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The initial set of experiments were conducted with linear regression model on YearPredictionMSD dataset, which contains more than 40, 000 samples. The results of using SVRG optimization showed strong guarantees of faster convergence compared to SGD. A more detailed analysis of experiment results can be found in Benchmark section.

Key Characteristics of SVRG:

• Explicit variance reduction 
• Ability to use relatively large learning rate compared to SGD, which leads to faster convergence.

Expected Deliverables

The goal is to implement an MXNet Python Module that implements SVRG optimization technique.

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