Probabilistic low-rank matrix completion on finite alphabets
Authors: Jean Lafond, Olga Klopp, Eric Moulines, Joseph Salmon
NeurIPS 2014 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | 3 Numerical Experiments |
| Researcher Affiliation | Academia | Jean Lafond Institut Mines-T el ecom T el ecom Paris Tech CNRS LTCI, Olga Klopp CREST et MODAL X Universit e Paris Ouest, Eric Moulines Institut Mines-T el ecom T el ecom Paris Tech CNRS LTCI, Joseph Salmon Institut Mines-T el ecom T el ecom Paris Tech CNRS LTCI |
| Pseudocode | Yes | Algorithm 1: Multinomial lifted coordinate gradient descent |
| Open Source Code | No | The paper states 'Algorithm 1 was implemented in C' but does not provide any link to public source code or an explicit statement of its release. |
| Open Datasets | Yes | We have also run the same estimators on the Movie Lens 100k dataset. |
| Dataset Splits | Yes | Therefore, to compare the prediction errors, we randomly selected 20% of the entries as a test set, and the remaining entries were split between a training set (80%) and a validation set (20%). |
| Hardware Specification | Yes | Algorithm 1 was implemented in C and Table 1 gives a rough idea of the execution time for the case of two classes on a 3.07Ghz w3550 Xeon CPU (RAM 1.66 Go, Cache 8Mo). |
| Software Dependencies | No | The paper states 'Algorithm 1 was implemented in C' but does not provide specific version numbers for any software libraries, frameworks, or dependencies used in their implementation. |
| Experiment Setup | Yes | data were simulated according to a multinomial logit distribution. ... We have then generated matrices of rank equals to 5, such that Xj = Γ m1m2 k=1 αkuj k(vj k) , with (α1, .0.0. , α5) = (2, 1, 0.5, 0.25, 0.1) and Γ is a scaling factor. The choice of the λ parameter has been set for both methods by performing 5-fold cross-validation on a geometric grid of size 0.8 log(n). |