Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Low Rank Matrix Completion via Robust Alternating Minimization in Nearly Linear Time
Authors: Yuzhou Gu, Zhao Song, Junze Yin, Lichen Zhang
ICLR 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | In this paper, we take a major step towards a more efficient and error-robust alternating minimization framework. To this end, we develop an analytical framework for alternating minimization that can tolerate a moderate amount of errors caused by approximate updates. Moreover, our algorithm runs in time e O(|Ω|k), which is nearly linear in the time to verify the solution while preserving the sample complexity. This improves upon all prior known alternating minimization approaches which require e O(|Ω|k2) time. |
| Researcher Affiliation | Collaboration | Yuzhou Gu Institute of Advanced Study EMAIL Zhao Song Adobe Research EMAIL Junze Yin Boston University EMAIL Lichen Zhang MIT CSAIL EMAIL |
| Pseudocode | Yes | Algorithm 1 Alternating minimization for matrix completion. The INIT procedure clips the rows with large norms, then performs a Gram-Schmidt process. Algorithm 2 High precision solver. Algorithm 3 Fast, high precision solver for weighted multiple response regression. Algorithm 4 Clipping and Gram-Schmidt. Algorithm 5 Alternating minimization for matrix completion, formal version of Algorithm 1. |
| Open Source Code | No | The paper does not contain any explicit statements or links indicating that source code for the described methodology is publicly available. |
| Open Datasets | No | The paper is theoretical and does not perform experiments on datasets, thus no training dataset is specified or made available. |
| Dataset Splits | No | The paper is theoretical and does not perform experiments on datasets, thus no validation splits are specified. |
| Hardware Specification | No | The paper is theoretical and focuses on algorithmic complexity and proofs. It does not mention any specific hardware used for running experiments. |
| Software Dependencies | No | The paper is theoretical and does not describe a software implementation or dependencies with version numbers. |
| Experiment Setup | No | The paper is theoretical and does not describe an experimental setup with hyperparameters or training settings. |