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Found 9 papers, 4 papers with code
Efficient Distributed Hessian Free Algorithm for Large-scale Empirical Risk Minimization via Accumulating Sample Strategy
In this paper, we propose a Distributed Accumulated Newton Conjugate gradiEnt (DANCE) method in which sample size is gradually increasing to quickly obtain a solution whose empirical loss is under satisfactory statistical accuracy.
An Accelerated Communication-Efficient Primal-Dual Optimization Framework for Structured Machine Learning
In this paper, an accelerated variant of CoCoA+ is proposed and shown to possess a convergence rate of $\mathcal{O}(1/t^2)$ in terms of reducing suboptimality.
Fast and Safe: Accelerated gradient methods with optimality certificates and underestimate sequences
In this work we introduce the concept of an Underestimate Sequence (UES), which is motivated by Nesterov's estimate sequence.
CoCoA: A General Framework for Communication-Efficient Distributed Optimization
The scale of modern datasets necessitates the development of efficient distributed optimization methods for machine learning.
Distributed Inexact Damped Newton Method: Data Partitioning and Load-Balancing
In this paper we study inexact dumped Newton method implemented in a distributed environment.
Distributed Optimization with Arbitrary Local Solvers
To this end, we present a framework for distributed optimization that both allows the flexibility of arbitrary solvers to be used on each (single) machine locally, and yet maintains competitive performance against other state-of-the-art special-purpose distributed methods.
Partitioning Data on Features or Samples in Communication-Efficient Distributed Optimization?
In this paper we study the effect of the way that the data is partitioned in distributed optimization.
Linear Convergence of the Randomized Feasible Descent Method Under the Weak Strong Convexity Assumption
We show that the famous SDCA algorithm for optimizing the SVM dual problem, or the stochastic coordinate descent method for the LASSO problem, fits into the framework of RC-FDM.
Adding vs. Averaging in Distributed Primal-Dual Optimization
Distributed optimization methods for large-scale machine learning suffer from a communication bottleneck.
