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 Differentially Private Clustering Algorithm for Well-Clustered Graphs
Authors: Weiqiang He, Hendrik Fichtenberger, Pan Peng
ICLR 2024 | Venue PDF | LLM Run Details | Input Tokens: 21,841 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 3,140 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 conduct experimental evaluations on datasets with known ground truth clusters to substantiate the prowess of our algorithm. We also show that any (pure) ϵ-DP algorithm would result in substantial error. |
| Researcher Affiliation | Collaboration | 1School of Computer Science and Technology, University of Science and Technology of China 2Google Research |
| Pseudocode | Yes | Algorithm 1: Private Clustering |
| Open Source Code | No | The paper does not provide an explicit statement or link to open-source code for the described methodology. |
| Open Datasets | No | The paper samples synthetic datasets from a stochastic block model and does not provide a public link, DOI, or specific citation to make the generated datasets publicly available. |
| Dataset Splits | No | The paper mentions sampling SBM graphs and running algorithms multiple times but does not specify any training, validation, or test dataset splits. |
| Hardware Specification | Yes | The experiments were run on a single Google Cloud n2-standard-128 instance. |
| Software Dependencies | Yes | We model the SDP (2) in CVXPY 1.3.2 and use SCS 3.2.3 as SDP solver. We use NumPy 1.23.5 for numerical computation and scikit-learn 1.3 for an implementation of k-means++. |
| Experiment Setup | Yes | For our algorithm, we use b = (k 1)/k and λ = c q mϵ2 n log(2/δ), where c is a trade-off constant that we fix for each SBM parameterization before sampling the input datasets. For randomized response, we replace the objective with arg min X D LG, X , i.e., we remove the regularizer term, and set p RR = 1/(1+eϵ). For our experiments, we use ϵ = 1 and δ = 1/n2. |