Filip Hanzely | DeepAI

DeepAI AI Chat

Featured Co-authors

Peter Richtarik
143 publications
Mladen Kolar
63 publications
Nicolas Loizou
26 publications
Konstantin Mishchenko
26 publications
Eduard Gorbunov
24 publications
Robert M. Gower
22 publications
Samuel Horvath
22 publications
Dmitry Kovalev
18 publications
Jakub Konečný
18 publications
Aritra Dutta
11 publications
Mher Safaryan
10 publications

∙ 04/28/2022

Personalized Federated Learning with Multiple Known Clusters

We consider the problem of personalized federated learning when there ar...

0 Boxiang Lyu, et al. ∙

∙ 02/19/2021

Personalized Federated Learning: A Unified Framework and Universal Optimization Techniques

We study the optimization aspects of personalized Federated Learning (FL...

0 Filip Hanzely, et al. ∙

∙ 02/14/2021

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

Large scale distributed optimization has become the default tool for the...

16 Mher Safaryan, et al. ∙

∙ 11/03/2020

Local SGD: Unified Theory and New Efficient Methods

We present a unified framework for analyzing local SGD methods in the co...

62 Eduard Gorbunov, et al. ∙

∙ 10/05/2020

Lower Bounds and Optimal Algorithms for Personalized Federated Learning

In this work, we consider the optimization formulation of personalized f...

0 Filip Hanzely, et al. ∙

∙ 08/26/2020

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

Many key problems in machine learning and data science are routinely mod...

0 Filip Hanzely, et al. ∙

∙ 02/21/2020

Stochastic Subspace Cubic Newton Method

In this paper, we propose a new randomized second-order optimization alg...

0 Filip Hanzely, et al. ∙

∙ 02/11/2020

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

We propose an accelerated version of stochastic variance reduced coordin...

0 Filip Hanzely, et al. ∙

∙ 02/10/2020

Federated Learning of a Mixture of Global and Local Models

We propose a new optimization formulation for training federated learnin...

7 Filip Hanzely, et al. ∙

∙ 05/27/2019

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

We propose a remarkably general variance-reduced method suitable for sol...

0 Filip Hanzely, et al. ∙

∙ 05/27/2019

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

In this paper we introduce a unified analysis of a large family of varia...

0 Eduard Gorbunov, et al. ∙

∙ 05/25/2019

Best Pair Formulation & Accelerated Scheme for Non-convex Principal Component Pursuit

The best pair problem aims to find a pair of points that minimize the di...

0 Aritra Dutta, et al. ∙

∙ 01/27/2019

99

It is well known that many optimization methods, including SGD, SAGA, an...

0 Konstantin Mishchenko, et al. ∙

∙ 01/27/2019

A Privacy Preserving Randomized Gossip Algorithm via Controlled Noise Insertion

In this work we present a randomized gossip algorithm for solving the av...

0 Filip Hanzely, et al. ∙

∙ 09/09/2018

SEGA: Variance Reduction via Gradient Sketching

We propose a randomized first order optimization method--SEGA (SkEtched ...

0 Filip Hanzely, et al. ∙

∙ 05/21/2018

A Nonconvex Projection Method for Robust PCA

Robust principal component analysis (RPCA) is a well-studied problem wit...

5 Aritra Dutta, et al. ∙

∙ 03/20/2018

Fastest Rates for Stochastic Mirror Descent Methods

Relative smoothness - a notion introduced by Birnbaum et al. (2011) and ...

0 Filip Hanzely, et al. ∙

∙ 02/12/2018

Accelerated Stochastic Matrix Inversion: General Theory and Speeding up BFGS Rules for Faster Second-Order Optimization

We present the first accelerated randomized algorithm for solving linear...

1 Robert M. Gower, et al. ∙

Are you a researcher?
Expose your work
to one of the largest
A.I. communities in the world