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..
Privacy Amplification by Iteration for ADMM with (Strongly) Convex Objective Functions
Authors: T-H. Hubert Chan, Hao Xie, Mengshi Zhao
AAAI 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Despite being primarily theoretical, we conduct experiments on a general Lasso problem. Specifically, we empirically examine the effects of strong convexity and privacy noise magnitude on convergence rates. |
| Researcher Affiliation | Academia | T-H. Hubert Chan*, Hao Xie*, Mengshi Zhao* Department of Computer Science, The University of Hong Kong EMAIL, EMAIL, EMAIL |
| Pseudocode | Yes | Algorithm 1: One ADMM Iteration. Algorithm 2: Mechanism M1. Algorithm 3: Mechanism M2. |
| Open Source Code | No | The paper does not include an unambiguous statement that the authors are releasing the code for the work described in this paper, nor does it provide a direct link to a source-code repository. |
| Open Datasets | No | The paper mentions conducting 'experiments on a general Lasso problem' and a 'numerical illustration of our algorithms on a general Lasso problem'. This describes the problem type but does not specify a concrete, publicly available dataset with a link, DOI, or citation. |
| Dataset Splits | No | The paper does not provide specific details on training, validation, or test dataset splits (e.g., percentages, sample counts, or references to predefined splits). |
| Hardware Specification | No | The paper does not explicitly describe any specific hardware specifications (e.g., GPU/CPU models, memory amounts, or detailed cloud/cluster configurations) used for running experiments. |
| Software Dependencies | No | The paper does not list specific software components with their version numbers (e.g., Python 3.x, PyTorch 1.x, CUDA x.x, or specific solver versions). |
| Experiment Setup | No | The paper does not provide specific hyperparameter values (e.g., learning rate, batch size, number of epochs) or detailed system-level training settings. It only generally refers to 'details are given in the full version' for experimental results. |