Learning to Learn and Predict: A Meta-Learning Approach for Multi-Label Classification
Table 6: The robustness analysis on the RCV1 dataset.
['[BOLD] Metrics', '[BOLD] Best', '[BOLD] Worst', '[BOLD] Average', '[BOLD] STDEV']
Algorithm Robustness Similar to Section The results indicate that the robustness of our meta-learner is consistent within different tasks and model structures, which again shows that the trained meta-learner can generate high-quality and robust policies.