Abstract: Methods and systems for generating a model to be used for evaluating credit and loan applications are provided. The method includes: training a first model by using all features included in a universe of candidate features; measuring a first metric that relates to an accuracy of the first model and a second metric that relates to a disparity of the first model; constructing a graph based on pairwise correlations of the features; clustering the features into feature sets; estimating a respective disparity contribution associated with each feature set; selecting feature sets to be included in a second model; training, the second model; measuring the first metric and the second metric with respect to the second model; and determining whether the second model satisfies a predetermined accuracy level and a predetermined disparity reduction with respect to the first model.

Abstract: A method for performing machine learning includes assigning processing jobs to a plurality of model learners, using a central parameter server. The processing jobs includes solving gradients based on a current set of parameters. As the results from the processing job are returned, the set of parameters is iterated. A degree of staleness of the solving of the second gradient is determined based on a difference between the set of parameters when the jobs are assigned and the set of parameters when the jobs are returned. The learning rates used to iterate the parameters based on the solved gradients are proportional to the determined degrees of staleness.

Abstract: A novel coding technique, referred to herein as Generalized PolyDot, for calculating matrix-vector products that advances on existing techniques for coded matrix operations under storage and communication constraints is disclosed. The method is resistant to soft errors and provides a trade-off between error resistance and communication cost.

Abstract: A method for performing machine learning includes assigning processing jobs to a plurality of model learners, using a central parameter server. The processing jobs includes solving gradients based on a current set of parameters. As the results from the processing job are returned, the set of parameters is iterated. A degree of staleness of the solving of the second gradient is determined based on a difference between the set of parameters when the jobs are assigned and the set of parameters when the jobs are returned. The learning rates used to iterate the parameters based on the solved gradients are proportional to the determined degrees of staleness.