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..
Preferences Single-Peaked on a Tree: Sampling and Tree Recognition
Authors: Jakub Sliwinski, Edith Elkind
IJCAI 2019 | Venue PDF | 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 EMAIL, EMAIL |
| 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 pro๏ฌle V with 2mฮฑ 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. |