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].
Weighted Voting Via No-Regret Learning
Authors: Nika Haghtalab, Ritesh Noothigattu, Ariel Procaccia
AAAI 2018 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | We derive possibility and impossibility results for the existence of such weighting schemes, depending on whether the voting rule and the weighting scheme are deterministic or randomized, as well as on the social choice axioms satisfied by the voting rule. |
| Researcher Affiliation | Academia | Nika Haghtalab Computer Science Department Carnegie Mellon University Pittsburgh, PA 15213 EMAIL Ritesh Noothigattu Machine Learning Department Carnegie Mellon University Pittsburgh, PA 15213 EMAIL Ariel D. Procaccia Computer Science Department Carnegie Mellon University Pittsburgh, PA 15213 EMAIL |
| Pseudocode | Yes | Algorithm 1: Full information setting, using randomized weights. ... Algorithm 2: Partial information setting, using randomized weights. |
| Open Source Code | No | The paper does not provide any information or links regarding the availability of its source code. |
| Open Datasets | No | The paper is theoretical and focuses on mathematical derivations and algorithm design; it does not describe the use of any datasets for training. |
| Dataset Splits | No | The paper is theoretical and does not involve empirical validation on datasets, thus no mention of validation splits is made. |
| Hardware Specification | No | The paper is theoretical and does not describe any empirical experiments, thus no hardware specifications are mentioned. |
| Software Dependencies | No | The paper is theoretical and does not describe an implementation or empirical experiments that would require specific software dependencies with version numbers. |
| Experiment Setup | No | The paper is theoretical and does not describe an experimental setup with hyperparameters or system-level training settings. |