← Authors

Peter Richtarik

120 papers · 2010–2025 · 10 conferences · across top CS/AI conferences

Achievements

🧭 Keyword Pioneer πŸŒ‰ Interdisciplinary Bridge 🐣 Hot Topic Early Bird 🐝 Cross-Pollinator (5) πŸ—ΊοΈ Taxonomy Completionist (18) 🌈 Renaissance Researcher (6) 🌍 Conference Polyglot (10) πŸƒ Academic Marathon (15) 🏠 Conference Loyalist (39) πŸ† Keyword Champion (6) 🀝 Dynamic Duo (18) πŸ† Grand Slam πŸ‘‘ Triple Crown πŸ”¬ Deep Specialist (22) πŸ“ˆ Trend Setter ❓ The Questioner (2) ⚑ Prolific Year (16) πŸš€ Conference Pioneer πŸ—ƒοΈ Keyword Collector (102) πŸ’Ž Century Club (120) πŸ”₯ Unstoppable (8)

Conferences

NIPS (39) ICML (31) AISTATS (15) ICLR (15) JMLR (7) UAI (6) AAAI (3) ALT (2) NAACL (1) NSDI (1)

Top co-authors

Dmitry Kovalev (18) Eduard Gorbunov (16) Samuel HorvΓ‘th (13) Alexander Tyurin (12) Filip Hanzely (11) Grigory Malinovsky (11) Martin Takac (10) Xun Qian (9) Konstantin Mishchenko (8) Alexander Gasnikov (8)

Keywords

stochastic optimization (25) distributed optimization (23) federated learning (21) stochastic gradient descent (21) variance reduction (19) communication compression (18) distributed learning (14) convex optimization (13) nonconvex optimization (11) communication efficiency (11) communication complexity (9) gradient compression (9) coordinate descent (9) stochastic gradient (8) error feedback (8) decentralized optimization (7) importance sampling (7) gradient descent (7) convergence analysis (7) empirical risk minimization (7)

