A Unified Framework for Structured Low-rank Matrix Learning
Authors: Pratik Jawanpuria, Bamdev Mishra
ICML 2018 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments on problems such as standard/robust/non-negative matrix completion, Hankel matrix learning and multi-task learning demonstrate the efficacy of our approach. In this section, we evaluate the generalization performance as well as computational efficiency of our approach against state-of-the-art in different applications. |
| Researcher Affiliation | Industry | Microsoft, India. Correspondence to: Pratik Jawanpuria <pratik.jawanpuria@microsoft.com>, Bamdev Mishra <bamdevm@microsoft.com>. |
| Pseudocode | Yes | Algorithm 1 Proposed firstand second-order algorithms for (3) |
| Open Source Code | Yes | Our codes are available at https://pratikjawanpuria.com/. |
| Open Datasets | Yes | Netflix (Recht and R e, 2013), Movie Lens10m (ML10m), and Movie Lens20m (ML20m) (Harper and Konstan, 2015). |
| Dataset Splits | No | For every data set, we create five random 80/20 train/test splits. (No explicit mention of a validation split.) |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., CPU/GPU models, memory) used for running the experiments. |
| Software Dependencies | Yes | All our algorithms are implemented using the Manopt toolbox (Boumal et al., 2014). |
| Experiment Setup | Yes | For every split, the regularization parameters for respective algorithms are cross-validated to obtain their best performance. All the fixed algorithms (R3MC, LMa Fit, MMBS, RTRMC, RSLM) are provided the rank r = 10. The rank r for both RSLM and RMC is fixed at r = 10. |