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.
DGD^2: A Linearly Convergent Distributed Algorithm For High-dimensional Statistical Recovery
Authors: Marie Maros, Gesualdo Scutari
NeurIPS 2022 | Venue PDF | LLM Run Details | Input Tokens: 6,426 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 4,681 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 | To demonstrate the effectiveness of DGD2, we conduct numerical experiments on various high-dimensional statistical recovery problems including group Lasso, sparse logistic regression, and sparse covariance matrix estimation. |
| Researcher Affiliation | Collaboration | Zheng Qu University of California, Berkeley EMAIL... Yang Shen Microsoft Research EMAIL... Jelena Kovacevic Carnegie Mellon University EMAIL... Mingyi Hong University of Minnesota EMAIL |
| Pseudocode | Yes | Algorithm 1: DGD2 for distributed statistical recovery |
| Open Source Code | No | The paper does not provide any explicit statements about releasing source code or links to a code repository for the described methodology. |
| Open Datasets | Yes | For sparse logistic regression, we downloaded the RCV1 dataset from LIBSVM Data [37]. For sparse covariance matrix estimation, we applied DGD2 to the Million Song Dataset, which is obtained from the UCI Machine Learning Repository. |
| Dataset Splits | No | The paper does not provide specific details on training, validation, and test dataset splits (e.g., percentages, sample counts, or explicit splitting methodology) needed for reproduction. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., exact GPU/CPU models, memory amounts, or detailed computer specifications) used for running its experiments. |
| Software Dependencies | Yes | All simulations are implemented in MATLAB R2018a. |
| Experiment Setup | Yes | Unless otherwise specified, for all experiments, the stepsize is set to Îłk = 0.001 and the regularization parameter Îť = 0.001. The maximum iteration number is 5000. |