Papers Distributed Optimization
“Distributed Optimization” 태그가 달린 논문 601편 · 필터 해제
Gradient Sparsification for Communication-Efficient Distributed Optimization
Modern large scale machine learning applications require stochastic optimization algorithms to be implemented on distributed computational architectures. A key bottleneck is the communication overhead for exchanging info…
BIG-bench Machine LearningDistributed OptimizationregressionStochastic OptimizationA Sequential Approximation Framework for Coded Distributed Optimization
Building on the previous work of Lee et al. and Ferdinand et al. on coded computation, we propose a sequential approximation framework for solving optimization problems in a distributed manner. In a distributed computati…
Distributed OptimizationDSCOVR: Randomized Primal-Dual Block Coordinate Algorithms for Asynchronous Distributed Optimization
Machine learning with big data often involves large optimization models. For distributed optimization over a cluster of machines, frequent communication and synchronization of all model parameters (optimization variables…
Distributed ComputingDistributed OptimizationDistributed Very Large Scale Bundle Adjustment by Global Camera Consensus
The increasing scale of Structure-from-Motion is fundamentally limited by the conventional optimization framework for the all-in-one global bundle adjustment. In this paper, we propose a distributed approach to coping wi…
Distributed ComputingDistributed OptimizationGIANT: Globally Improved Approximate Newton Method for Distributed Optimization
For distributed computing environment, we consider the empirical risk minimization problem and propose a distributed and communication-efficient Newton-type optimization method. At every iteration, each worker locally fi…
Distributed ComputingDistributed OptimizationDebiased distributed learning for sparse partial linear models in high dimensions
Although various distributed machine learning schemes have been proposed recently for pure linear models and fully nonparametric models, little attention has been paid on distributed optimization for semi-paramemetric mo…
Distributed OptimizationVocal Bursts Intensity PredictionStochastic, Distributed and Federated Optimization for Machine Learning
We study optimization algorithms for the finite sum problems frequently arising in machine learning applications. First, we propose novel variants of stochastic gradient descent with a variance reduction property that en…
BIG-bench Machine LearningDistributed OptimizationAdaptive Consensus ADMM for Distributed Optimization
The alternating direction method of multipliers (ADMM) is commonly used for distributed model fitting problems, but its performance and reliability depend strongly on user-defined penalty parameters. We study distributed…
Distributed OptimizationDistributed Algorithms for Feature Extraction Off-loading in Multi-Camera Visual Sensor Networks
Real-time visual analysis tasks, like tracking and recognition, require swift execution of computationally intensive algorithms. Visual sensor networks can be enabled to perform such tasks by augmenting the sensor networ…
Distributed OptimizationAccelerated Distributed Dual Averaging over Evolving Networks of Growing Connectivity
We consider the problem of accelerating distributed optimization in multi-agent networks by sequentially adding edges. Specifically, we extend the distributed dual averaging (DDA) subgradient algorithm to evolving networ…
Distributed OptimizationSchedulingDOPE: Distributed Optimization for Pairwise Energies
We formulate an Alternating Direction Method of Mul-tipliers (ADMM) that systematically distributes the computations of any technique for optimizing pairwise functions, including non-submodular potentials. Such discrete …
Distributed OptimizationPrivate Learning on Networks: Part II
This paper considers a distributed multi-agent optimization problem, with the global objective consisting of the sum of local objective functions of the agents. The agents solve the optimization problem using local compu…
Distributed OptimizationOptimal algorithms for smooth and strongly convex distributed optimization in networks
In this paper, we determine the optimal convergence rates for strongly convex and smooth distributed optimization in two settings: centralized and decentralized communications over a network. For centralized (i.e. master…
Distributed OptimizationregressionAn Online Optimization Approach for Multi-Agent Tracking of Dynamic Parameters in the Presence of Adversarial Noise
This paper addresses tracking of a moving target in a multi-agent network. The target follows a linear dynamics corrupted by an adversarial noise, i.e., the noise is not generated from a statistical distribution. The loc…
Distributed OptimizationHemingway: Modeling Distributed Optimization Algorithms
Distributed optimization algorithms are widely used in many industrial machine learning applications. However choosing the appropriate algorithm and cluster size is often difficult for users as the performance and conver…
Distributed OptimizationTraining Deep Neural Networks via Optimization Over Graphs
In this work, we propose to train a deep neural network by distributed optimization over a graph. Two nonlinear functions are considered: the rectified linear unit (ReLU) and a linear unit with both lower and upper cutof…
Distributed OptimizationAn Integrated Optimization + Learning Approach to Optimal Dynamic Pricing for the Retailer with Multi-type Customers in Smart Grids
In this paper, we consider a realistic and meaningful scenario in the context of smart grids where an electricity retailer serves three different types of customers, i.e., customers with an optimal home energy management…
Decision MakingDistributed Optimizationenergy managementManagementPrivate Learning on Networks
Continual data collection and widespread deployment of machine learning algorithms, particularly the distributed variants, have raised new privacy challenges. In a distributed machine learning scenario, the dataset is st…
BIG-bench Machine LearningDistributed OptimizationPrivacy PreservingDistributed Multi-Task Relationship Learning
Multi-task learning aims to learn multiple tasks jointly by exploiting their relatedness to improve the generalization performance for each task. Traditionally, to perform multi-task learning, one needs to centralize dat…
Distributed OptimizationMulti-Task LearningWithout-Replacement Sampling for Stochastic Gradient Methods
Stochastic gradient methods for machine learning and optimization problems are usually analyzed assuming data points are sampled *with* replacement. In contrast, sampling *without* replacement is far less understood, yet…
Distributed OptimizationLearning TheoryStochastic OptimizationTransductive Learning