Papers Distributed Optimization
“Distributed Optimization” 태그가 달린 논문 601편 · 필터 해제
A primal-dual method for conic constrained distributed optimization problems
We consider cooperative multi-agent consensus optimization problems over an undirected network of agents, where only those agents connected by an edge can directly communicate. The objective is to minimize the sum of age…
Distributed OptimizationCoCoA: A General Framework for Communication-Efficient Distributed Optimization
The scale of modern datasets necessitates the development of efficient distributed optimization methods for machine learning. We present a general-purpose framework for distributed computing environments, CoCoA, that has…
BIG-bench Machine LearningDistributed ComputingDistributed OptimizationOptimization for Large-Scale Machine Learning with Distributed Features and Observations
As the size of modern data sets exceeds the disk and memory capacities of a single computer, machine learning practitioners have resorted to parallel and distributed computing. Given that optimization is one of the pilla…
BIG-bench Machine LearningDistributed ComputingDistributed OptimizationFederated Optimization: Distributed Machine Learning for On-Device Intelligence
We introduce a new and increasingly relevant setting for distributed optimization in machine learning, where the data defining the optimization are unevenly distributed over an extremely large number of nodes. The goal i…
BIG-bench Machine LearningDistributed OptimizationGeometrically Convergent Distributed Optimization with Uncoordinated Step-Sizes
A recent algorithmic family for distributed optimization, DIGing's, have been shown to have geometric convergence over time-varying undirected/directed graphs. Nevertheless, an identical step-size for all agents is neede…
Distributed OptimizationDistributed Online Optimization in Dynamic Environments Using Mirror Descent
This work addresses decentralized online optimization in non-stationary environments. A network of agents aim to track the minimizer of a global time-varying convex function. The minimizer evolves according to a known dy…
Distributed OptimizationAIDE: Fast and Communication Efficient Distributed Optimization
In this paper, we present two new communication-efficient methods for distributed minimization of an average of functions. The first algorithm is an inexact variant of the DANE algorithm that allows any local algorithm t…
Distributed OptimizationAccelerating Exact and Approximate Inference for (Distributed) Discrete Optimization with GPUs
Discrete optimization is a central problem in artificial intelligence. The optimization of the aggregated cost of a network of cost functions arises in a variety of problems including (W)CSP, DCOP, as well as optimizatio…
Distributed OptimizationDistributed Optimization of Convex Sum of Non-Convex Functions
We present a distributed solution to optimizing a convex function composed of several non-convex functions. Each non-convex function is privately stored with an agent while the agents communicate with neighbors to form a…
Distributed OptimizationDistributed Optimization for Client-Server Architecture with Negative Gradient Weights
Availability of both massive datasets and computing resources have made machine learning and predictive analytics extremely pervasive. In this work we present a synchronous algorithm and architecture for distributed opti…
BIG-bench Machine LearningDistributed OptimizationDistributed Asynchronous Dual Free Stochastic Dual Coordinate Ascent
The primal-dual distributed optimization methods have broad large-scale machine learning applications. Previous primal-dual distributed methods are not applicable when the dual formulation is not available, e.g. the sum-…
Distributed OptimizationDistributed Inexact Damped Newton Method: Data Partitioning and Load-Balancing
In this paper we study inexact dumped Newton method implemented in a distributed environment. We start with an original DiSCO algorithm [Communication-Efficient Distributed Optimization of Self-Concordant Empirical Loss,…
Distributed OptimizationWithout-Replacement Sampling for Stochastic Gradient Methods: Convergence Results and Application to Distributed Optimization
Stochastic gradient methods for machine learning and optimization problems are usually analyzed assuming data points are sampled \emph{with} replacement. In practice, however, sampling \emph{without} replacement is very …
Distributed OptimizationLearning TheoryStochastic OptimizationTransductive LearningDistributed Optimization with Arbitrary Local Solvers
With the growth of data and necessity for distributed optimization methods, solvers that work well on a single machine must be re-designed to leverage distributed computation. Recent work in this area has been limited by…
Distributed OptimizationL1-Regularized Distributed Optimization: A Communication-Efficient Primal-Dual Framework
Despite the importance of sparsity in many large-scale applications, there are few methods for distributed optimization of sparsity-inducing objectives. In this paper, we present a communication-efficient framework for L…
Distributed OptimizationMixed Robust/Average Submodular Partitioning: Fast Algorithms, Guarantees, and Applications
We investigate two novel mixed robust/average-case submodular data partitioning problems that we collectively call Submodular Partitioning. These problems generalize purely robust instances of the problem, namely max-min…
ClusteringDistributed OptimizationImage SegmentationSemantic Segmentation+1Federated Optimization:Distributed Optimization Beyond the Datacenter
We introduce a new and increasingly relevant setting for distributed optimization in machine learning, where the data defining the optimization are distributed (unevenly) over an extremely large number of \nodes, but the…
Distributed OptimizationMixed Robust/Average Submodular Partitioning: Fast Algorithms, Guarantees, and Applications to Parallel Machine Learning and Multi-Label Image Segmentation
We study two mixed robust/average-case submodular partitioning problems that we collectively call Submodular Partitioning. These problems generalize both purely robust instances of the problem (namely max-min submodular …
ClusteringDistributed OptimizationImage SegmentationInteractive Segmentation+2Partitioning 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. The original DiSCO algorithm [Communication-Efficient Distributed Optimization of Self-Concordant Empirical Loss, Yuc…
Distributed OptimizationSemantics, Representations and Grammars for Deep Learning
Deep learning is currently the subject of intensive study. However, fundamental concepts such as representations are not formally defined -- researchers "know them when they see them" -- and there is no common language f…
Deep LearningDistributed Optimization