One-sided Matrix Completion from Two Observations Per Row
Authors: Steven Cao, Percy Liang, Gregory Valiant
ICML 2023 | Conference PDF | Archive PDF | Plain Text | 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 <shcao@stanford.edu>. |
| 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. |