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 [1].
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. |