Doing well with less! On Sampling Techniques for Empirical Pairwise Loss Estimation/Minimization
arXiv:2606.02345v1 Announce Type: cross Abstract: Many machine learning problems, including similarity learning, ranking, and clustering, rely on empirical pairwise loss functions whose quadratic computational cost quickly becomes prohibitive at scale. We demonstrate how a frugal approach that retains only a fraction of the available information on pairs can achieve estimation or optimization performance comparable to that obtained by using all pairs, by leveraging survey sampling techniques. A central finding, supported by both theory and experiments, is that such sampling plans must target p
The explosion of data and the increasing scale of machine learning models necessitate more efficient computational methods to maintain or improve performance without quadratic cost increases.
This research provides a pathway to significantly reduce the computational cost of many core machine learning problems, making advanced AI more accessible and scalable.
Machine learning problems previously constrained by quadratic computational costs can now be tackled with linear or near-linear complexity using strategic sampling techniques.
- · AI compute providers
- · Large-scale machine learning applications
- · Data scientists
- · Cloud service providers
- · Inefficient ML training paradigms
- · Those relying on brute-force computation
Reduced training times and infrastructure costs for similarity learning, ranking, and clustering models.
Enables the application of complex pairwise loss functions to much larger datasets than previously feasible, accelerating AI development.
Could lead to the emergence of new AI applications and services that were previously computationally intractable due to data scale.
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Read at arXiv cs.LG