Supporting on-stack replacement in unstructured languages by loop reconstruction and extraction. - Raphael Mosaner; David Leopoldseder; Manuel Rigger; Roland Schatz; Hanspeter Mössenböck (MPLR, 2019)

Machine learning to ease understanding of data driven compiler optimizations. - Raphael Mosaner (SPLASH (Companion), 2020)

Using machine learning to predict the code size impact of duplication heuristics in a dynamic compiler. - Raphael Mosaner; David Leopoldseder; Lukas Stadler; Hanspeter Mössenböck (MPLR, 2021)

Improving Vectorization Heuristics in a Dynamic Compiler with Machine Learning Models. - Raphael Mosaner; Gergö Barany; David Leopoldseder; Hanspeter Mössenböck (VMIL@SPLASH, 2022)

Machine-Learning-Based Self-Optimizing Compiler Heuristics✱. - Raphael Mosaner; David Leopoldseder; Wolfgang Kisling; Lukas Stadler; Hanspeter Mössenböck (MPLR, 2022)

Compilation Forking: A Fast and Flexible Way of Generating Data for Compiler-Internal Machine Learning Tasks. - Raphael Mosaner; David Leopoldseder; Wolfgang Kisling; Lukas Stadler; Hanspeter Mössenböck (Art Sci. Eng. Program., 2023)

Taking a Closer Look: An Outlier-Driven Approach to Compilation-Time Optimization. - Florian Huemer; David Leopoldseder; Aleksandar Prokopec; Raphael Mosaner; Hanspeter Mössenböck (ECOOP, 2024)