arXiv:2605.17189v2 Announce Type: replace-cross Abstract: Inductive matrix completion (IMC) is a variant of low-rank matrix completion that incorporates row and column side-information. In principle, it can reduce the effective dimension of the recovery problem from the ambient matrix size to the dimension of the side-information features. Existing theory, however, does not fully realize this advantage in the noisy setting: sample-efficient guarantees only apply to noiseless recovery, while noisy guarantees require sample sizes comparable to ordinary matrix completion. This paper closes this g
This research addresses a long-standing theoretical gap in sample-efficient inductive matrix completion, a fundamental AI task, particularly in noisy environments.
Improved matrix completion techniques are crucial for more robust and data-efficient AI systems, impacting recommendation engines, machine learning, and data analysis in real-world, imperfect data settings.
The ability to accurately complete matrices with less data and in the presence of noise means AI models can be trained more efficiently and reliably, reducing the data burden for some applications.
- · AI/ML researchers
- · Data scientists
- · Tech companies utilizing recommendation systems
- · Sectors with large but noisy datasets
More efficient and accurate data imputation and recommendation systems will become possible.
This could accelerate the development of AI applications in data-sparse or high-noise environments.
Reduced data requirements might slightly mitigate the compute bottleneck by enabling better performance from smaller datasets, indirectly impacting data center energy consumption.
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Read at arXiv cs.LG