Convex Elicitation of Continuous Properties
Authors: Jessica Finocchiaro, Rafael Frongillo
NeurIPS 2018 | Conference PDF | Archive PDF | Plain Text | 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 jessica.finocchiaro@colorado.edu Rafael Frongillo Department of Computer Science University of Colorado, Boulder raf@colorado.edu |
| 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. |