Contextual Symmetries in Probabilistic Graphical Models
Authors: Ankit Anand, Aditya Grover, Mausam, Parag Singla
IJCAI 2016 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our experiments on several domains of interest demonstrate that exploiting contextual symmetries can result in significant computational gains.Our experiments evaluate the use of contextual symmetries for faster inference in graphical models. We compare our approach against Orbital MCMC, which is the only available algorithm that exploits symmetries in a general MCMC framework. We also compare with vanilla Gibbs sampling, which does not exploit any symmetries. |
| Researcher Affiliation | Academia | Ankit Anand Indian Institute of Technology, Delhi ankit.anand@cse.iitd.ac.in Aditya Grover Stanford University adityag@cs.stanford.edu Mausam and Parag Singla Indian Institute of Technology, Delhi {mausam,parags}@cse.iitd.ac.in |
| Pseudocode | No | The paper describes the CON-MCMC algorithm in detail (Section 4) but does not present it in a structured pseudocode block or algorithm box. |
| Open Source Code | Yes | We also release a reference implementation of CON-MCMC sampler for wider use.1 1https://github.com/dair-iitd/con-mcmc |
| Open Datasets | No | The paper describes custom-built domains ('Sports Network' and 'Young and Old') but does not provide access information (link, DOI, or formal citation) for any publicly available or open datasets used in the experiments. |
| Dataset Splits | No | The paper discusses various experimental conditions and domain characteristics, but it does not provide specific dataset split information (e.g., percentages, sample counts, or citations to predefined splits) for training, validation, or testing. |
| Hardware Specification | Yes | All the experiments are run on a quad-core Intel i-7 processor. |
| Software Dependencies | Yes | We implement CONMCMC( ) as an extension of the original Orbital MCMC implementation3 available in the GAP language [GAP, 2015]. The existing implementation uses Saucy [Darga et al., 2008] for graph isomorphism and Gibbs sampler as the base Markov chain. |
| Experiment Setup | Yes | We show CON-MCMC results for = 0 and 0.01, which was chosen based on performance on smaller problem sizes.Figure 4 shows the performance of CON-MCMC( ) for different values of in the range 0.001 to 0.5 for both Sports network (single) and Y &O (single) domains. |