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].
Weighted Regret-Based Likelihood: A New Approach to Describing Uncertainty
Authors: Joseph Y. Halpern
JAIR 2015 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | In this paper, a notion of comparative likelihood when uncertainty is represented by a set of weighted probability measures is defined. It generalizes the ordering defined by probability (and by lower probability) in a natural way; a generalization of upper probability can also be defined. A complete axiomatic characterization of this notion of regret-based likelihood is given. |
| Researcher Affiliation | Academia | Joseph Y. Halpern EMAIL Computer Science Department Cornell University Ithaca, NY 14853, USA |
| Pseudocode | No | The paper primarily focuses on theoretical definitions, theorems, and axiomatic characterizations. It does not contain any structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not contain any explicit statements about releasing source code, nor does it provide links to any code repositories or mention code in supplementary materials. This is a theoretical paper. |
| Open Datasets | No | The paper is theoretical and does not use any specific datasets. It discusses abstract concepts and provides illustrative examples without involving empirical data. |
| Dataset Splits | No | As no datasets are used in this theoretical paper, there is no mention of dataset splits for training, validation, or testing. |
| Hardware Specification | No | This paper presents theoretical work and does not involve running experiments that would require specific hardware. Therefore, no hardware specifications are provided. |
| Software Dependencies | No | The paper is theoretical and focuses on mathematical concepts and axiomatic characterization. It does not mention any software dependencies or specific version numbers for implementation. |
| Experiment Setup | No | The paper is purely theoretical, defining a new approach and providing an axiomatic characterization. There are no experiments described, and thus no experimental setup details, hyperparameters, or training configurations are present. |