Search Results for author:
Found 4 papers, 2 papers with code
Does Knowledge Graph Really Matter for Recommender Systems?
We consider the scenarios where knowledge in a KG gets completely removed, randomly distorted and decreased, and also where recommendations are for cold-start users.
Causality-Aided Trade-off Analysis for Machine Learning Fairness
This paper uses causality analysis as a principled method for analyzing trade-offs between fairness parameters and other crucial metrics in ML pipelines.
Measuring Discrimination to Boost Comparative Testing for Multiple Deep Learning Models
To tackle this problem, we propose Sample Discrimination based Selection (SDS) to select efficient samples that could discriminate multiple models, i. e., the prediction behaviors (right/wrong) of these samples would be helpful to indicate the trend of model performance.
Connecting Software Metrics across Versions to Predict Defects
Accurate software defect prediction could help software practitioners allocate test resources to defect-prone modules effectively and efficiently.
