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..
On Elicitation Complexity
Authors: Rafael Frongillo, Ian Kash
NeurIPS 2015 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | Building on previous work, we introduce a new notion of elicitation complexity and lay the foundations for a calculus of elicitation. We establish several general results and techniques for proving upper and lower bounds on elicitation complexity. |
| Researcher Affiliation | Collaboration | Rafael Frongillo University of Colorado, Boulder EMAIL Ian A. Kash Microsoft Research EMAIL |
| Pseudocode | No | The paper does not contain any structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper is theoretical and does not present a new software methodology or provide any statements about releasing open-source code. |
| Open Datasets | No | The paper is theoretical and does not involve empirical studies or dataset usage for training. |
| Dataset Splits | No | The paper is theoretical and does not involve empirical studies or dataset usage for validation. |
| Hardware Specification | No | The paper is theoretical and does not describe computational experiments that would require specific hardware. |
| Software Dependencies | No | The paper is theoretical and does not mention any software dependencies with specific version numbers. |
| Experiment Setup | No | The paper is theoretical and does not describe any experimental setup details such as hyperparameters or training configurations. |