Papers

Methods for Convex $(L_0,L_1)$-Smooth Optimization: Clipping, Acceleration, and Adaptivity ICLR 2025
Methods with Local Steps and Random Reshuffling for Generally Smooth Non-Convex Federated Optimization ICLR 2025
Ringmaster ASGD: The First Asynchronous SGD with Optimal Time Complexity ICML 2025
ATA: Adaptive Task Allocation for Efficient Resource Management in Distributed Machine Learning ICML 2025
Correlated Quantization for Faster Nonconvex Distributed Optimization UAI 2025
MindFlayer SGD: Efficient Parallel SGD in the Presence of Heterogeneous and Random Worker Compute Times UAI 2025
ELF: Federated Langevin Algorithms with Primal, Dual and Bidirectional Compression UAI 2025
LoCoDL: Communication-Efficient Distributed Learning with Local Training and Compression ICLR 2025
EF21 with Bells & Whistles: Six Algorithmic Extensions of Modern Error Feedback JMLR 2025
HIGGS: Pushing the Limits of Large Language Model Quantization via the Linearity Theorem NAACL 2025
MAST: model-agnostic sparsified training ICLR 2025
Minibatch Stochastic Three Points Method for Unconstrained Smooth Minimization AAAI 2024
High-Probability Convergence for Composite and Distributed Stochastic Minimization and Variational Inequalities with Heavy-Tailed Noise ICML 2024
The Power of Extrapolation in Federated Learning NIPS 2024
Error Feedback Reloaded: From Quadratic to Arithmetic Mean of Smoothness Constants ICLR 2024
Towards a Better Theoretical Understanding of Independent Subnetwork Training ICML 2024
On the Optimal Time Complexities in Decentralized Stochastic Asynchronous Optimization NIPS 2024
Understanding Progressive Training Through the Framework of Randomized Coordinate Descent AISTATS 2024
Communication Compression for Byzantine Robust Learning: New Efficient Algorithms and Improved Rates AISTATS 2024
MicroAdam: Accurate Adaptive Optimization with Low Space Overhead and Provable Convergence NIPS 2024
Shadowheart SGD: Distributed Asynchronous SGD with Optimal Time Complexity Under Arbitrary Computation and Communication Heterogeneity NIPS 2024
PV-Tuning: Beyond Straight-Through Estimation for Extreme LLM Compression NIPS 2024
Byzantine Robustness and Partial Participation Can Be Achieved at Once: Just Clip Gradient Differences NIPS 2024
Freya PAGE: First Optimal Time Complexity for Large-Scale Nonconvex Finite-Sum Optimization with Heterogeneous Asynchronous Computations NIPS 2024
Don't Compress Gradients in Random Reshuffling: Compress Gradient Differences NIPS 2024
Improving the Worst-Case Bidirectional Communication Complexity for Nonconvex Distributed Optimization under Function Similarity NIPS 2024
Det-CGD: Compressed Gradient Descent with Matrix Stepsizes for Non-Convex Optimization ICLR 2024
FedP3: Federated Personalized and Privacy-friendly Network Pruning under Model Heterogeneity ICLR 2024
High-Probability Bounds for Stochastic Optimization and Variational Inequalities: the Case of Unbounded Variance ICML 2023
Variance Reduction is an Antidote to Byzantines: Better Rates, Weaker Assumptions and Communication Compression as a Cherry on the Top ICLR 2023
DASHA: Distributed Nonconvex Optimization with Communication Compression and Optimal Oracle Complexity ICLR 2023
Random Reshuffling with Variance Reduction: New Analysis and Better Rates UAI 2023
2Direction: Theoretically Faster Distributed Training with Bidirectional Communication Compression NIPS 2023
Optimal Time Complexities of Parallel Stochastic Optimization Methods Under a Fixed Computation Model NIPS 2023
A Guide Through the Zoo of Biased SGD NIPS 2023
A Computation and Communication Efficient Method for Distributed Nonconvex Problems in the Partial Participation Setting NIPS 2023
Momentum Provably Improves Error Feedback! NIPS 2023
RandProx: Primal-Dual Optimization Algorithms with Randomized Proximal Updates ICLR 2023
EF21-P and Friends: Improved Theoretical Communication Complexity for Distributed Optimization with Bidirectional Compression ICML 2023
Catalyst Acceleration of Error Compensated Methods Leads to Better Communication Complexity AISTATS 2023
Can 5th Generation Local Training Methods Support Client Sampling? Yes! AISTATS 2023
Convergence of Stein Variational Gradient Descent under a Weaker Smoothness Condition AISTATS 2023
On Biased Compression for Distributed Learning JMLR 2023
A Convergence Theory for SVGD in the Population Limit under Talagrand’s Inequality T1 ICML 2022
FedNL: Making Newton-Type Methods Applicable to Federated Learning ICML 2022
3PC: Three Point Compressors for Communication-Efficient Distributed Training and a Better Theory for Lazy Aggregation ICML 2022
Shifted compression framework: generalizations and improvements UAI 2022
Accelerated Primal-Dual Gradient Method for Smooth and Convex-Concave Saddle-Point Problems with Bilinear Coupling NIPS 2022
EF-BV: A Unified Theory of Error Feedback and Variance Reduction Mechanisms for Biased and Unbiased Compression in Distributed Optimization NIPS 2022
Basis Matters: Better Communication-Efficient Second Order Methods for Federated Learning AISTATS 2022
An Optimal Algorithm for Strongly Convex Minimization under Affine Constraints AISTATS 2022
FLIX: A Simple and Communication-Efficient Alternative to Local Methods in Federated Learning AISTATS 2022
IntSGD: Adaptive Floatless Compression of Stochastic Gradients ICLR 2022
Doubly Adaptive Scaled Algorithm for Machine Learning Using Second-Order Information ICLR 2022
