Online Low Rank Matrix Completion
Authors: Soumyabrata Pal, Prateek Jain
ICLR 2023 | Conference PDF | Archive PDF | Plain Text | 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 prajain@google.com Soumyabrata Pal Google Research Bangalore, India soumyabrata@google.com |
| 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 |