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Automatically debugging AutoML pipelines using maro: ML automated remediation oracle
MAPS 2022: Proceedings of the 6th ACM SIGPLAN International Symposium on Machine ProgrammingPages 60–69https://doi.org/10.1145/3520312.3534868Machine learning in practice often involves complex pipelines for data cleansing, feature engineering, preprocessing, and prediction. These pipelines are composed of operators, which have to be correctly connected and whose hyperparameters must be ...
- research-articleJune 2022
Predictive synthesis of API-centric code
MAPS 2022: Proceedings of the 6th ACM SIGPLAN International Symposium on Machine ProgrammingPages 40–49https://doi.org/10.1145/3520312.3534866Today’s programmers, especially data science practitioners, make heavy use of data-processing libraries (APIs) such as PyTorch, Tensorflow, NumPy, and the like. Program synthesizers can provide significant coding assistance to this community of users; ...
- research-articleJune 2022
Productivity assessment of neural code completion
- Albert Ziegler,
- Eirini Kalliamvakou,
- X. Alice Li,
- Andrew Rice,
- Devon Rifkin,
- Shawn Simister,
- Ganesh Sittampalam,
- Edward Aftandilian
MAPS 2022: Proceedings of the 6th ACM SIGPLAN International Symposium on Machine ProgrammingPages 21–29https://doi.org/10.1145/3520312.3534864Neural code synthesis has reached a point where snippet generation is accurate enough to be considered for integration into human software development workflows. Commercial products aim to increase programmers’ productivity, without being able to ...