizing generalization error. CoRR Nicolas Loizou, Peter Richtárik
2017	J	`jnl`	Matrix completion under interval uncertainty. Eur. J. Oper. Res. Jakub Marecek, Peter Richtárik, Martin Takác
2017	J	`jnl`	Momentum and Stochastic Momentum for Stochastic Gradient, Newton, Proximal Point and Subspace Descent Methods. CoRR Nicolas Loizou, Peter Richtárik
2017	J	`jnl`	Online and Batch Supervised Background Estimation via L1 Regression. CoRR Aritra Dutta, Peter Richtárik
2017	J	`jnl`	Randomized Quasi-Newton Updates Are Linearly Convergent Matrix Inversion Algorithms. SIAM J. Matrix Anal. Appl. Robert M. Gower, Peter Richtárik
2017	J	`jnl`	Semi-Stochastic Gradient Descent Methods. Frontiers Appl. Math. Stat. Jakub Konecný, Peter Richtárik
2017	J	`jnl`	Semi-stochastic coordinate descent. Optim. Methods Softw. Jakub Konecný, Zheng Qu, Peter Richtárik
2017	J	`jnl`	Stochastic Primal-Dual Hybrid Gradient Algorithm with Arbitrary Sampling and Imaging Applications. CoRR Antonin Chambolle, Matthias J. Ehrhardt, Peter Richtárik, Carola-Bibiane Schönlieb
2017	J	`jnl`	Stochastic Reformulations of Linear Systems: Algorithms and Convergence Theory. CoRR Peter Richtárik, Martin Takác
2016	J	`jnl`	A New Perspective on Randomized Gossip Algorithms. CoRR Nicolas Loizou, Peter Richtárik
2016	—	`conf`	A new perspective on randomized gossip algorithms. GlobalSIP Nicolas Loizou, Peter Richtárik
2016	J	`jnl`	AIDE: Fast and Communication Efficient Distributed Optimization. CoRR Sashank J. Reddi, Jakub Konecný, Peter Richtárik, Barnabás Póczos, Alexander J. Smola
2016	J	`jnl`	Coordinate descent with arbitrary sampling I: algorithms and complexity. Optim. Methods Softw. Zheng Qu, Peter Richtárik
2016	J	`jnl`	Coordinate descent with arbitrary sampling II: expected separable overapproximation. Optim. Methods Softw. Zheng Qu, Peter Richtárik
2016	J	`jnl`	Distributed Coordinate Descent Method for Learning with Big Data. J. Mach. Learn. Res. Peter Richtárik, Martin Takác
2016	A*	`conf`	Even Faster Accelerated Coordinate Descent Using Non-Uniform Sampling. ICML Zeyuan Allen Zhu, Zheng Qu, Peter Richtárik, Yang Yuan
2016	J	`jnl`	Federated Learning: Strategies for Improving Communication Efficiency. CoRR Jakub Konecný, H. Brendan McMahan, Felix X. Yu, Peter Richtárik, Ananda Theertha Suresh, Dave Bacon
2016	J	`jnl`	Federated Optimization: Distributed Machine Learning for On-Device Intelligence. CoRR Jakub Konecný, H. Brendan McMahan, Daniel Ramage, Peter Richtárik
2016	J	`jnl`	Importance Sampling for Minibatches. CoRR Dominik Csiba, Peter Richtárik
2016	J	`jnl`	Inexact Coordinate Descent: Complexity and Preconditioning. J. Optim. Theory Appl. Rachael Tappenden, Peter Richtárik, Jacek Gondzio
2016	J	`jnl`	Mini-Batch Semi-Stochastic Gradient Descent in the Proximal Setting. IEEE J. Sel. Top. Signal Process. Jakub Konecný, Jie Liu, Peter Richtárik, Martin Takác
2016	J	`jnl`	On optimal probabilities in stochastic coordinate descent methods. Optim. Lett. Peter Richtárik, Martin Takác
2016	J	`jnl`	Optimization in High Dimensions via Accelerated, Parallel, and Proximal Coordinate Descent. SIAM Rev. Olivier Fercoq, Peter Richtárik
