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..
Differentially Private Decomposable Submodular Maximization
Authors: Anamay Chaturvedi, Huy Lê Nguyễn, Lydia Zakynthinou6984-6992
AAAI 2021 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We complement our theoretical bounds with experiments demonstrating improved empirical performance. |
| Researcher Affiliation | Academia | Khoury College of Computer Sciences, Northeastern University, Boston, Massachusetts 02115 |
| Pseudocode | Yes | Algorithm 1 Private Continuous Greedy |
| Open Source Code | Yes | The code and dataset used for our experiments are available at https://github.com/Anamay-Chaturvedi/Differentially-privatedecomposable-submodular-optimization |
| Open Datasets | Yes | We use the same dataset of coordinates of Uber pick-ups2. 2https://www.kaggle.com/fivethirtyeight/uber-pickups-in-newyork-city. |
| Dataset Splits | No | The paper does not provide specific train/validation/test dataset splits. It describes a resampling strategy for evaluation. |
| Hardware Specification | No | The paper mentions 'on a personal computer' but does not provide any specific hardware details such as GPU/CPU models or memory specifications. |
| Software Dependencies | No | The paper does not list any specific software dependencies with version numbers. |
| Experiment Setup | Yes | In PCG, we set η = 0.33 and use the closed-form expression for the multilinear relaxation of f D. We set ε = 0.1, δ = 1/m1.5 where m = |D| = 100, with which the privacy parameter used in the differentially private choices of increment is ε0 0.01006. |