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 (NeurIPS, 2018)

SEGA: Variance Reduction via Gradient Sketching. - Filip Hanzely; Konstantin Mishchenko; Peter Richtárik (NeurIPS, 2018)

A Nonconvex Projection Method for Robust PCA. - Aritra Dutta; Filip Hanzely; Peter Richtárik (AAAI, 2019)

Accelerated Coordinate Descent with Arbitrary Sampling and Best Rates for Minibatches. - Filip Hanzely; Peter Richtárik (AISTATS, 2019)

A Privacy Preserving Randomized Gossip Algorithm via Controlled Noise Insertion. - Filip Hanzely; Jakub Konecný; Nicolas Loizou; Peter Richtárik; Dmitry Grishchenko (CoRR, 2019)

99% of Parallel Optimization is Inevitably a Waste of Time. - Konstantin Mishchenko; Filip Hanzely; Peter Richtárik (CoRR, 2019)

One Method to Rule Them All: Variance Reduction for Data, Parameters and Many New Methods. - Filip Hanzely; Peter Richtárik (CoRR, 2019)

A Unified Theory of SGD: Variance Reduction, Sampling, Quantization and Coordinate Descent. - Eduard Gorbunov; Filip Hanzely; Peter Richtárik (AISTATS, 2020)

Stochastic Subspace Cubic Newton Method. - Filip Hanzely; Nikita Doikov; Yurii E. Nesterov; Peter Richtárik (ICML, 2020)

Variance Reduced Coordinate Descent with Acceleration: New Method With a Surprising Application to Finite-Sum Problems. - Filip Hanzely; Dmitry Kovalev; Peter Richtárik (ICML, 2020)

Lower Bounds and Optimal Algorithms for Personalized Federated Learning. - Filip Hanzely; Slavomír Hanzely; Samuel Horváth; Peter Richtárik (NeurIPS, 2020)

99% of Worker-Master Communication in Distributed Optimization Is Not Needed. - Konstantin Mishchenko; Filip Hanzely; Peter Richtárik (UAI, 2020)

Federated Learning of a Mixture of Global and Local Models. - Filip Hanzely; Peter Richtárik (CoRR, 2020)

Optimization for Supervised Machine Learning: Randomized Algorithms for Data and Parameters. - Filip Hanzely (CoRR, 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 (2020)

Local SGD: Unified Theory and New Efficient Methods. - Eduard Gorbunov; Filip Hanzely; Peter Richtárik (AISTATS, 2021)

Smoothness Matrices Beat Smoothness Constants: Better Communication Compression Techniques for Distributed Optimization. - Mher Safaryan; Filip Hanzely; Peter Richtárik (NeurIPS, 2021)

Fastest rates for stochastic mirror descent methods. - Filip Hanzely; Peter Richtárik (Comput. Optim. Appl., 2021)

Accelerated Bregman proximal gradient methods for relatively smooth convex optimization. - Filip Hanzely; Peter Richtárik; Lin Xiao 003 (Comput. Optim. Appl., 2021)

A Field Guide to Federated Optimization. - Jianyu Wang; Zachary Charles; Zheng Xu 002; Gauri Joshi; H. Brendan McMahan; Blaise Agüera y Arcas; Maruan Al-Shedivat; Galen Andrew; Salman Avestimehr; Katharine Daly; Deepesh Data; Suhas N. Diggavi; et al. (CoRR, 2021)

Personalized Federated Learning with Multiple Known Clusters. - Boxiang Lyu; Filip Hanzely; Mladen Kolar (CoRR, 2022)

Personalized Federated Learning: A Unified Framework and Universal Optimization Techniques. - Filip Hanzely; Boxin Zhao; Mladen Kolar (Trans. Mach. Learn. Res., 2023)