N²统一了近邻矩阵补全方法，支持快速实验与真实数据测试。
N$^2$: A Unified Python Package and Test Bench for Nearest Neighbor-Based Matrix Completion
- 模块化设计整合多种近邻补全算法，便于扩展和实验。
- 在医疗、推荐系统等真实数据上表现优于经典方法。
- 提供涵盖多领域的基准数据集，适合实际场景验证。
近邻（NN）方法重新成为矩阵补全的有力工具，兼具优异的实证性能和近期的理论保证，包括逐项误差界、置信区间和极小极大最优性。尽管结构简单，最新研究表明其对多种缺失模式具有鲁棒性，并适用于广泛应用场景。本文提出 N²，一个统一的 Python 工具包与测试平台，通过模块化、可扩展接口整合了广泛的基于近邻的矩阵补全方法。该框架面向研究人员与实践者，支持快速实验与基准测试。利用此框架，我们提出一种新的近邻变体，在多个场景下达到当前最优结果。同时，我们发布了涵盖医疗、推荐系统、因果推断及大模型评估的真实世界数据集基准套件，旨在超越合成场景对补全方法进行压力测试。实验表明，虽然经典方法在理想数据上表现良好，但基于近邻的技术在真实数据中持续领先。
原文摘要 · Abstract (English)
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.
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