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..
Exact Optimality of Communication-Privacy-Utility Tradeoffs in Distributed Mean Estimation
Authors: Berivan Isik, Wei-Ning Chen, Ayfer Ozgur, Tsachy Weissman, Albert No
NeurIPS 2023 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We empirically demonstrate the communication-privacy-utility tradeoffs of RRSC and compare it with order-optimal schemes under privacy and communication constraints, namely SQKR [6] and MMRC [30]. |
| Researcher Affiliation | Academia | Berivan Isik Stanford University EMAIL Wei-Ning Chen Stanford University EMAIL Ayfer Ozgur Stanford University EMAIL Tsachy Weissman Stanford University EMAIL Albert No Hongik University EMAIL |
| Pseudocode | Yes | We call this approach Randomly Rotating Simplex Coding (RRSC) and provide the pseudocode in Algorithm 1. |
| Open Source Code | Yes | The codebase for this work is open-sourced at https://github.com/Berivan Isik/rrsc. |
| Open Datasets | No | The paper uses synthetically generated data: 'More precisely, for the first half of the users, we set v1, . . . , vn/2 i.i.d N(1, 1) d; and for the second half of the users, we set vn/2+1, . . . , vn i.i.d N(10, 1) d. We further normalize each vi to ensure that they lie on Sd 1.' It does not provide access information for a publicly available dataset. |
| Dataset Splits | No | The paper describes the generation of synthetic data but does not specify train/validation/test splits or cross-validation for experimental reproduction. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., 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, such as library or solver names with version numbers, needed to replicate the experiment. |
| Experiment Setup | Yes | To find the optimal values for k and rk, we compute the optimal rk using the formula in (33) for k = 1, . . . , M and pick the k that gives the smallest rk (which corresponds to the bias). To estimate the expectation Ck in (33), we run a Monte Carlo simulation with 1M trials. |