Title: How to Improve Sample Complexity of SGD over Highly Dependent Data?. Abstract: Conventional machine learning applications typically assume that data samples are independently and identically distributed (i.i.d.). However, many practical scenarios naturally involve a data-generating process that produces highly dependent data samples, which are known to heavily bias the stochastic optimization process and slow down the convergence of learning. In this paper, we conduct a fundamental study on how to facilitate the convergence of SGD over highly dependent data using different popular update schemes. Specifically, with a $\phi$-mixing model that captures both exponential and polynomial decay of the data dependence over time, we show that SGD with periodic data-subsampling achieves an improved sample complexity over the standard SGD in the full spectrum of the $\phi$-mixing data dependence. Moreover, we show that by fully utilizing the data, mini-batch SGD can further substantially improve the sample complexity with highly dependent data. Numerical experiments validate our theory.  