Add to ORKGbig data optimization in machine learning: special structure Single machine optimization stochastic gradient (1st order) versus batch gradient: pros and cons algorithm 1: SVRG (Stochastic variance reduced gradient) algorithm 2: SDCA (Stochastic Dual Coordinate Ascent
Gradient-based algorithms are popular when solving unconstrained optimization problems. By exploitin...
This thesis reports on experiments aimed at explaining why machine learning algorithms using the gre...
The steplength selection is a crucial issue for the effectiveness of the stochastic gradient methods...
Big Data problems in Machine Learning have large number of data points or large number of features, ...
University of Minnesota Ph.D. dissertation. April 2020. Major: Computer Science. Advisor: Arindam Ba...
This work considers optimization methods for large-scale machine learning (ML). Optimization in ML ...
Recent years have witnessed huge advances in machine learning (ML) and its applications, especially ...
Optimization has been the workhorse of solving machine learning problems. However, the efficiency of...
Large-scale learning problems require algorithms that scale benignly with respect to the size of the...
Machine learning for “big data” • Large-scale machine learning: large p, large n, large k – p: dimen...
Stochastic gradient descent is popular for large scale optimization but has slow convergence asympto...
Stochastic gradient descent is popular for large scale optimization but has slow convergence asympto...
Stochastic Gradient Descent (SGD) has become popular for solving large scale supervised machine lear...
Stochastic gradient descent (SGD) holds as a classical method to build large scale machine learning ...
In the age of artificial intelligence, the best approach to handling huge amounts of data is a treme...
Gradient-based algorithms are popular when solving unconstrained optimization problems. By exploitin...
This thesis reports on experiments aimed at explaining why machine learning algorithms using the gre...
The steplength selection is a crucial issue for the effectiveness of the stochastic gradient methods...
Big Data problems in Machine Learning have large number of data points or large number of features, ...
University of Minnesota Ph.D. dissertation. April 2020. Major: Computer Science. Advisor: Arindam Ba...
This work considers optimization methods for large-scale machine learning (ML). Optimization in ML ...
Recent years have witnessed huge advances in machine learning (ML) and its applications, especially ...
Optimization has been the workhorse of solving machine learning problems. However, the efficiency of...
Large-scale learning problems require algorithms that scale benignly with respect to the size of the...
Machine learning for “big data” • Large-scale machine learning: large p, large n, large k – p: dimen...
Stochastic gradient descent is popular for large scale optimization but has slow convergence asympto...
Stochastic gradient descent is popular for large scale optimization but has slow convergence asympto...
Stochastic Gradient Descent (SGD) has become popular for solving large scale supervised machine lear...
Stochastic gradient descent (SGD) holds as a classical method to build large scale machine learning ...
In the age of artificial intelligence, the best approach to handling huge amounts of data is a treme...
Gradient-based algorithms are popular when solving unconstrained optimization problems. By exploitin...
This thesis reports on experiments aimed at explaining why machine learning algorithms using the gre...
The steplength selection is a crucial issue for the effectiveness of the stochastic gradient methods...