Preferences Single-Peaked on a Tree: Sampling and Tree Recognition
Authors: Jakub Sliwinski, Edith Elkind
IJCAI 2019 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We test our algorithm empirically; to this end, we develop a procedure to uniformly sample preferences that are singlepeaked on a given tree. |
| Researcher Affiliation | Academia | 1ETH Zurich 2University of Oxford jsliwinski@ethz.ch, elkind@cs.ox.ac.uk |
| Pseudocode | Yes | Algorithm 1 Sample v single-peaked on T with v[1] = c; Algorithm 2 Sample v single-peaked on T with v[1] = c; Algorithm 3 Compute Pr(v[1] = k) for k = 1, . . . , m given the tree T = ({1, . . . , m}, E); Algorithm 4 Build attachment digraph D = (C, A) of V; Algorithm 5 Guess T, given a profile V with 2mα votes; Algorithm 6 Try to sample a V such that E1,V E2,V holds for given c, p and f |
| Open Source Code | No | The paper does not provide any statement or link indicating that the source code for the described methodology is publicly available. |
| Open Datasets | No | The paper describes generating data through a sampling algorithm for preferences single-peaked on a tree (U(T)), which is not an external, publicly available dataset. It does not provide access information for such a dataset. |
| Dataset Splits | No | The paper does not specify traditional training/validation/test dataset splits. It discusses sampling votes for tree identification but not data partitioning for model validation in the machine learning sense. |
| Hardware Specification | No | The paper does not specify any hardware details (e.g., CPU, GPU models, memory, or cloud instances) used for running the experiments. |
| Software Dependencies | No | The paper does not provide specific software dependencies with version numbers (e.g., programming languages, libraries, or solvers). |
| Experiment Setup | No | The paper describes general experimental parameters like the number of trials (2000) and types of trees, but it does not include specific hyperparameters or detailed system-level training settings for the algorithms used in the experiments. |