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Filip Hanzely · SciRah

Researcher profile · Semantic Scholar

Filip Hanzely

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Filip Hanzely — researcher profile on SciRah

h-index: 18
Works: 26
Citations: 2,164

Most-cited papers

A Field Guide to Federated Optimization
Jianyu Wang, Zachary B. Charles, Zheng Xu, Gauri Joshi +49 more
2021arXiv.org497 citations
Semantic Scholar
Federated Learning of a Mixture of Global and Local Models
Filip Hanzely, Peter Richtárik
2020arXiv.org457 citations
Semantic Scholar
Lower Bounds and Optimal Algorithms for Personalized Federated Learning
Filip Hanzely, S. Hanzely, Samuel Horváth, Peter Richtárik
2020Neural Information Processing Systems217 citations
Semantic Scholar
A Unified Theory of SGD: Variance Reduction, Sampling, Quantization and Coordinate Descent
Eduard A. Gorbunov, Filip Hanzely, Peter Richtárik
2019International Conference on Artificial Intelligence and Statistics173 citations
Semantic Scholar
Local SGD: Unified Theory and New Efficient Methods
Eduard A. Gorbunov, Filip Hanzely, Peter Richtárik
2020International Conference on Artificial Intelligence and Statistics130 citations
Semantic Scholar
Accelerated Bregman proximal gradient methods for relatively smooth convex optimization
Filip Hanzely, Peter Richtárik, Lin Xiao
2018Computational optimization and applications115 citations Open access
Semantic Scholar
SEGA: Variance Reduction via Gradient Sketching
Filip Hanzely, Konstantin Mishchenko, Peter Richtárik
2018Neural Information Processing Systems83 citations
Semantic Scholar
Stochastic Subspace Cubic Newton Method
Filip Hanzely, N. Doikov, Y. Nesterov, Peter Richtárik
2020International Conference on Machine Learning66 citations
Semantic Scholar
Personalized Federated Learning: A Unified Framework and Universal Optimization Techniques
Filip Hanzely, Boxin Zhao, M. Kolar
2021Trans. Mach. Learn. Res.62 citations
Semantic Scholar
Fastest rates for stochastic mirror descent methods
Filip Hanzely, Peter Richtárik
2018Computational optimization and applications58 citations Open access
Semantic Scholar
Accelerated Stochastic Matrix Inversion: General Theory and Speeding up BFGS Rules for Faster Second-Order Optimization
R. Gower, Filip Hanzely, Peter Richtárik, Sebastian U. Stich
2018Neural Information Processing Systems49 citations
Semantic Scholar
Accelerated Coordinate Descent with Arbitrary Sampling and Best Rates for Minibatches
Filip Hanzely, Peter Richtárik
2018International Conference on Artificial Intelligence and Statistics43 citations
Semantic Scholar
Testing for causality in reconstructed state spaces by an optimized mixed prediction method.
A. Krakovská, Filip Hanzely
2016Physical Review E34 citations
Semantic Scholar
Smoothness Matrices Beat Smoothness Constants: Better Communication Compression Techniques for Distributed Optimization
Mher Safaryan, Filip Hanzely, Peter Richtárik
2021Neural Information Processing Systems32 citations
Semantic Scholar
One Method to Rule Them All: Variance Reduction for Data, Parameters and Many New Methods
Filip Hanzely, Peter Richtárik
2019arXiv.org28 citations
Semantic Scholar
A Nonconvex Projection Method for Robust PCA
Aritra Dutta, Filip Hanzely, Peter Richtárik
2018AAAI Conference on Artificial Intelligence25 citations Open access
Semantic Scholar
Privacy preserving randomized gossip algorithms
Filip Hanzely, Jakub Konecný, Nicolas Loizou, Peter Richtárik +1 more
201721 citations
Semantic Scholar
Variance Reduced Coordinate Descent with Acceleration: New Method With a Surprising Application to Finite-Sum Problems
Filip Hanzely, Dmitry Kovalev, Peter Richtárik
2020International Conference on Machine Learning18 citations
Semantic Scholar
99% of Worker-Master Communication in Distributed Optimization Is Not Needed
Konstantin Mishchenko, Filip Hanzely, Peter Richtárik
2020Conference on Uncertainty in Artificial Intelligence15 citations
Semantic Scholar
99% of Parallel Optimization is Inevitably a Waste of Time
Konstantin Mishchenko, Filip Hanzely, Peter Richtárik
2019arXiv.org13 citations
Semantic Scholar
Best Pair Formulation & Accelerated Scheme for Non-Convex Principal Component Pursuit
Aritra Dutta, Filip Hanzely, Jingwei Liang, Peter Richtárik
2019IEEE Transactions on Signal Processing12 citations Open access
Semantic Scholar
A Privacy Preserving Randomized Gossip Algorithm via Controlled Noise Insertion
Filip Hanzely, Jakub Konecný, Nicolas Loizou, Peter Richtárik +1 more
2019arXiv.org11 citations
Semantic Scholar
99% of Distributed Optimization is a Waste of Time: The Issue and How to Fix it
Konstantin Mishchenko, Filip Hanzely, Peter Richtárik
20199 citations
Semantic Scholar
Personalized Federated Learning with Multiple Known Clusters
Boxiang Lyu, Filip Hanzely, M. Kolar
2022arXiv.org3 citations Open access
Semantic Scholar
Optimization for Supervised Machine Learning: Randomized Algorithms for Data and Parameters
Filip Hanzely
2020arXiv.org0 citations
Semantic Scholar
Extending the Reach of Big Data Optimization: Randomized Algorithms for Minimizing Relatively Smooth Functions
Filip Hanzely, Peter Richtárik
20170 citations
Semantic Scholar

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