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 [1].
Online Low Rank Matrix Completion
Authors: Soumyabrata Pal, Prateek Jain
ICLR 2023 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We conducted detailed empirical study of our proposed algorithms (see Appendix A) on synthetic and multiple real datasets, and demonstrate that our algorithms can achieve significantly lower regret than methods that do not use collaboration between users. |
| Researcher Affiliation | Industry | Prateek Jain Google Research Bangalore, India EMAIL Soumyabrata Pal Google Research Bangalore, India EMAIL |
| Pseudocode | Yes | Algorithm 1 ESTIMATE; Algorithm 2 ETC ALGORITHM; Algorithm 3 OCTAL (ONLINE COLLABORATIVE FILTERING USING ITERATIVE USER CLUSTERING) |
| Open Source Code | No | The paper does not provide any statement about releasing source code for the described methodology, nor does it include a link to a code repository. |
| Open Datasets | No | The paper mentions using |
| Dataset Splits | No | The paper mentions using |
| Hardware Specification | No | The paper does not provide specific details about the hardware used to run experiments (e.g., GPU/CPU models, memory specifications). |
| Software Dependencies | No | The paper does not provide specific software dependencies with version numbers (e.g., Python 3.8, PyTorch 1.9). |
| Experiment Setup | No | The paper mentions |