Permutation Compressors for Provably Faster Distributed Nonconvex Optimization ICLR 2022
Variance Reduced ProxSkip: Algorithm, Theory and Application to Federated Learning NIPS 2022
Distributed Methods with Compressed Communication for Solving Variational Inequalities, with Theoretical Guarantees NIPS 2022
ProxSkip: Yes! Local Gradient Steps Provably Lead to Communication Acceleration! Finally! ICML 2022
BEER: Fast $O(1/T)$ Rate for Decentralized Nonconvex Optimization with Communication Compression NIPS 2022
Optimal Algorithms for Decentralized Stochastic Variational Inequalities NIPS 2022
A Damped Newton Method Achieves Global $\mathcal O \left(\frac{1}{k^2}\right)$ and Local Quadratic Convergence Rate NIPS 2022
Proximal and Federated Random Reshuffling ICML 2022
Communication Acceleration of Local Gradient Methods via an Accelerated Primal-Dual Algorithm with an Inexact Prox NIPS 2022
Theoretically Better and Numerically Faster Distributed Optimization with Smoothness-Aware Quantization Techniques NIPS 2022
Error Compensated Distributed SGD Can Be Accelerated NIPS 2021
EF21: A New, Simpler, Theoretically Better, and Practically Faster Error Feedback NIPS 2021
CANITA: Faster Rates for Distributed Convex Optimization with Communication Compression NIPS 2021
Lower Bounds and Optimal Algorithms for Smooth and Strongly Convex Decentralized Optimization Over Time-Varying Networks NIPS 2021
Smoothness Matrices Beat Smoothness Constants: Better Communication Compression Techniques for Distributed Optimization NIPS 2021
Hyperparameter Transfer Learning with Adaptive Complexity AISTATS 2021
Local SGD: Unified Theory and New Efficient Methods AISTATS 2021
A Linearly Convergent Algorithm for Decentralized Optimization: Sending Less Bits for Free! AISTATS 2021
A Better Alternative to Error Feedback for Communication-Efficient Distributed Learning ICLR 2021
MARINA: Faster Non-Convex Distributed Learning with Compression ICML 2021
Distributed Second Order Methods with Fast Rates and Compressed Communication ICML 2021
ADOM: Accelerated Decentralized Optimization Method for Time-Varying Networks ICML 2021
PAGE: A Simple and Optimal Probabilistic Gradient Estimator for Nonconvex Optimization ICML 2021
Stochastic Sign Descent Methods: New Algorithms and Better Theory ICML 2021
L-SVRG and L-Katyusha with Arbitrary Sampling JMLR 2021
Scaling Distributed Machine Learning with In-Network Aggregation NSDI 2021
Optimal and Practical Algorithms for Smooth and Strongly Convex Decentralized Optimization NIPS 2020
Lower Bounds and Optimal Algorithms for Personalized Federated Learning NIPS 2020
Primal Dual Interpretation of the Proximal Stochastic Gradient Langevin Algorithm NIPS 2020
Stochastic Subspace Cubic Newton Method ICML 2020
99% of Worker-Master Communication in Distributed Optimization Is Not Needed UAI 2020
Don’t Jump Through Hoops and Remove Those Loops: SVRG and Katyusha are Better Without the Outer Loop ALT 2020
Revisiting Stochastic Extragradient AISTATS 2020
Tighter Theory for Local SGD on Identical and Heterogeneous Data AISTATS 2020
A Unified Theory of SGD: Variance Reduction, Sampling, Quantization and Coordinate Descent AISTATS 2020
A Stochastic Derivative-Free Optimization Method with Importance Sampling: Theory and Learning to Control AAAI 2020
A Stochastic Derivative Free Optimization Method with Momentum ICLR 2020
Linearly Converging Error Compensated SGD NIPS 2020
Variance Reduced Coordinate Descent with Acceleration: New Method With a Surprising Application to Finite-Sum Problems ICML 2020
Random Reshuffling: Simple Analysis with Vast Improvements NIPS 2020
Acceleration for Compressed Gradient Descent in Distributed and Federated Optimization ICML 2020
From Local SGD to Local Fixed-Point Methods for Federated Learning ICML 2020
New Convergence Aspects of Stochastic Gradient Algorithms JMLR 2019
Stochastic Proximal Langevin Algorithm: Potential Splitting and Nonasymptotic Rates NIPS 2019
SAGA with Arbitrary Sampling ICML 2019
Accelerated Coordinate Descent with Arbitrary Sampling and Best Rates for Minibatches AISTATS 2019
SGD: General Analysis and Improved Rates ICML 2019
Nonconvex Variance Reduced Optimization with Arbitrary Sampling ICML 2019
A Nonconvex Projection Method for Robust PCA AAAI 2019
RSN: Randomized Subspace Newton NIPS 2019
Randomized Block Cubic Newton Method ICML 2018
SGD and Hogwild! Convergence Without the Bounded Gradients Assumption ICML 2018
Accelerated Stochastic Matrix Inversion: General Theory and Speeding up BFGS Rules for Faster Second-Order Optimization NIPS 2018
SEGA: Variance Reduction via Gradient Sketching NIPS 2018
Coordinate Descent Faceoff: Primal or Dual? ALT 2018
Importance Sampling for Minibatches JMLR 2018
Stochastic Spectral and Conjugate Descent Methods NIPS 2018
Distributed Coordinate Descent Method for Learning with Big Data JMLR 2016
Even Faster Accelerated Coordinate Descent Using Non-Uniform Sampling ICML 2016
SDNA: Stochastic Dual Newton Ascent for Empirical Risk Minimization ICML 2016
Stochastic Block BFGS: Squeezing More Curvature out of Data ICML 2016
Stochastic Dual Coordinate Ascent with Adaptive Probabilities ICML 2015
Adding vs. Averaging in Distributed Primal-Dual Optimization ICML 2015
Quartz: Randomized Dual Coordinate Ascent with Arbitrary Sampling NIPS 2015
Mini-Batch Primal and Dual Methods for SVMs ICML 2013
Generalized Power Method for Sparse Principal Component Analysis JMLR 2010