A Review on Oracle Issues in Machine Learning
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Abstract (arXiv)
Machine learning contrasts with traditional software development in that the oracle is the data, and the data is not always a correct representation of the problem that machine learning tries to model. We present a survey of the oracle issues found in machine learning and state-of-the-art solutions for dealing with these issues. These include lines of research for differential testing, metamorphic testing, and test coverage. We also review some recent improvements to robustness during modeling that reduce the impact of oracle issues, as well as tools and frameworks for assisting in testing and discovering issues specific to the dataset.
Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 9 of its 53 references that are in the map. First posted on arXiv 4 May 2021.
Builds on
9 of 53 references.
References · 9
- 2016XGBoost: A Scalable Tree Boosting Systemcited by 5,120
- 2019Optuna: A Next-generation Hyperparameter Optimization Frameworkcited by 2,455
- 2019Machine Learning Testing: Survey, Landscapes and Horizonscited by 213
- 2019Taxonomy of Real Faults in Deep Learning Systemscited by 90
- 2018On Testing Machine Learning Programscited by 48
- 2017OptionGAN: Learning Joint Reward-Policy Options using Generative Adversarial Inverse Reinforcement Learningcited by 40
- 20173D Object Reconstruction from a Single Depth View with Adversarial Learningcited by 26
- 2020Probabilistic Machine Learning for Healthcarecited by 23
- 2020Smoke Testing for Machine Learning: Simple Tests to Discover Severe Defectscited by 5
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Citing papers per year: 2022: 1.
1 paper.