A case study on machine learning for synthesizing benchmarks. - Andrés Goens; Alexander Brauckmann; Sebastian Ertel; Chris Cummins; Hugh Leather; Jerónimo Castrillón (MAPL@PLDI, 2019)

Compiler-based graph representations for deep learning models of code. - Alexander Brauckmann; Andrés Goens; Sebastian Ertel; Jerónimo Castrillón (CC, 2020)

ComPy-Learn: A toolbox for exploring machine learning representations for compilers. - Alexander Brauckmann; Andrés Goens; Jerónimo Castrillón (FDL, 2020)

PolyGym: Polyhedral Optimizations as an Environment for Reinforcement Learning. - Alexander Brauckmann; Andrés Goens; Jerónimo Castrillón (PACT, 2021)

A Reinforcement Learning Environment for Polyhedral Optimizations. - Alexander Brauckmann; Andrés Goens; Jerónimo Castrillón (CoRR, 2021)

ExeBench: an ML-scale dataset of executable C functions. - Jordi Armengol-Estapé; Jackson Woodruff; Alexander Brauckmann; José Wesley de Souza Magalhães; Michael F. P. O'Boyle (MAPS@PLDI, 2022)

mlirSynth: Automatic, Retargetable Program Raising in Multi-Level IR Using Program Synthesis. - Alexander Brauckmann; Elizabeth Polgreen; Tobias Grosser; Michael F. P. O'Boyle (PACT, 2023)

Rewriting History: Repurposing Domain-Specific CGRAs. - Jackson Woodruff; Thomas Koehler; Alexander Brauckmann; Chris Cummins; Sam Ainsworth; Michael F. P. O'Boyle (CoRR, 2023)

DFA-Net: A Compiler-Specific Neural Architecture for Robust Generalization in Data Flow Analyses. - Alexander Brauckmann; Anderson Faustino da Silva; Gabriel Synnaeve; Michael F. P. O'Boyle; Jerónimo Castrillón; Hugh Leather (CC, 2025)

Tensorize: Fast Synthesis of Tensor Programs from Legacy Code using Symbolic Tracing, Sketching and Solving. - Alexander Brauckmann; Luc Jaulmes; José Wesley de Souza Magalhães; Elizabeth Polgreen; Michael F. P. O'Boyle (CGO, 2025)

Guided Tensor Lifting. - Yixuan Li 003; José Wesley de Souza Magalhães; Alexander Brauckmann; Michael F. P. O'Boyle; Elizabeth Polgreen (Proc. ACM Program. Lang., 2025)