Filip Hanzely

Publication Activity (10 Years)

Years Active: 2018-2023
Publications (10 Years): 34

Top Topics

Variance Reduction

Matrix Inversion

Quasi Monte Carlo

Top Venues

Publications

Filip Hanzely, Boxin Zhao, Mladen Kolar
Personalized Federated Learning: A Unified Framework and Universal Optimization Techniques. Trans. Mach. Learn. Res. 2023 (2023)
Boxiang Lyu, Filip Hanzely, Mladen Kolar
Personalized Federated Learning with Multiple Known Clusters. CoRR (2022)
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 Horvath, Zhouyuan Huo, Alex Ingerman, Martin Jaggi, Tara Javidi, Peter Kairouz, Satyen Kale, Sai Praneeth Karimireddy, Jakub Konecný, Sanmi Koyejo, Tian Li, Luyang Liu, Mehryar Mohri, Hang Qi, Sashank J. Reddi, Peter Richtárik, Karan Singhal, Virginia Smith, Mahdi Soltanolkotabi, Weikang Song, Ananda Theertha Suresh, Sebastian U. Stich, Ameet Talwalkar, Hongyi Wang, Blake E. Woodworth, Shanshan Wu, Felix X. Yu, Honglin Yuan, Manzil Zaheer, Mi Zhang, Tong Zhang, Chunxiang Zheng, Chen Zhu, Wennan Zhu
A Field Guide to Federated Optimization. CoRR (2021)
Filip Hanzely, Peter Richtárik, Lin Xiao
Accelerated Bregman proximal gradient methods for relatively smooth convex optimization. Comput. Optim. Appl. 79 (2) (2021)
Filip Hanzely, Peter Richtárik
Fastest rates for stochastic mirror descent methods. Comput. Optim. Appl. 79 (3) (2021)
Mher Safaryan, Filip Hanzely, Peter Richtárik
Smoothness Matrices Beat Smoothness Constants: Better Communication Compression Techniques for Distributed Optimization. CoRR (2021)
Eduard A. Gorbunov, Filip Hanzely, Peter Richtárik
Local SGD: Unified Theory and New Efficient Methods. AISTATS (2021)
Mher Safaryan, Filip Hanzely, Peter Richtárik
Smoothness Matrices Beat Smoothness Constants: Better Communication Compression Techniques for Distributed Optimization. NeurIPS (2021)
Filip Hanzely, Boxin Zhao, Mladen Kolar
Personalized Federated Learning: A Unified Framework and Universal Optimization Techniques. CoRR (2021)
Filip Hanzely, Dmitry Kovalev, Peter Richtárik
Variance Reduced Coordinate Descent with Acceleration: New Method With a Surprising Application to Finite-Sum Problems. CoRR (2020)
Konstantin Mishchenko, Filip Hanzely, Peter Richtárik
99% of Worker-Master Communication in Distributed Optimization Is Not Needed. UAI (2020)
Filip Hanzely
Optimization for Supervised Machine Learning: Randomized Algorithms for Data and Parameters. CoRR (2020)
Filip Hanzely, Nikita Doikov, Yurii E. Nesterov, Peter Richtárik
Stochastic Subspace Cubic Newton Method. ICML (2020)
Filip Hanzely, Peter Richtárik
Federated Learning of a Mixture of Global and Local Models. CoRR (2020)
Filip Hanzely, Slavomír Hanzely, Samuel Horváth, Peter Richtárik
Lower Bounds and Optimal Algorithms for Personalized Federated Learning. CoRR (2020)
Filip Hanzely, Dmitry Kovalev, Peter Richtárik
Variance Reduced Coordinate Descent with Acceleration: New Method With a Surprising Application to Finite-Sum Problems. ICML (2020)
Filip Hanzely, Slavomír Hanzely, Samuel Horváth, Peter Richtárik
Lower Bounds and Optimal Algorithms for Personalized Federated Learning. NeurIPS (2020)
Eduard A. Gorbunov, Filip Hanzely, Peter Richtárik
A Unified Theory of SGD: Variance Reduction, Sampling, Quantization and Coordinate Descent. AISTATS (2020)
Filip Hanzely, Nikita Doikov, Peter Richtárik, Yurii E. Nesterov
Stochastic Subspace Cubic Newton Method. CoRR (2020)
Aritra Dutta, Filip Hanzely, Jingwei Liang, Peter Richtárik
Best Pair Formulation & Accelerated Scheme for Non-Convex Principal Component Pursuit. IEEE Trans. Signal Process. 68 (2020)
Eduard A. Gorbunov, Filip Hanzely, Peter Richtárik
Local SGD: Unified Theory and New Efficient Methods. CoRR (2020)
Aritra Dutta, Filip Hanzely, Peter Richtárik
A Nonconvex Projection Method for Robust PCA. AAAI (2019)
Filip Hanzely, Peter Richtárik
Accelerated Coordinate Descent with Arbitrary Sampling and Best Rates for Minibatches. AISTATS (2019)
Aritra Dutta, Filip Hanzely, Jingwei Liang, Peter Richtárik
Best Pair Formulation & Accelerated Scheme for Non-convex Principal Component Pursuit. CoRR (2019)
Eduard A. Gorbunov, Filip Hanzely, Peter Richtárik
A Unified Theory of SGD: Variance Reduction, Sampling, Quantization and Coordinate Descent. CoRR (2019)
Konstantin Mishchenko, Filip Hanzely, Peter Richtárik
99% of Parallel Optimization is Inevitably a Waste of Time. CoRR (2019)
Filip Hanzely, Jakub Konecný, Nicolas Loizou, Peter Richtárik, Dmitry Grishchenko
A Privacy Preserving Randomized Gossip Algorithm via Controlled Noise Insertion. CoRR (2019)
Filip Hanzely, Peter Richtárik
One Method to Rule Them All: Variance Reduction for Data, Parameters and Many New Methods. CoRR (2019)
Robert M. Gower, Filip Hanzely, Peter Richtárik, Sebastian U. Stich
Accelerated Stochastic Matrix Inversion: General Theory and Speeding up BFGS Rules for Faster Second-Order Optimization. NeurIPS (2018)
Robert M. Gower, Filip Hanzely, Peter Richtárik, Sebastian U. Stich
Accelerated Stochastic Matrix Inversion: General Theory and Speeding up BFGS Rules for Faster Second-Order Optimization. CoRR (2018)
Aritra Dutta, Filip Hanzely, Peter Richtárik
A Nonconvex Projection Method for Robust PCA. CoRR (2018)
Filip Hanzely, Konstantin Mishchenko, Peter Richtárik
SEGA: Variance Reduction via Gradient Sketching. NeurIPS (2018)
Filip Hanzely, Konstantin Mishchenko, Peter Richtárik
SEGA: Variance Reduction via Gradient Sketching. CoRR (2018)
Filip Hanzely, Peter Richtárik
Fastest Rates for Stochastic Mirror Descent Methods. CoRR (2018)