An end-to-end Differentially Private Latent Dirichlet Allocation Using a Spectral Algorithm
Authors: Chris Decarolis, Mukul Ram, Seyed Esmaeili, Yu-Xiang Wang, Furong Huang
ICML 2020 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We conduct empirical studies with synthetic and real-life datasets, which confirm that the DP spectral algorithm systematically outperforms DP variational inference. |
| Researcher Affiliation | Academia | 1Department of Computer Science, University of Maryland 2Department of Computer Science, UC Santa Barbara. Correspondence to: Furong Huang <furongh@cs.umd.edu>. |
| Pseudocode | Yes | Procedure 1 ( 1 + 0 1 + , δ1 + δ0 1 + δ)-Differential Privacy (DP) Noise Calibration |
| Open Source Code | No | The paper does not provide any explicit statements about releasing source code or links to a code repository. |
| Open Datasets | No | The paper mentions using "Wikipedia Dataset" but does not provide a specific link, DOI, repository name, or formal citation with authors/year for accessing it publicly. |
| Dataset Splits | No | The paper does not explicitly provide specific train/validation/test dataset splits (e.g., exact percentages, sample counts, or citations to predefined splits). |
| 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 | 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 | To evaluate Configuration 1, we set the vocabulary size and the number of topics to be small (d = 50, k = 5) in our synthetic settings. The vocabulary size is truncated to be d = 8000. Number of words d = 8000, number of documents N = 50000, 0 = 0.01. |