Search Results for author:
Found 3 papers, 1 papers with code
Understanding Difficulty-based Sample Weighting with a Universal Difficulty Measure
In this study, we theoretically prove that the generalization error of a sample can be used as a universal difficulty measure.
Exploring the Learning Difficulty of Data Theory and Measure
As learning difficulty is crucial for machine learning (e. g., difficulty-based weighting learning strategies), previous literature has proposed a number of learning difficulty measures.
A Mathematical Foundation for Robust Machine Learning based on Bias-Variance Trade-off
Conducting a clear comparison for existing RML algorithms in dealing with different samples is difficult due to lack of a unified theoretical framework for RML.
Cannot find the paper you are looking for? You can
Submit a new open access paper.
