Ablation and optimal empirical configuration of parametrization and optimization

Investigate and quantify the effects of parametrization and optimization choices on spectral representation learning performance, and determine the optimal empirical configuration of these choices.

Background

In the conclusion, the authors note observed performance gaps across algorithms and argue that both parametrization (e.g., different spectral formulations) and optimization objectives (e.g., contrastive vs. non-contrastive) matter. They explicitly defer a systematic ablation and identification of optimal empirical configurations to future work.

This open direction aims to disentangle how design choices impact empirical results and to provide guidance on selecting configurations that yield the best performance.