Representing Aggregators in Relational Probabilistic Models
Authors: David Buchman, David Poole
AAAI 2015 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | We obtained surprisingly strong negative results on the capability of flexible undirected relational models such as MLNs to represent aggregators without affecting the original model s distribution. We provide a map of what aspects of the models, including the use of auxiliary variables and quantifiers, result in the ability to represent various aggregators. In addition, we provide proof techniques which can be used to facilitate future theoretic results on relational models, and demonstrate them on relational logistic regression (RLR). |
| Researcher Affiliation | Academia | David Buchman Department of Computer Science University of British Columbia Vancouver, BC, Canada davidbuc@cs.ubc.ca David Poole Department of Computer Science University of British Columbia Vancouver, BC, Canada poole@cs.ubc.ca |
| Pseudocode | No | The paper does not contain any structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide concrete access to source code for the methodology described. It only provides links to the authors' personal websites (www.cs.ubc.ca/~davidbuc, www.cs.ubc.ca/~poole) which do not explicitly state code availability for this work. |
| Open Datasets | No | The paper is theoretical and does not use or refer to any specific datasets. |
| Dataset Splits | No | The paper is theoretical and does not involve dataset splitting for empirical validation. |
| Hardware Specification | No | The paper is theoretical and does not report any experimental setup or specific hardware specifications. |
| Software Dependencies | No | The paper is theoretical and does not describe any specific software dependencies or versions. |
| Experiment Setup | No | The paper is theoretical and does not describe any experimental setup details such as hyperparameters or training configurations. |