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..
Efficient and Thrifty Voting by Any Means Necessary
Authors: Debmalya Mandal, Ariel D. Procaccia, Nisarg Shah, David Woodruff
NeurIPS 2019 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | Our results chart the Pareto frontier of the communication-distortion tradeoff. We propose a novel voting rule PREFTHRESHOLDt,ℓ, parametrized by t ∈ [m] and ℓ ∈ N. It is presented as Algorithm 1. ... The next theorem provides bounds on the communication and distortion of PREFTHRESHOLDt,ℓ. |
| Researcher Affiliation | Academia | Debmalya Mandal Columbia University EMAIL Ariel D. Procaccia Carnegie Mellon University EMAIL Nisarg Shah University of Toronto EMAIL David P. Woodruff Carnegie Mellon University EMAIL |
| Pseudocode | Yes | ALGORITHM 1: PREFTHRESHOLDt,ℓ, where t ∈ [m] and ℓ ∈ N. ... ALGORITHM 2: RANDSUBSET(f, s), where f is a voting rule and s ∈ [m] |
| Open Source Code | No | The paper does not mention providing open-source code for the described methodology. |
| Open Datasets | No | This paper focuses on theoretical analysis and does not use datasets for training or experimentation, so there is no mention of publicly available datasets. |
| Dataset Splits | No | This paper is theoretical and does not involve experimental validation on datasets, thus no dataset split information for validation is provided. |
| Hardware Specification | No | The paper is theoretical and does not describe experimental procedures that would require specific hardware, so no hardware specifications are mentioned. |
| Software Dependencies | No | The paper is theoretical and focuses on mathematical proofs and algorithm design, thus it does not list any specific software dependencies or version numbers. |
| Experiment Setup | No | This paper is theoretical and does not describe an experimental setup with hyperparameters or system-level training settings. |