Variance Regularization for Accelerating Stochastic Optimization

• URL: http://arxiv.org/abs/2008.05969v1
• Date: Thu, 13 Aug 2020 15:34:01 GMT
• Title: Variance Regularization for Accelerating Stochastic Optimization
• Authors: Tong Yang, Long Sha, Pengyu Hong
• Abstract summary: We propose a universal principle which reduces the random error accumulation by exploiting statistic information hidden in mini-batch gradients. This is achieved by regularizing the learning-rate according to mini-batch variances.
• Score: 14.545770519120898
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: While nowadays most gradient-based optimization methods focus on exploring the high-dimensional geometric features, the random error accumulated in a stochastic version of any algorithm implementation has not been stressed yet. In this work, we propose a universal principle which reduces the random error accumulation by exploiting statistic information hidden in mini-batch gradients. This is achieved by regularizing the learning-rate according to mini-batch variances. Due to the complementarity of our perspective, this regularization could provide a further improvement for stochastic implementation of generic 1st order approaches. With empirical results, we demonstrated the variance regularization could speed up the convergence as well as stabilize the stochastic optimization.

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