N$^2$: A Unified Python Package and Test Bench for Nearest Neighbor-Based Matrix Completion
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arXiv
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| Autori principali: | , , , , , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| Soggetti: | |
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| _version_ | 1866910021300781056 |
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| author | Chin, Caleb Khubchandani, Aashish Maskara, Harshvardhan Choi, Kyuseong Feitelberg, Jacob Gong, Albert Paul, Manit Sadhukhan, Tathagata Agarwal, Anish Dwivedi, Raaz |
| author_facet | Chin, Caleb Khubchandani, Aashish Maskara, Harshvardhan Choi, Kyuseong Feitelberg, Jacob Gong, Albert Paul, Manit Sadhukhan, Tathagata Agarwal, Anish Dwivedi, Raaz |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2506_04166 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | N$^2$: A Unified Python Package and Test Bench for Nearest Neighbor-Based Matrix Completion Chin, Caleb Khubchandani, Aashish Maskara, Harshvardhan Choi, Kyuseong Feitelberg, Jacob Gong, Albert Paul, Manit Sadhukhan, Tathagata Agarwal, Anish Dwivedi, Raaz Machine Learning Computation 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. |
| title | N$^2$: A Unified Python Package and Test Bench for Nearest Neighbor-Based Matrix Completion |
| topic | Machine Learning Computation |
| url | https://arxiv.org/abs/2506.04166 |