Stratified Sampling for Stochastic Transparency

Stratified filtered sampling in stochastic optimization

We develop a methodology for evaluating a decision strategy generated by a stochastic optimization model. The methodology is based on a pilot study in which we estimate the distribution of performance associated with the strategy, and define an appropriate stratified sampling plan. An algorithm we call filtered search allows us to implement this plan efficiently. We demonstrate the approach’s a...

Accelerating Minibatch Stochastic Gradient Descent using Stratified Sampling

Stochastic Gradient Descent (SGD) is a popular optimization method which has been applied to many important machine learning tasks such as Support Vector Machines and Deep Neural Networks. In order to parallelize SGD, minibatch training is often employed. The standard approach is to uniformly sample a minibatch at each step, which often leads to high variance. In this paper we propose a stratif...

Stratified Sampling Method

Stratified Sampling Method

Scalable Simple Random Sampling and Stratified Sampling

Analyzing data sets of billions of records has now become a regular task in many companies and institutions. In the statistical analysis of those massive data sets, sampling generally plays a very important role. In this work, we describe a scalable simple random sampling algorithm, named ScaSRS, which uses probabilistic thresholds to decide on the fly whether to accept, reject, or wait-list an...