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 [1].
Measuring the Likelihood of Numerical Constraints
Authors: Marco Console, Matthias Hofer, Leonid Libkin
IJCAI 2019 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | Our goal is to measure the likelihood of the satisfaction of numerical constraints in the absence of prior information. We study expressive constraints... We show that for constraints on n variables, the proper way to deο¬ne such a measure is as the limit... We prove that the existence of such a limit is closely related to the notion of o-minimality from model theory... We look at computing and approximating such likelihoods for order and linear constraints, and prove an impossibility result for approximating with multiplicative error. However, as the likelihood is a number between 0 and 1, an approximation scheme with additive error is acceptable, and we give it for arbitrary linear constraints. |
| Researcher Affiliation | Academia | Marco Console , Matthias Hofer and Leonid Libkin School of Informatics, University of Edinburgh EMAIL |
| Pseudocode | Yes | Algorithm 1 apx-mes |
| Open Source Code | No | The paper does not contain any statement about open-source code availability nor does it provide a link. |
| Open Datasets | No | The paper is theoretical, focused on mathematical definitions, proofs, and algorithm design. It does not involve any empirical training on datasets. |
| Dataset Splits | No | The paper is theoretical and does not involve empirical evaluation with data splits. |
| Hardware Specification | No | The paper is purely theoretical and does not report on any empirical experiments or the hardware used to conduct them. |
| Software Dependencies | No | The paper is theoretical and focuses on mathematical concepts and algorithm design. It does not list specific software dependencies with version numbers required for implementation or replication. |
| Experiment Setup | No | The paper is theoretical and does not describe any empirical experiments, thus no experimental setup details like hyperparameters are provided. |