Peter Richtárik

Peter Richtárik, Michael G. Rabbat, Nicolas Loizou, 2018, ICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).

A Nonconvex Projection Method for Robust PCA pdf

Aritra Dutta, Peter Richtárik, Filip Hanzely, 2018, AAAI.

Quartz: Randomized Dual Coordinate Ascent with Arbitrary Sampling

Peter Richtárik, Tong Zhang, Zheng Qu, 2015, NIPS.

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

Aritra Dutta, Peter Richtárik, Jingwei Liang, 2019, IEEE Transactions on Signal Processing.

SEGA: Variance Reduction via Gradient Sketching pdf

Peter Richtárik, Konstantin Mishchenko, Filip Hanzely, 2018, NeurIPS.

Improved Algorithms for Convex Minimization in Relative Scale

Peter Richtárik, Peter Richtárik, 2011, SIAM J. Optim..

On optimal probabilities in stochastic coordinate descent methods

Peter Richtárik, Martin Takác, Peter Richtárik, 2013, Optim. Lett..

Importance Sampling for Minibatches pdf

Peter Richtárik, Dominik Csiba, Peter Richtárik, 2016, J. Mach. Learn. Res..

Linearly convergent stochastic heavy ball method for minimizing generalization error pdf

Peter Richtárik, Nicolas Loizou, Peter Richtárik, 2017, ArXiv.

SDNA: Stochastic Dual Newton Ascent for Empirical Risk Minimization

Peter Richtárik, Martin Takác, Zheng Qu, 2015, ICML.

Approximate Level Method for Nonsmooth Convex Minimization

Peter Richtárik, Peter Richtárik, 2011, Journal of Optimization Theory and Applications.

Scaling Distributed Machine Learning with In-Network Aggregation pdf

Panos Kalnis, Marco Canini, Jacob Nelson, 2019, NSDI.

A new perspective on randomized gossip algorithms

Peter Richtárik, Nicolas Loizou, Peter Richtárik, 2016, 2016 IEEE Global Conference on Signal and Information Processing (GlobalSIP).

Accelerated, Parallel, and Proximal Coordinate Descent

Peter Richtárik, Olivier Fercoq, Peter Richtárik, 2013, SIAM J. Optim..

Gradient Descent with Compressed Iterates pdf

Peter Richtárik, Ahmed Khaled, 2019, ArXiv.

Direct Nonlinear Acceleration pdf

Aritra Dutta, Marco Canini, Peter Richtárik, 2019, EURO J. Comput. Optim..

Even Faster Accelerated Coordinate Descent Using Non-Uniform Sampling

Zeyuan Allen Zhu, Peter Richtárik, Yang Yuan, 2015, ICML.

Revisiting Stochastic Extragradient

Peter Richtárik, Dmitry Kovalev, Yura Malitsky, 2019, AISTATS.

Accelerated Gossip via Stochastic Heavy Ball Method

Peter Richtárik, Nicolas Loizou, Peter Richtárik, 2018, 2018 56th Annual Allerton Conference on Communication, Control, and Computing (Allerton).

Stochastic Reformulations of Linear Systems: Algorithms and Convergence Theory pdf

Peter Richtárik, Martin Takác, Peter Richtárik, 2017, SIAM J. Matrix Anal. Appl..

Optimal diagnostic tests for sporadic Creutzfeldt-Jakob disease based on support vector machine classification of RT-QuIC data pdf

Peter Richtárik, Alison Green, William Hulme, 2012, ArXiv.

Randomized Block Cubic Newton Method

Peter Richtárik, Nikita Doikov, Peter Richtárik, 2018, ICML.

Better Communication Complexity for Local SGD pdf

Peter Richtárik, Konstantin Mishchenko, Ahmed Khaled, 2019, ArXiv.

Extending the Reach of Big Data Optimization: Randomized Algorithms for Minimizing Relatively Smooth Functions

Peter Richtárik, Filip Hanzely, 2017 .

Natural Compression for Distributed Deep Learning pdf

Marco Canini, Peter Richtárik, Atal Narayan Sahu, 2019, MSML.

Momentum and stochastic momentum for stochastic gradient, Newton, proximal point and subspace descent methods pdf

Peter Richtárik, Nicolas Loizou, Peter Richtárik, 2017, Computational Optimization and Applications.

Accelerated Coordinate Descent with Arbitrary Sampling and Best Rates for Minibatches

Peter Richtárik, Filip Hanzely, Peter Richtárik, 2018, AISTATS.

Stochastic Spectral and Conjugate Descent Methods

Peter Richtárik, Dmitry Kovalev, Eduard A. Gorbunov, 2018, NeurIPS.

Distributed optimization with arbitrary local solvers

Michael I. Jordan, Martin Jaggi, Peter Richtárik, 2015, Optim. Methods Softw..

Stochastic Dual Coordinate Ascent with Adaptive Probabilities

Peter Richtárik, Zheng Qu, Dominik Csiba, 2015, ICML.

