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 Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
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 | Venue PDF | 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 <EMAIL>. |
| 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. |