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.
Sparse PCA with Oracle Property
Authors: Quanquan Gu, Zhaoran Wang, Han Liu
NeurIPS 2014 | Venue PDF | LLM Run Details | Input Tokens: 15,518 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 5,445 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 | We validate the theoretical results by numerical experiments on synthetic datasets. |
| Researcher Affiliation | Academia | Quanquan Gu Department of Operations Research and Financial Engineering Princeton University Princeton, NJ 08544, USA EMAIL Zhaoran Wang Department of Operations Research and Financial Engineering Princeton University Princeton, NJ 08544, USA EMAIL Han Liu Department of Operations Research and Financial Engineering Princeton University Princeton, NJ 08544, USA EMAIL |
| Pseudocode | Yes | Algorithm 1 Solving Convex Relaxation (5) using ADMM. |
| Open Source Code | No | No explicit statement providing concrete access to source code for the described methodology (e.g., repository link, code release statement) was found in the paper. |
| Open Datasets | No | The paper states: 'We generate two synthetic datasets via designing two covariance matrices.' It does not provide specific access information (link, DOI, repository, or formal citation) for a publicly available or open dataset. |
| Dataset Splits | Yes | The regularization parameter λ in our estimators as well as Fantope SPCA is tuned by 5-fold cross validation on a held-out dataset. |
| Hardware Specification | No | No specific hardware details (e.g., GPU/CPU models, memory amounts, or detailed computer specifications) used for running experiments are provided in the paper. |
| Software Dependencies | No | No specific ancillary software details (e.g., library or solver names with version numbers) are provided in the paper. |
| Experiment Setup | Yes | We sample n = 80 i.i.d. observations from a normal distribution N(0, Σ) with p = 128. Both of our estimators use MCP penalty... In particular, we set b = 3. For Convex SPCA, we set τ = 2b. The regularization parameter λ in our estimators as well as Fantope SPCA is tuned by 5-fold cross validation on a held-out dataset. |