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.

References

Ablating the effect of parametrization and optimization and finding the optimal empirical configuration is out of the scope of this paper, and we will leave as our future work.

— Spectral Ghost in Representation Learning: from Component Analysis to Self-Supervised Learning  (2601.20154 - Dai et al., 28 Jan 2026) in Section Conclusion

Water-filling's advantage over a well-chosen low-pass truncation is not established by our measurements, and the argument of this section rests on the transform, not on the allocation rule inside it.

— Lossy Compression of PDE Training Inputs: Field Reconstruction Error Does Not Order the Cost to a Trained Operator  (2610.06095 - Le, 5 Oct 2026) in Appendix, Section “The codec comparison in full,” paragraph “Replacing the flat-allocation arm”