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This release introduces N$^2$, a unified Python framework designed to streamline experimentation with nearest neighbor methods for matrix completion, addressing the gap between theoretical guarantees and real-world robustness. By consolidating diverse algorithms into a modular interface and providing a rigorous benchmark suite across domains like healthcare and LLM evaluation, the tool enables practitioners to validate that NN-based techniques consistently outperform classical approaches on imperfect data. The inclusion of confidence intervals and entry-wise error bounds further strengthens the theoretical foundation for deploying these models in production environments.
N$^2$: A Unified Python Package and Test Bench for Nearest Neighbor-Based Matrix Completion
arXiv:2506.04166v3 Announce Type: replace Abstract: Nearest neighbor (NN) methods have re-emerged as competitive tools for matrix completion, offering strong empirical performance and recent theoretical guarantees, including entry-wise error bounds, confidence intervals, and minimax optimality. Despite their simplicity, recent work has shown that NN approaches are robust to a range of missingness patterns and effective across diverse applications. This paper introduces N$^2$, a unified Python package and testbed that consolidates a broad class of NN-based methods through a modular, extensible interface. Built for both researchers and practitioners, N$^2$ supports rapid experimentation and benchmarking. Using this framework, we introduce a new NN variant that achieves state-of-the-art results in several settings. We also release a benchmark suite of real-world datasets, from healthcare and recommender systems to causal inference and LLM evaluation, designed to stress-test matrix completion methods beyond synthetic scenarios. Our experiments demonstrate that while classical methods excel on idealized data, NN-based techniques consistently outperform them in real-world settings.
Abstract:Nearest neighbor (NN) methods have re-emerged as competitive tools for matrix completion, offering strong empirical performance and recent theoretical guarantees, including entry-wise error bounds, confidence intervals, and minimax optimality. Despite their simplicity, recent work has shown that NN approaches are robust to a range of missingness patterns and effective across diverse applications. This paper introduces N$^2$, a unified Python package and testbed that consolidates a broad class of NN-based methods through a modular, extensible interface. Built for both researchers and practitioners, N$^2$ supports rapid experimentation and benchmarking. Using this framework, we introduce a new NN variant that achieves state-of-the-art results in several settings. We also release a benchmark suite of real-world datasets, from healthcare and recommender systems to causal inference and LLM evaluation, designed to stress-test matrix completion methods beyond synthetic scenarios. Our experiments demonstrate that while classical methods excel on idealized data, NN-based techniques consistently outperform them in real-world settings.
Machine Learning (cs.LG); Computation (stat.CO); Machine Learning (stat.ML)
arXiv:2506.04166 [cs.LG]
(or arXiv:2506.04166v3 [cs.LG] for this version)
https://doi.org/10.48550/arXiv.2506.04166 Focus to learn more arXiv-issued DOI via DataCite
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