This repository aggregates academic papers, software tools, benchmarks, and datasets focused on using machine learning for compiler optimization and systems tuning. It covers topics such as iterative compilation, instruction-level optimization, parallelism mapping, cost modeling, and auto-tuning. The list is organized by theme including surveys, domain-specific optimizations, learning program representations, and memory/cache analysis.