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..
Convex Elicitation of Continuous Properties
Authors: Jessica Finocchiaro, Rafael Frongillo
NeurIPS 2018 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | Our proof is constructive, and leads to convex loss functions for new properties. |
| Researcher Affiliation | Academia | Jessica Finocchiaro Department of Computer Science University of Colorado, Boulder EMAIL Rafael Frongillo Department of Computer Science University of Colorado, Boulder EMAIL |
| Pseudocode | No | The paper contains mathematical derivations and proofs but no pseudocode or clearly labeled algorithm blocks. |
| Open Source Code | No | The paper does not provide any statement about making source code available or include a link to a code repository. |
| Open Datasets | No | This paper is theoretical and does not use or reference any specific datasets for training models. It discusses theoretical properties of distributions. |
| Dataset Splits | No | This paper is theoretical and does not involve experimental validation on datasets, thus no dataset split information is provided. |
| Hardware Specification | No | This is a theoretical paper that does not describe computational experiments, and therefore no hardware specifications are mentioned. |
| Software Dependencies | No | This is a theoretical paper and does not involve computational experiments requiring specific software dependencies with version numbers. |
| Experiment Setup | No | This is a theoretical paper and does not describe an experimental setup with hyperparameters or training configurations. |