This paper provides a theoretical analysis comparing ranking-based constraints with absolute quality labels in representation learning. It establishes a unified framework to characterize sample complexity, generalization bounds, and consistency properties of both approaches, showing that ranking constraints can achieve comparable or superior performance with significantly fewer labeled examples under certain conditions.
Key findings
Ranking constraints can achieve comparable or superior performance with fewer labeled examples, especially when the underlying quality metric has bounded variance.
Novel generalization bounds derived show logarithmic scaling with the number of negative samples for ranking-based methods, contrasting with linear scaling for absolute-label approaches.
Analysis establishes conditions for statistical consistency of surrogate losses for pairwise and listwise ranking methods.
Ranking constraints offer inherent protection against certain types of annotation noise that affect absolute labels more severely.
Limitations & open questions
The analysis assumes certain conditions on the underlying quality metric, which may not hold in all practical scenarios.
The theoretical findings require empirical validation across a broader range of representation learning tasks and datasets.