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[2506.04166] N$^2$: A Unified Python Package and Test Bench for Nearest Neighbor-Based Matrix Completion
Summary
The paper presents N$^2$, a Python package for nearest neighbor-based matrix completion, emphasizing its modular design and superior performance in real-world applications compared to classical methods.
Why It Matters
N$^2$ addresses the growing need for effective matrix completion techniques in diverse fields such as healthcare and recommender systems. By providing a unified framework, it enables researchers and practitioners to conduct rapid experiments and benchmarks, thus advancing the state of the art in this area of machine learning.
Key Takeaways
- N$^2$ offers a modular and extensible interface for NN-based methods.
- The package supports rapid experimentation and benchmarking across various datasets.
- NN methods outperform classical techniques in real-world scenarios, demonstrating their robustness.
- A new NN variant introduced achieves state-of-the-art results in multiple settings.
- The benchmark suite includes datasets from healthcare, recommender systems, and more.
Computer Science > Machine Learning arXiv:2506.04166 (cs) [Submitted on 4 Jun 2025 (v1), last revised 13 Feb 2026 (this version, v2)] Title:N$^2$: A Unified Python Package and Test Bench for Nearest Neighbor-Based Matrix Completion Authors:Caleb Chin, Aashish Khubchandani, Harshvardhan Maskara, Kyuseong Choi, Jacob Feitelberg, Albert Gong, Manit Paul, Tathagata Sadhukhan, Anish Agarwal, Raaz Dwivedi View a PDF of the paper titled N$^2$: A Unified Python Package and Test Bench for Nearest Neighbor-Based Matrix Completion, by Caleb Chin and 9 other authors View PDF HTML (experimental) 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 ...