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Found 4 papers, 3 papers with code
Azimuth: Systematic Error Analysis for Text Classification
Compared to other stages of the ML development cycle, such as model training and hyper-parameter tuning, the process and tooling for the error analysis stage are less mature.
Can Active Learning Preemptively Mitigate Fairness Issues?
We also explore the interaction of algorithmic fairness methods such as gradient reversal (GRAD) and BALD.
Synbols: Probing Learning Algorithms with Synthetic Datasets
Progress in the field of machine learning has been fueled by the introduction of benchmark datasets pushing the limits of existing algorithms.
Bayesian active learning for production, a systematic study and a reusable library
Active learning is able to reduce the amount of labelling effort by using a machine learning model to query the user for specific inputs.
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