Notice: This website is continuously updated as new conference proceedings and journal volumes are published. Consequently, the figures presented here may differ from those reported in our publications.
A brief overview of our methodology is provided on the Methods page, along with references to publications documenting our approach and links to the code and data used in each study.
The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates.
A Unified Framework for Structured Low-rank Matrix Learning
Authors: Pratik Jawanpuria, Bamdev Mishra
ICML 2018 | Venue PDF | LLM Run Details | Input Tokens: 19,296 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 4,583 Total number of tokens produced by the LLM (including reasoning/thinking tokens) for this paper's analysis.
| 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 <EMAIL>, Bamdev Mishra <EMAIL>. |
| 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. |