On Elicitation Complexity

Authors: Rafael Frongillo, Ian Kash

NeurIPS 2015 | Conference PDF | Archive PDF | Plain Text | 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 raf@colorado.edu Ian A. Kash Microsoft Research iankash@microsoft.com
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.