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..
Regression with Label Differential Privacy
Authors: Badih Ghazi, Pritish Kamath, Ravi Kumar, Ethan Leeman, Pasin Manurangsi, Avinash Varadarajan, Chiyuan Zhang
ICLR 2023 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We carry out a thorough experimental evaluation on several datasets demonstrating the efficacy of our algorithm. |
| Researcher Affiliation | Industry | Email: EMAIL, EMAIL |
| Pseudocode | Yes | Algorithm 1 RR-on-BinsΦ ε. [...] Algorithm 2 Compute optimal Φ for RR-on-BinsΦ ε. [...] Algorithm 3 Labels Party s Randomizer Label Randomizerε1,ε2. |
| Open Source Code | No | The paper does not provide concrete access to source code for the methodology described in this paper. |
| Open Datasets | Yes | The Criteo Sponsored Search Conversion Log Dataset (Tallis & Yadav, 2018) is publicly available from https://ailab. criteo.com/criteo-sponsored-search-conversion-log-dataset/. [...] The 1940 US Census dataset has been made publicly available for research since 2012 |
| Dataset Splits | No | The paper mentions "80% 20% train test splits" but does not specify a validation dataset split. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., GPU/CPU models, processor types) used for running its experiments. |
| Software Dependencies | No | The paper mentions software like "Tensor Flow Privacy" and "Py Torch Opacus" but does not specify version numbers for these or other relevant libraries/solvers. |
| Experiment Setup | Yes | We use learning rate 0.001 with cosine decay (Loshchilov & Hutter, 2017), batch size 8192, and train for 50 epochs. [...] We use learning rate 0.001 with cosine decay (Loshchilov & Hutter, 2017), batch size 8192, and train for 200 epochs. |