Notice: 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. Full accuracy metrics and methodology are described in [1].
Faster Principal Component Regression and Stable Matrix Chebyshev Approximation
Authors: Zeyuan Allen-Zhu, Yuanzhi Li
ICML 2017 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We provide empirical evaluations in the full version of this paper. |
| Researcher Affiliation | Collaboration | 1Microsoft Reseaerch 2Princeton University. Correspondence to: Zeyuan Allen-Zhu <EMAIL>, Yuanzhi Li <EMAIL>. |
| Pseudocode | Yes | Since the high-level structure of our PCP algorithm is very clear, due to space limitation, we present the pseudocodes of our PCP and PCR algorithms in the full version. |
| Open Source Code | No | The paper states that a "Future version of this paper shall be found at https://arxiv.org/abs/1608.04773" and mentions that pseudocodes are in the "full version," but it does not provide an explicit statement or link to the open-source code for the methodology described in this paper. |
| Open Datasets | No | The paper mentions "a large-scale dataset" and references experiments in a "full version," but it does not provide concrete access information (link, DOI, repository name, or formal citation with authors/year) for any specific public dataset used. |
| Dataset Splits | No | The paper does not provide specific dataset split information (exact percentages, sample counts, citations to predefined splits, or detailed splitting methodology) for training, validation, or testing needed to reproduce the data partitioning. |
| Hardware Specification | No | The paper does not provide specific hardware details (exact GPU/CPU models, processor types with speeds, memory amounts, or detailed computer specifications) used for running its experiments. |
| Software Dependencies | No | The paper refers to various algorithms and methods like "SVRG (Johnson & Zhang, 2013)" and "Katyusha (Allen-Zhu, 2017)" but does not provide specific version numbers for these or any other software dependencies needed to replicate the experiment. |
| Experiment Setup | No | The paper states "We provide empirical evaluations in the full version of this paper," but it does not provide specific experimental setup details (concrete hyperparameter values, training configurations, or system-level settings) in the main text. |