Filip Hanzely

According to our database, Filip Hanzely authored at least 23 papers between 2018 and 2022.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of four.

2022

Personalized Federated Learning with Multiple Known Clusters.

Boxiang Lyu, Filip Hanzely, Mladen Kolar

CoRR, 2022

2021

A Field Guide to Federated Optimization.

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

CoRR, 2021

Personalized Federated Learning: A Unified Framework and Universal Optimization Techniques.

Filip Hanzely, Boxin Zhao, Mladen Kolar

CoRR, 2021

Accelerated Bregman proximal gradient methods for relatively smooth convex optimization.

Filip Hanzely, Peter Richtárik, Lin Xiao

Comput. Optim. Appl., 2021

Fastest rates for stochastic mirror descent methods.

Filip Hanzely, Peter Richtárik

Comput. Optim. Appl., 2021

Smoothness Matrices Beat Smoothness Constants: Better Communication Compression Techniques for Distributed Optimization.

Mher Safaryan, Filip Hanzely, Peter Richtárik

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

Local SGD: Unified Theory and New Efficient Methods.

Eduard Gorbunov, Filip Hanzely, Peter Richtárik

Proceedings of the 24th International Conference on Artificial Intelligence and Statistics, 2021

2020

Optimization for Supervised Machine Learning: Randomized Algorithms for Data and Parameters.

Filip Hanzely

PhD thesis, 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

Optimization for Supervised Machine Learning: Randomized Algorithms for Data and Parameters.

Filip Hanzely

CoRR, 2020

Federated Learning of a Mixture of Global and Local Models.

Filip Hanzely, Peter Richtárik

CoRR, 2020

99% of Worker-Master Communication in Distributed Optimization Is Not Needed.

Konstantin Mishchenko, Filip Hanzely, Peter Richtárik

Proceedings of the Thirty-Sixth Conference on Uncertainty in Artificial Intelligence, 2020

Lower Bounds and Optimal Algorithms for Personalized Federated Learning.

Filip Hanzely, Slavomír Hanzely, Samuel Horváth, Peter Richtárik

Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

Variance Reduced Coordinate Descent with Acceleration: New Method With a Surprising Application to Finite-Sum Problems.

Filip Hanzely, Dmitry Kovalev, Peter Richtárik

Proceedings of the 37th International Conference on Machine Learning, 2020

Stochastic Subspace Cubic Newton Method.

Filip Hanzely, Nikita Doikov, Yurii E. Nesterov, Peter Richtárik

Proceedings of the 37th International Conference on Machine Learning, 2020

A Unified Theory of SGD: Variance Reduction, Sampling, Quantization and Coordinate Descent.

Eduard Gorbunov, Filip Hanzely, Peter Richtárik

Proceedings of the 23rd International Conference on Artificial Intelligence and Statistics, 2020

2019

One Method to Rule Them All: Variance Reduction for Data, Parameters and Many New Methods.

Filip Hanzely, Peter Richtárik

CoRR, 2019

99% of Parallel Optimization is Inevitably a Waste of Time.

Konstantin Mishchenko, Filip Hanzely, 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

Accelerated Coordinate Descent with Arbitrary Sampling and Best Rates for Minibatches.

Filip Hanzely, Peter Richtárik

Proceedings of the 22nd International Conference on Artificial Intelligence and Statistics, 2019

A Nonconvex Projection Method for Robust PCA.

Aritra Dutta, Filip Hanzely, Peter Richtárik

Proceedings of the Thirty-Third AAAI Conference on Artificial Intelligence, 2019

2018

SEGA: Variance Reduction via Gradient Sketching.

Filip Hanzely, Konstantin Mishchenko, Peter Richtárik

Proceedings of the Advances in Neural Information Processing Systems 31: Annual Conference on Neural Information Processing Systems 2018, 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

Proceedings of the Advances in Neural Information Processing Systems 31: Annual Conference on Neural Information Processing Systems 2018, 2018