World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
35
Citations
6191
World Ranking
11582
National Ranking
43

Best Publications

  • Iteration complexity of randomized block-coordinate descent methods for minimizing a composite function

    Peter Richtárik;Martin Takáč

  • Parallel coordinate descent methods for big data optimization

    Peter Richtárik;Martin Takáč

  • SARAH: A Novel Method for Machine Learning Problems Using Stochastic Recursive Gradient

    Lam M. Nguyen;Jie Liu;Katya Scheinberg;Martin Takáč

  • Communication-Efficient Distributed Dual Coordinate Ascent

    Martin Jaggi;Virginia Smith;Martin Takac;Jonathan Terhorst

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

    Jakub Konecny;Jie Liu;Peter Richtarik;Martin Takac

  • CoCoA: A General Framework for Communication-Efficient Distributed Optimization

    Virginia Smith;Simone Forte;Chenxin Ma;Martin Takáč

  • Distributed coordinate descent method for learning with big data

    Peter Richtárik;Martin Takáč

  • Distributed optimization with arbitrary local solvers

    Chenxin Ma;Jakub Konečný;Martin Jaggi;Virginia Smith

  • Mini-Batch Primal and Dual Methods for SVMs

    Martin Takac;Avleen Bijral;Peter Richtarik;Nati Srebro

  • Adding vs. Averaging in Distributed Primal-Dual Optimization

    Chenxin Ma;Virginia Smith;Martin Jaggi;Michael Jordan

  • Distributed Learning with Compressed Gradient Differences.

    Konstantin Mishchenko;Eduard A. Gorbunov;Martin Takác;Peter Richtárik

  • SGD and Hogwild! Convergence Without the Bounded Gradients Assumption

    Lam M. Nguyen;Phuong Ha Nguyen;Marten van Dijk;Peter Richtárik

  • Reinforcement Learning for Solving the Vehicle Routing Problem

    Unknown

  • On Optimal Probabilities in Stochastic Coordinate Descent Methods

    Peter Richtárik;Martin Takáč

  • Mini-Batch Primal and Dual Methods for SVMs

    Martin Takáč;Avleen Bijral;Peter Richtárik;Nathan Srebro

  • Stochastic Recursive Gradient Algorithm for Nonconvex Optimization

    Lam M. Nguyen;Jie Liu;Katya Scheinberg;Martin Takác

  • Efficient Serial and Parallel Coordinate Descent Methods for Huge-Scale Truss Topology Design

    Peter Richtárik;Martin Takáč

  • Stochastic reformulations of linear systems: Algorithms and convergence theory

    Peter Richtárik;Martin Takáč

  • SDNA: stochastic dual Newton ascent for empirical risk minimization

    Zheng Qu;Peter Richtárik;Martin Takáč;Olivier Fercoq

  • Communication-Efficient Distributed Dual Coordinate Ascent

    Martin Jaggi;Virginia Smith;Martin Takáč;Jonathan Terhorst

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