Leveraging machine learning for system optimization can relieve researchers of designing manual heuristics, a time-consuming procedure. In this talk, we mainly discuss data-driven iterative refinement that models optimization as a sequential decision process: an initial solution to the optimization problem is iteratively improved until convergence. Each refinement step is controlled by a ML model learned from previous optimization trials, or data collected so far in this trial. We then introduce two examples in ML system, Coda and N-Bref, that de-compile assembly codes back to its source code. In both cases, first a coarse source program is proposed, and then refined by learned models to match the assembly. These approaches show strong performance compared to existing de-compilation tools that rely upon human heuristics and domain knowledge.