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].
Learning Abduction Using Partial Observability
Authors: Brendan Juba, Zongyi Li, Evan Miller
AAAI 2018 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | In this work, we consider a learning to reason (Khardon and Roth 1997) or PAC-learning (Valiant 1984; 2000) formulation of the combined task of learning to abduce, introduced by Juba (2016). |
| Researcher Affiliation | Academia | Brendan Juba, Zongyi Li, Evan Miller Dept. of Computer Science and Engineering Washington University in St. Louis {bjuba, zli, evan.a.miller} @wustl.edu |
| Pseudocode | Yes | Algorithm 1: Decide PAC |
| Open Source Code | No | The paper does not contain any explicit statements about releasing source code or provide links to a code repository for the methodology described. |
| Open Datasets | No | This paper is theoretical and focuses on a PAC-learning framework with conceptual 'examples drawn from the prior distribution' rather than using a specific, publicly available dataset for empirical evaluation. |
| Dataset Splits | No | As a theoretical paper, no empirical experiments with validation dataset splits are described. |
| Hardware Specification | No | The paper is theoretical and does not describe any experiments that would require specific hardware, thus no hardware specifications are provided. |
| Software Dependencies | No | The paper does not provide specific software dependencies with version numbers for replication. |
| Experiment Setup | No | The paper is theoretical and presents algorithms and proofs, but does not describe any empirical experimental setups or hyperparameter details. |