2016	J	`jnl`	Parallel coordinate descent methods for big data optimization. Math. Program. Peter Richtárik, Martin Takác
2016	J	`jnl`	Randomized Distributed Mean Estimation: Accuracy vs Communication. CoRR Jakub Konecný, Peter Richtárik
2016	J	`jnl`	Randomized Quasi-Newton Updates are Linearly Convergent Matrix Inversion Algorithms. CoRR Robert M. Gower, Peter Richtárik
2016	A*	`conf`	SDNA: Stochastic Dual Newton Ascent for Empirical Risk Minimization. ICML Zheng Qu, Peter Richtárik, Martin Takác, Olivier Fercoq
2016	A*	`conf`	Stochastic Block BFGS: Squeezing More Curvature out of Data. ICML Robert M. Gower, Donald Goldfarb, Peter Richtárik
2015	J	`jnl`	Accelerated, Parallel, and Proximal Coordinate Descent. SIAM J. Optim. Olivier Fercoq, Peter Richtárik
2015	A*	`conf`	Adding vs. Averaging in Distributed Primal-Dual Optimization. ICML Chenxin Ma, Virginia Smith, Martin Jaggi, Michael I. Jordan, Peter Richtárik, Martin Takác
2015	J	`jnl`	Adding vs. Averaging in Distributed Primal-Dual Optimization. CoRR Chenxin Ma, Virginia Smith, Martin Jaggi, Michael I. Jordan, Peter Richtárik, Martin Takác
2015	J	`jnl`	Distributed Mini-Batch SDCA. CoRR Martin Takác, Peter Richtárik, Nathan Srebro
2015	J	`jnl`	Distributed Optimization with Arbitrary Local Solvers. CoRR Chenxin Ma, Jakub Konecný, Martin Jaggi, Virginia Smith, Michael I. Jordan, Peter Richtárik, Martin Takác
2015	J	`jnl`	Mini-Batch Semi-Stochastic Gradient Descent in the Proximal Setting. CoRR Jakub Konecný, Jie Liu, Peter Richtárik, Martin Takác
2015	J	`jnl`	Primal Method for ERM with Flexible Mini-batching Schemes and Non-convex Losses. CoRR Dominik Csiba, Peter Richtárik
2015	—	`conf`	Quartz: Randomized Dual Coordinate Ascent with Arbitrary Sampling. NIPS Zheng Qu, Peter Richtárik, Tong Zhang
2015	J	`jnl`	Randomized Iterative Methods for Linear Systems. SIAM J. Matrix Anal. Appl. Robert Mansel Gower, Peter Richtárik
2015	J	`jnl`	SDNA: Stochastic Dual Newton Ascent for Empirical Risk Minimization. CoRR Zheng Qu, Peter Richtárik, Martin Takác, Olivier Fercoq
2015	J	`jnl`	Separable approximations and decomposition methods for the augmented Lagrangian. Optim. Methods Softw. Rachael Tappenden, Peter Richtárik, Burak Büke
2015	J	`jnl`	Stochastic Dual Ascent for Solving Linear Systems. CoRR Robert Mansel Gower, Peter Richtárik
2015	A*	`conf`	Stochastic Dual Coordinate Ascent with Adaptive Probabilities. ICML Dominik Csiba, Zheng Qu, Peter Richtárik
2015	J	`jnl`	Stochastic Dual Coordinate Ascent with Adaptive Probabilities. CoRR Dominik Csiba, Zheng Qu, Peter Richtárik
2014	J	`jnl`	Coordinate Descent with Arbitrary Sampling I: Algorithms and Complexity. CoRR Zheng Qu, Peter Richtárik
2014	J	`jnl`	Coordinate Descent with Arbitrary Sampling II: Expected Separable Overapproximation. CoRR Zheng Qu, Peter Richtárik
2014	J	`jnl`	Fast Distributed Coordinate Descent for Non-Strongly Convex Losses. CoRR Olivier Fercoq, Zheng Qu, Peter Richtárik, Martin Takác
2014	—	`conf`	Fast distributed coordinate descent for non-strongly convex losses. MLSP Olivier Fercoq, Zheng Qu, Peter Richtárik, Martin Takác
