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..
Lossless Compression of Efficient Private Local Randomizers
Authors: Vitaly Feldman, Kunal Talwar
ICML 2021 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Figure 1. Expected ℓ2 2 error of mechanisms Priv HS, Priv Unit, Priv Unit Optimized and SQKR for values of ε between 1 and 8. These plots show both Priv Unit and Priv Unit Optimized are more accurate than Priv HS and SQKR in the whole range of parameters |
| Researcher Affiliation | Industry | Vitaly Feldman 1 Kunal Talwar 1 1Apple. |
| Pseudocode | Yes | Algorithm 1 R[G, γ]: PRG compression of R ... Algorithm 2 PI-RAPPOR randomizer ... Algorithm 3 Server-side frequency for PI-RAPPOR |
| Open Source Code | No | The provided link is for an implementation of a Kashin-based mean estimation scheme, which is discussed as a related work and a baseline for comparison, not the authors' own methodology described in the paper. |
| Open Datasets | No | The paper describes empirical comparisons and uses parameters like d, n, and ε for these comparisons, but does not provide concrete access information (link, DOI, etc.) to a specific dataset used for training or evaluation in their experiments. |
| Dataset Splits | No | The paper discusses statistical settings and error metrics but does not specify any training, validation, or test dataset splits. |
| Hardware Specification | No | The paper does not provide specific details about the hardware (e.g., CPU, GPU models, memory) used to run the experiments. |
| Software Dependencies | No | The paper mentions an implementation from (Kas) for comparison but does not list specific software dependencies with version numbers for their own described methodology or experiments. |
| Experiment Setup | Yes | We show error bars for the empirical squared error based on 20 trials. ... For d = 1,000, n = 10,000 and ε taking integer values from 1 to 8. ... The Priv Unit algorithm internally splits its privacy budget ε into two parts ε0, ε1 = 1 ε0. ... optimize the splitting so as to minimize the variance proxy, by evaluating the expression for the variance proxy as a function of the θ = ε0/ε, for 101 values of θ = 0.00, 0.01, 0.02, . . . , 0.99, 1.0. |