N$^2$: A Unified Python Package and Test Bench for Nearest Neighbor-Based Matrix Completion
This provides a practical tool for researchers and practitioners working on matrix completion in domains like healthcare and recommender systems, though it is incremental in consolidating existing methods.
The authors tackled the problem of matrix completion by introducing N², a unified Python package and testbed for nearest neighbor-based methods, which achieved state-of-the-art results in several real-world settings and outperformed classical methods on diverse datasets.
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.