31 citations · 63 across the 3 of their papers we have counts for
5 papers
Data Sampling Strategies in Stochastic Algorithms for Empirical Risk Minimization
Dominik Csiba
Gradient descent methods and especially their stochastic variants have become highly popular in the last decade due to their efficiency on big data optimization problems. In this t…
Global Convergence of Arbitrary-Block Gradient Methods for Generalized Polyak-Łojasiewicz Functions
Dominik Csiba, Peter Richtárik
In this paper we introduce two novel generalizations of the theory for gradient descent type methods in the proximal setting. First, we introduce the proportion function, which we…
Coordinate Descent Face-Off: Primal or Dual?
Dominik Csiba, Peter Richtárik
Randomized coordinate descent (RCD) methods are state-of-the-art algorithms for training linear predictors via minimizing regularized empirical risk. When the number of examples ($…
Primal Method for ERM with Flexible Mini-batching Schemes and Non-convex Losses
Dominik Csiba, Peter Richtárik
In this work we develop a new algorithm for regularized empirical risk minimization. Our method extends recent techniques of Shalev-Shwartz [02/2015], which enable a dual-free anal…
Stochastic Dual Coordinate Ascent with Adaptive Probabilities
Dominik Csiba, Zheng Qu, Peter Richtárik
This paper introduces AdaSDCA: an adaptive variant of stochastic dual coordinate ascent (SDCA) for solving the regularized empirical risk minimization problems. Our modification co…