2014	J	`jnl`	Inequality-Constrained Matrix Completion: Adding the Obvious Helps! CoRR Martin Takác, Jakub Marecek, Peter Richtárik
2014	J	`jnl`	Iteration complexity of randomized block-coordinate descent methods for minimizing a composite function. Math. Program. Peter Richtárik, Martin Takác
2014	J	`jnl`	Randomized Dual Coordinate Ascent with Arbitrary Sampling. CoRR Zheng Qu, Peter Richtárik, Tong Zhang
2014	J	`jnl`	Semi-Stochastic Coordinate Descent. CoRR Jakub Konecný, Zheng Qu, Peter Richtárik
2014	J	`jnl`	Simple Complexity Analysis of Direct Search. CoRR Jakub Konecný, Peter Richtárik
2014	J	`jnl`	mS2GD: Mini-Batch Semi-Stochastic Gradient Descent in the Proximal Setting. CoRR Jakub Konecný, Jie Liu, Peter Richtárik, Martin Takác
2013	J	`jnl`	Accelerated, Parallel and Proximal Coordinate Descent. CoRR Olivier Fercoq, Peter Richtárik
2013	J	`jnl`	Distributed Coordinate Descent Method for Learning with Big Data. CoRR Peter Richtárik, Martin Takác
2013	J	`jnl`	Inexact Coordinate Descent: Complexity and Preconditioning CoRR Rachael Tappenden, Peter Richtárik, Jacek Gondzio
2013	J	`jnl`	Mini-Batch Primal and Dual Methods for SVMs CoRR Martin Takác, Avleen Singh Bijral, Peter Richtárik, Nathan Srebro
2013	—	`conf`	Mini-Batch Primal and Dual Methods for SVMs. ICML (3) Martin Takác, Avleen Singh Bijral, Peter Richtárik, Nati Srebro
2013	J	`jnl`	On Optimal Probabilities in Stochastic Coordinate Descent Methods. CoRR Peter Richtárik, Martin Takác
2013	J	`jnl`	Semi-Stochastic Gradient Descent Methods. CoRR Jakub Konecný, Peter Richtárik
2013	J	`jnl`	Separable Approximations and Decomposition Methods for the Augmented Lagrangian. CoRR Rachael Tappenden, Peter Richtárik, Burak Büke
2013	J	`jnl`	Smooth minimization of nonsmooth functions with parallel coordinate descent methods. CoRR Olivier Fercoq, Peter Richtárik
2013	J	`jnl`	TOP-SPIN: TOPic discovery via Sparse Principal component INterference. CoRR Martin Takác, Selin Damla Ahipasaoglu, Ngai-Man Cheung, Peter Richtárik
2012	J	`jnl`	Alternating Maximization: Unifying Framework for 8 Sparse PCA Formulations and Efficient Parallel Codes CoRR Peter Richtárik, Martin Takác, Selin Damla Ahipasaoglu
2012	J	`jnl`	Approximate Level Method for Nonsmooth Convex Minimization. J. Optim. Theory Appl. Peter Richtárik
2012	J	`jnl`	Optimal diagnostic tests for sporadic Creutzfeldt-Jakob disease based on support vector machine classification of RT-QuIC data CoRR William Hulme, Peter Richtárik, Lynne McGuire, Alison Green
2012	J	`jnl`	Parallel Coordinate Descent Methods for Big Data Optimization CoRR Peter Richtárik, Martin Takác
2011	—	`conf`	Efficient Serial and Parallel Coordinate Descent Methods for Huge-Scale Truss Topology Design. OR Peter Richtárik, Martin Takác
2011	J	`jnl`	Improved Algorithms for Convex Minimization in Relative Scale. SIAM J. Optim. Peter Richtárik
2010	J	`jnl`	Generalized Power Method for Sparse Principal Component Analysis. J. Mach. Learn. Res. Michel Journée, Yurii E. Nesterov, Peter Richtárik, Rodolphe Sepulchre