Projection Free Rank-Drop Steps
Authors: Edward Cheung, Yuying Li
IJCAI 2017 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | 6 Experimental Results We validate the rank-drop steps on a matrix completion task using various datasets from Movie Lens2. We compare the proposed Rank-Drop Frank Wolfe (RDFW) against the aforementioned FW variants, FW [Frank and Wolfe, 1956], AFW [Lacoste-Julien and Jaggi, 2015], and IF(0, ), [Freund et al., 2015], as well as a state-of-the-art nuclear norm regularized solver in Active ALT [Hsieh and Olsen, 2014]. |
| Researcher Affiliation | Academia | Edward Cheung Yuying Li Cheriton School of Computer Science, University of Waterloo, Waterloo, Canada {eycheung, yuying}@uwaterloo.ca |
| Pseudocode | Yes | Algorithm 1 Frank-Wolfe (FW), Algorithm 2 (Atomic) Away Steps Frank-Wolfe (AFW), Algorithm 3 Compute Rank-Drop Direction (rank Drop), Algorithm 4 Rank-Drop Frank-Wolfe (RDFW) |
| Open Source Code | No | The paper does not provide any links to source code or explicit statements about its release. |
| Open Datasets | Yes | We validate the rank-drop steps on a matrix completion task using various datasets from Movie Lens2. 2http://grouplens.org/datasets/movielens/ and Table 2: Movie Lens Data |
| Dataset Splits | Yes | Following [Yao et al., 2016], we randomly partition each dataset into 50% training, 25% validation, and 25% testing. |
| Hardware Specification | No | The paper only states: 'All simulations were run in MATLAB.' No specific hardware details (e.g., CPU/GPU models, memory) are provided. |
| Software Dependencies | No | The paper only states: 'All simulations were run in MATLAB.' It does not specify a version number for MATLAB or any other software dependencies. |
| Experiment Setup | Yes | The δ value in (1) is tuned with δ = µj Y F , where Y F is the Frobenius norm of the training data matrix, and µj = 2 + 0.2j, j N. We increase j until the mean RMSE on the validation set does not improve by more than 10 3. We terminate the algorithm when an upper bound on the relative optimality gap ensures (f(Xk) f )/f < 10 2 or a maximum iteration count of 1000 is reached. and Table 3: Parameters used for each dataset. |