Non-parametric Domain Approximation for Scalable Gibbs Sampling in MLNs

Deepak Venugopal, Somdeb Sarkhel, Kyle Cherry
Proceedings of the 32nd Conference on Uncertainty in Artificial Intelligence, PMLR R14:831-840, 2016.

Abstract

MLNs utilize relational structures that are ubiquitous in real-world situations to represent large probabilistic graphical models compactly. However, as is now well-known, inference complexity is one of the main bottlenecks in MLNs. Recently, several approaches have been proposed that exploit approximate symmetries in the MLN to reduce inference complexity. These approaches approximate large domains containing many objects with much smaller domains of meta-objects (or cluster-centers), so that inference is considerably faster and more scalable. However, a drawback in most of these approaches is that it is typically very hard to tune the parameters (e.g., number of clusters) such that inference is both efficient and accurate. Here, we propose a novel non-parametric approach that trades-off solution quality with efficiency to automatically learn the optimal domain approximation. Further, we show how to perform Gibbs sampling effectively in a domain-approximated MLN by adapting the sampler according to the approximation. Our results on several benchmarks show that our approach is scalable, accurate and converges faster than existing methods.

Cite this Paper

BibTeX
@InProceedings{pmlr-vR14-venugopal16a, title = {Non-parametric Domain Approximation for Scalable {G}ibbs Sampling in MLNs}, author = {Venugopal, Deepak and Sarkhel, Somdeb and Cherry, Kyle}, booktitle = {Proceedings of the 32nd Conference on Uncertainty in Artificial Intelligence}, pages = {831--840}, year = {2016}, editor = {Ihler, Alexander and Janzing, Dominik}, volume = {R14}, series = {Proceedings of Machine Learning Research}, month = {25--29 Jun}, publisher = {PMLR}, pdf = {https://raw.githubusercontent.com/mlresearch/r14/main/assets/venugopal16a/venugopal16a.pdf}, url = {https://proceedings.mlr.press/r14/venugopal16a.html}, abstract = {MLNs utilize relational structures that are ubiquitous in real-world situations to represent large probabilistic graphical models compactly. However, as is now well-known, inference complexity is one of the main bottlenecks in MLNs. Recently, several approaches have been proposed that exploit approximate symmetries in the MLN to reduce inference complexity. These approaches approximate large domains containing many objects with much smaller domains of meta-objects (or cluster-centers), so that inference is considerably faster and more scalable. However, a drawback in most of these approaches is that it is typically very hard to tune the parameters (e.g., number of clusters) such that inference is both efficient and accurate. Here, we propose a novel non-parametric approach that trades-off solution quality with efficiency to automatically learn the optimal domain approximation. Further, we show how to perform Gibbs sampling effectively in a domain-approximated MLN by adapting the sampler according to the approximation. Our results on several benchmarks show that our approach is scalable, accurate and converges faster than existing methods.}, note = {Reissued by PMLR on 04 October 2026.} }
Endnote
%0 Conference Paper %T Non-parametric Domain Approximation for Scalable Gibbs Sampling in MLNs %A Deepak Venugopal %A Somdeb Sarkhel %A Kyle Cherry %B Proceedings of the 32nd Conference on Uncertainty in Artificial Intelligence %C Proceedings of Machine Learning Research %D 2016 %E Alexander Ihler %E Dominik Janzing %F pmlr-vR14-venugopal16a %I PMLR %P 831--840 %U https://proceedings.mlr.press/r14/venugopal16a.html %V R14 %X MLNs utilize relational structures that are ubiquitous in real-world situations to represent large probabilistic graphical models compactly. However, as is now well-known, inference complexity is one of the main bottlenecks in MLNs. Recently, several approaches have been proposed that exploit approximate symmetries in the MLN to reduce inference complexity. These approaches approximate large domains containing many objects with much smaller domains of meta-objects (or cluster-centers), so that inference is considerably faster and more scalable. However, a drawback in most of these approaches is that it is typically very hard to tune the parameters (e.g., number of clusters) such that inference is both efficient and accurate. Here, we propose a novel non-parametric approach that trades-off solution quality with efficiency to automatically learn the optimal domain approximation. Further, we show how to perform Gibbs sampling effectively in a domain-approximated MLN by adapting the sampler according to the approximation. Our results on several benchmarks show that our approach is scalable, accurate and converges faster than existing methods. %Z Reissued by PMLR on 04 October 2026.
APA
Venugopal, D., Sarkhel, S. & Cherry, K.. (2016). Non-parametric Domain Approximation for Scalable Gibbs Sampling in MLNs. Proceedings of the 32nd Conference on Uncertainty in Artificial Intelligence, in Proceedings of Machine Learning Research R14:831-840 Available from https://proceedings.mlr.press/r14/venugopal16a.html. Reissued by PMLR on 04 October 2026.

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