11 citations · 46 across the 22 of their papers we have counts for
4 papers · 1 filter
Refining Labeling Functions with Limited Labeled Data
Chenjie Li, Amir Gilad, Boris Glavic +2
Programmatic weak supervision (PWS) significantly reduces human effort for labeling data by combining the outputs of user-provided labeling functions (LFs) on unlabeled datapoints.…
Stress-Testing ML Pipelines with Adversarial Data Corruption
Jiongli Zhu, Geyang Xu, Felipe Lorenzi +2
Structured data-quality issues, such as missing values correlated with demographics, culturally biased labels, or systemic selection biases, routinely degrade the reliability of ma…
Learning from Uncertain Data: From Possible Worlds to Possible Models
Jiongli Zhu, Su Feng, Boris Glavic +1
We introduce an efficient method for learning linear models from uncertain data, where uncertainty is represented as a set of possible variations in the data, leading to predictive…
Interpretable Data-Based Explanations for Fairness Debugging
Romila Pradhan, Jiongli Zhu, Boris Glavic +1
A wide variety of fairness metrics and eXplainable Artificial Intelligence (XAI) approaches have been proposed in the literature to identify bias in machine learning models that ar…