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..
One-sided Matrix Completion from Two Observations Per Row
Authors: Steven Cao, Percy Liang, Gregory Valiant
ICML 2023 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We evaluate our algorithm on one-sided recovery of synthetic data and low-coverage genome sequencing. In these settings, our algorithm substantially outperforms standard matrix completion and a variety of direct factorization methods. |
| Researcher Affiliation | Academia | Steven Cao 1 Percy Liang 1 Gregory Valiant 1 1Stanford University. Correspondence to: Steven Cao <EMAIL>. |
| Pseudocode | No | No explicit pseudocode or algorithm block was found. |
| Open Source Code | No | No statement regarding the release of source code or a link to a repository was found. |
| Open Datasets | Yes | Finally, we evaluate our method on synthetic data and the 1000genomes dataset (Fairley et al., 2019). |
| Dataset Splits | No | The paper describes varying the number of rows (m) and observations per row (k) for experiments, and a fixed number of columns (d), but does not specify explicit train/validation/test dataset splits with percentages or counts. |
| Hardware Specification | Yes | Experiments were run on TITAN RTX and RTX 3090 GPUs with 24 gigabytes of memory |
| Software Dependencies | No | The paper mentions 'Optimization was done via Adam' and 'implement via non-convex optimization' but does not list specific software packages or libraries with version numbers (e.g., PyTorch 1.9, TensorFlow 2.x). |
| Experiment Setup | Yes | Optimization was done via Adam (Kingma & Ba, 2015) with lr = 1e 10, β = (0.9, 0.999), and 10,000 steps. |