Is Approval Voting Optimal Given Approval Votes?
Authors: Ariel D. Procaccia, Nisarg Shah
NeurIPS 2015 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | While the answer is generally positive, our theoretical and empirical results call attention to situations where approval voting is suboptimal. ... And our experiments, using real data, show that the accuracy of approval voting is usually quite close to that of the MLE in pinpointing the best alternative. |
| Researcher Affiliation | Academia | Ariel D. Procaccia Computer Science Department Carnegie Mellon University arielpro@cs.cmu.edu Nisarg Shah Computer Science Department Carnegie Mellon University nkshah@cs.cmu.edu |
| Pseudocode | No | The paper does not include any explicitly labeled 'Pseudocode' or 'Algorithm' blocks. The proofs contain mathematical derivations. |
| Open Source Code | No | The paper does not contain an explicit statement or link indicating that the source code for the described methodology is publicly available. |
| Open Datasets | Yes | We perform experiments with two real-world datasets Dots and Puzzle [20]... Mao et al. [20] collected these datasets... |
| Dataset Splits | Yes | We partition the set of proļ¬les in each noise level of each dataset into training (90%) and test (10%) sets. |
| Hardware Specification | No | The paper does not provide any specific details about the hardware used for running experiments, such as GPU/CPU models or memory specifications. |
| Software Dependencies | No | The paper does not specify any software dependencies with version numbers (e.g., specific programming languages, libraries, or solvers with their versions). |
| Experiment Setup | No | The paper mentions that 'the MLE rule learns the probabilities of observing each of the 6 possible 2-subsets of the alternatives', but it does not provide specific hyperparameter values or detailed training configurations. |