A Sparse Interactive Model for Matrix Completion with Side Information
Authors: Jin Lu, Guannan Liang, Jiangwen Sun, Jinbo Bi
NeurIPS 2016 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Empirical results show that our approach outperforms three state-of-the-art methods both in simulations and on real world datasets. |
| Researcher Affiliation | Academia | Jin Lu Guannan Liang Jiangwen Sun Jinbo Bi University of Connecticut Storrs, CT 06269 {jin.lu, guannan.liang, jiangwen.sun, jinbo.bi}@uconn.edu |
| Pseudocode | Yes | Algorithm 1 The adaptive LADMM algorithm to solve Ck, Gk, Ek, k = 1, ..., K |
| Open Source Code | No | The paper does not provide concrete access to source code or explicitly state its release. |
| Open Datasets | Yes | Movie Lens. This dataset was downloaded from [12] and contained 100,000 user ratings [...] NCI-DREAM Challenge. The data on the reactions of 46 breast cancer cell lines to 26 drugs and the expression data of 18633 genes for all the cell lines were provided by NCI-DREAM Chal-lenge [10]. |
| Dataset Splits | Yes | The hyperparameters λ s and the rank of G (required by IMC and Dirty IMC) were tuned via the same cross validation process: we randomly picked 10% of the given entries to form a validation set. |
| Hardware Specification | No | The paper does not provide specific hardware details used for running its experiments. |
| Software Dependencies | No | The paper mentions algorithms and methods but does not provide specific software dependencies with version numbers. |
| Experiment Setup | Yes | The hyperparameters λ s and the rank of G (required by IMC and Dirty IMC) were tuned via the same cross validation process: we randomly picked 10% of the given entries to form a validation set. Then models were obtained by applying each method to the remaining entries with a specific choice of λ from 10 3, 10 2, ..., 104. The average validation RMSE was examined by repeating the above procedure six times. The hyperparameter values that gave the best average validation RMSE were chosen for each method. For IMC and Dirty IMC, the best rank of G was chosen from = 1 to 15 within each data split. |