500 citations · 1.3k across the 52 of their papers we have counts for
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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…
SDNA: Stochastic Dual Newton Ascent for Empirical Risk Minimization
Zheng Qu, Peter Richtárik, Martin Takáč +1
We propose a new algorithm for minimizing regularized empirical loss: Stochastic Dual Newton Ascent (SDNA). Our method is dual in nature: in each iteration we update a random subse…
Adding vs. Averaging in Distributed Primal-Dual Optimization
Chenxin Ma, Virginia Smith, Martin Jaggi +3
Distributed optimization methods for large-scale machine learning suffer from a communication bottleneck. It is difficult to reduce this bottleneck while still efficiently and accu…