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..
Elicitation for Preferences Single Peaked on Trees
Authors: Palash Dey, Neeldhara Misra
IJCAI 2016 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | We study the query complexity for preference elicitation when the preference proο¬le is single peaked on a tree. We provide tight connections between various parameters of the underlying tree and the query complexity for preference elicitation. |
| Researcher Affiliation | Academia | Palash Dey Indian Institute of Science, Bangalore EMAIL Neeldhara Misra Indian Institute of Technology, Gandhinagar EMAIL |
| Pseudocode | No | The paper describes algorithmic strategies and proofs in prose (e.g., 'The idea is to partition the tree into k disjoint paths, use the algorithm from Theorem 1...'), but it does not include formal pseudocode blocks or algorithm listings. |
| Open Source Code | No | The paper does not provide any links to source code repositories or explicitly state that the code for their described methodology is being released. |
| Open Datasets | No | The paper is theoretical and does not use or reference any datasets for training or evaluation. Therefore, no information on public dataset access is relevant or provided. |
| Dataset Splits | No | The paper is theoretical and does not involve experimental data or dataset splits for training, validation, or testing. |
| Hardware Specification | No | The paper is theoretical and does not describe any experimental setup that would require hardware specifications. |
| Software Dependencies | No | The paper is theoretical and does not describe any computational experiments that would require specific software dependencies with version numbers. |
| Experiment Setup | No | The paper is theoretical and focuses on mathematical proofs and algorithmic analysis rather than empirical experiments, hence no experimental setup details like hyperparameters or training configurations are provided. |