A Privacy Preserving Randomized Gossip Algorithm via Controlled Noise Insertion pdf

Peter Richtárik, Dmitry Grishchenko, Jakub Konecný, 2019, ArXiv.

Coordinate Descent Faceoff: Primal or Dual?

Peter Richtárik, Dominik Csiba, Peter Richtárik, 2018, ALT.

Inexact Coordinate Descent: Complexity and Preconditioning

Peter Richtárik, Jacek Gondzio, Rachael Tappenden, 2013, J. Optim. Theory Appl..

Randomized Dual Coordinate Ascent with Arbitrary Sampling pdf

Peter Richtárik, Tong Zhang, Zheng Qu, 2014, ArXiv.

Federated Learning: Strategies for Improving Communication Efficiency pdf

Peter Richtárik, Dave Bacon, Ananda Theertha Suresh, 2016, ArXiv.

Faster PET reconstruction with a stochastic primal-dual hybrid gradient method

Pawel Markiewicz, Antonin Chambolle, Peter Richtárik, 2017, Optical Engineering + Applications.

Stochastic Block BFGS: Squeezing More Curvature out of Data

Robert M. Gower, Peter Richtárik, Donald Goldfarb, 2016, ICML.

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

Peter Richtárik, Konstantin Mishchenko, Filip Hanzely, 2019, ArXiv.

Coordinate descent with arbitrary sampling I: algorithms and complexity†

Peter Richtárik, Zheng Qu, Peter Richtárik, 2014, Optim. Methods Softw..

Inequality-Constrained Matrix Completion: Adding the Obvious Helps! pdf

Jakub Marecek, Peter Richtárik, Martin Takác, 2014, ArXiv.

SGD: General Analysis and Improved Rates pdf

Peter Richtárik, Xun Qian, Nicolas Loizou, 2019, ICML 2019.

Federated Optimization: Distributed Machine Learning for On-Device Intelligence pdf

Peter Richtárik, Jakub Konecný, H. Brendan McMahan, 2016, ArXiv.

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

Peter Richtárik, Konstantin Mishchenko, Filip Hanzely, 2020, UAI.

Distributed Mini-Batch SDCA pdf

Peter Richtárik, Martin Takác, Nathan Srebro, 2015, ArXiv.

Mini-Batch Primal and Dual Methods for SVMs pdf

Avleen Singh Bijral, Peter Richtárik, Martin Takác, 2013, ICML.

Convergence Analysis of Inexact Randomized Iterative Methods

Peter Richtárik, Nicolas Loizou, Peter Richtárik, 2019, SIAM J. Sci. Comput..

Stochastic Convolutional Sparse Coding

Wolfgang Heidrich, Peter Richtárik, Jinhui Xiong, 2019, VMV.

Fastest rates for stochastic mirror descent methods pdf

Peter Richtárik, Filip Hanzely, Peter Richtárik, 2018, Computational Optimization and Applications.

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

Peter Richtárik, Filip Hanzely, Peter Richtárik, 2019, ArXiv.

Efficiency of randomized coordinate descent methods on minimization problems with a composite objective function

Peter Richtárik, Peter Richtárik, 2011 .

Matrix completion under interval uncertainty

Jakub Marecek, Peter Richtárik, Martin Takác, 2017, Eur. J. Oper. Res..

Revisiting Randomized Gossip Algorithms: General Framework, Convergence Rates and Novel Block and Accelerated Protocols pdf

Peter Richtárik, Nicolas Loizou, Peter Richtárik, 2019, IEEE Transactions on Information Theory.

Randomized Iterative Methods for Linear Systems

Peter Richtárik, Robert Mansel Gower, Peter Richtárik, 2015, SIAM J. Matrix Anal. Appl..

TOP-SPIN: TOPic discovery via Sparse Principal component INterference pdf

Ngai-Man Cheung, Peter Richtárik, Martin Takác, 2013, Modeling and Optimization: Theory and Applications.

Weighted Low-Rank Approximation of Matrices and Background Modeling pdf

Xin Li, Aritra Dutta, Peter Richtárik, 2018, ArXiv.

Mini-Batch Semi-Stochastic Gradient Descent in the Proximal Setting

Jie Liu, Peter Richtárik, Martin Takác, 2015, IEEE Journal of Selected Topics in Signal Processing.

A Batch-Incremental Video Background Estimation Model Using Weighted Low-Rank Approximation of Matrices

Aritra Dutta, Xin Li, Peter Richtárik, 2017, 2017 IEEE International Conference on Computer Vision Workshops (ICCVW).

SOME ALGORITHMS FOR LARGE-SCALE LINEAR

Peter Richtárik, Peter Richtárik, 2007 .

FL_PyTorch: optimization research simulator for federated learning

Peter Richtárik, Konstantin Burlachenko, Samuel Horváth, 2021, DistributedML@CoNEXT.

Approximate level method

Peter Richtárik, Peter Richtárik, 2008 .

Some algorithms for large-scale linear and convex minimization in relative scale

Michael J. Todd, Peter Richtárik, M. Todd, 2007 .