Tractability through Exchangeability: A New Perspective on Efficient Probabilistic Inference
Authors: Mathias Niepert, Guy Van den Broeck
AAAI 2014 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | We develop a theory of finite exchangeability and its relation to tractable probabilistic inference. The theory is complementary to that of independence and conditional independence. We show that tractable inference in probabilistic models with high treewidth and millions of variables can be explained with the notion of finite (partial) exchangeability. We also show that existing lifted inference algorithms implicitly utilize a combination of conditional independence and partial exchangeability. |
| Researcher Affiliation | Academia | Mathias Niepert Computer Science and Engineering University of Washington, Seattle mniepert@cs.washington.edu Guy Van den Broeck Computer Science Department University of California, Los Angeles guyvdb@cs.ucla.edu Guy Van den Broeck is also at KU Leuven, Belgium. |
| Pseudocode | No | The paper does not contain any pseudocode or clearly labeled algorithm blocks. |
| Open Source Code | No | The paper does not contain any statements about releasing code or links to source code repositories. |
| Open Datasets | No | The paper describes theoretical concepts and case studies using Markov Logic Networks, but it does not mention specific datasets used for training or evaluation, nor does it provide access information for any dataset. |
| Dataset Splits | No | This paper is theoretical and does not conduct experiments involving data splits for training, validation, or testing. |
| Hardware Specification | No | This paper is theoretical and does not describe any hardware used for experiments. |
| Software Dependencies | No | This paper is theoretical and does not mention any specific software dependencies with version numbers related to experimental work. |
| Experiment Setup | No | This paper is theoretical and does not describe an experimental setup with hyperparameters or training settings. |