Online Optimization for Max-Norm Regularization
Authors: Jie Shen, Huan Xu, Ping Li
NeurIPS 2014 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In this section, we report some simulation results on synthetic data to demonstrate the effectiveness and robustness of our online max-norm regularized matrix decomposition (OMRMD) algorithm. |
| Researcher Affiliation | Academia | Jie Shen Dept. of Computer Science Rutgers University Piscataway, NJ 08854 js2007@rutgers.edu Huan Xu Dept. of Mech. Engineering National Univ. of Singapore Singapore 117575 mpexuh@nus.edu.sg Ping Li Dept. of Statistics Dept. of Computer Science Rutgers University pingli@stat.rutgers.edu |
| Pseudocode | Yes | Algorithm 1 Online Max-Norm Regularized Matrix Decomposition |
| Open Source Code | No | The paper does not provide concrete access to source code for the methodology described, nor does it explicitly state that the code is available. |
| Open Datasets | No | The simulation data are generated by following a similar procedure in [6]. The paper describes a data generation procedure for synthetic data, but does not provide access information (link, DOI, repository, or explicit public dataset name with attribution) to a pre-existing publicly available dataset. |
| Dataset Splits | No | The paper mentions 'total number of samples n = 5000' but does not provide specific dataset split information (exact percentages, sample counts, or a detailed splitting methodology) needed to reproduce the data partitioning into train, validation, or test sets. |
| Hardware Specification | No | The paper does not provide specific hardware details (exact GPU/CPU models, processor types, or memory amounts) used for running its experiments. |
| Software Dependencies | No | The paper does not provide specific ancillary software details (e.g., library or solver names with version numbers) needed to replicate the experiment. |
| Experiment Setup | Yes | We set the ambient dimension p = 400 and the total number of samples n = 5000 unless otherwise specified. We fix the tunable parameter λ1 = λ2 = 1/ p, and use default parameters for all baseline algorithms we compare with. |