SINE: Scalable MPE Inference for Probabilistic Graphical Models using Advanced Neural Embeddings

Shivvrat Arya, Tahrima Rahman, Vibhav Giridhar Gogate
Proceedings of The 28th International Conference on Artificial Intelligence and Statistics, PMLR 258:4465-4473, 2025.

Abstract

Our paper builds on the recent trend of using neural networks trained with self-supervised or supervised learning to solve the Most Probable Explanation (MPE) task in discrete graphical models. At inference time, these networks take an evidence assignment as input and generate the most likely assignment for the remaining variables via a single forward pass. We address two key limitations of existing approaches: (1) the inability to fully exploit the graphical model’s structure and parameters, and (2) the suboptimal discretization of continuous neural network outputs. Our approach embeds model structure and parameters into a more expressive feature representation, significantly improving performance. Existing methods rely on standard thresholding, which often yields suboptimal results due to the non-convexity of the loss function. We introduce two methods to overcome discretization challenges: (1) an external oracle-based approach that infers uncertain variables using additional evidence from confidently predicted ones, and (2) a technique that identifies and selects the highest-scoring discrete solutions near the continuous output. Experimental results on various probabilistic models demonstrate the effectiveness and scalability of our approach, highlighting its practical impact.

Cite this Paper

BibTeX
@InProceedings{pmlr-v258-arya25a, title = {SINE: Scalable MPE Inference for Probabilistic Graphical Models using Advanced Neural Embeddings}, author = {Arya, Shivvrat and Rahman, Tahrima and Gogate, Vibhav Giridhar}, booktitle = {Proceedings of The 28th International Conference on Artificial Intelligence and Statistics}, pages = {4465--4473}, year = {2025}, editor = {Li, Yingzhen and Mandt, Stephan and Agrawal, Shipra and Khan, Emtiyaz}, volume = {258}, series = {Proceedings of Machine Learning Research}, month = {03--05 May}, publisher = {PMLR}, pdf = {https://raw.githubusercontent.com/mlresearch/v258/main/assets/arya25a/arya25a.pdf}, url = {https://proceedings.mlr.press/v258/arya25a.html}, abstract = {Our paper builds on the recent trend of using neural networks trained with self-supervised or supervised learning to solve the Most Probable Explanation (MPE) task in discrete graphical models. At inference time, these networks take an evidence assignment as input and generate the most likely assignment for the remaining variables via a single forward pass. We address two key limitations of existing approaches: (1) the inability to fully exploit the graphical model’s structure and parameters, and (2) the suboptimal discretization of continuous neural network outputs. Our approach embeds model structure and parameters into a more expressive feature representation, significantly improving performance. Existing methods rely on standard thresholding, which often yields suboptimal results due to the non-convexity of the loss function. We introduce two methods to overcome discretization challenges: (1) an external oracle-based approach that infers uncertain variables using additional evidence from confidently predicted ones, and (2) a technique that identifies and selects the highest-scoring discrete solutions near the continuous output. Experimental results on various probabilistic models demonstrate the effectiveness and scalability of our approach, highlighting its practical impact.} }
Endnote
%0 Conference Paper %T SINE: Scalable MPE Inference for Probabilistic Graphical Models using Advanced Neural Embeddings %A Shivvrat Arya %A Tahrima Rahman %A Vibhav Giridhar Gogate %B Proceedings of The 28th International Conference on Artificial Intelligence and Statistics %C Proceedings of Machine Learning Research %D 2025 %E Yingzhen Li %E Stephan Mandt %E Shipra Agrawal %E Emtiyaz Khan %F pmlr-v258-arya25a %I PMLR %P 4465--4473 %U https://proceedings.mlr.press/v258/arya25a.html %V 258 %X Our paper builds on the recent trend of using neural networks trained with self-supervised or supervised learning to solve the Most Probable Explanation (MPE) task in discrete graphical models. At inference time, these networks take an evidence assignment as input and generate the most likely assignment for the remaining variables via a single forward pass. We address two key limitations of existing approaches: (1) the inability to fully exploit the graphical model’s structure and parameters, and (2) the suboptimal discretization of continuous neural network outputs. Our approach embeds model structure and parameters into a more expressive feature representation, significantly improving performance. Existing methods rely on standard thresholding, which often yields suboptimal results due to the non-convexity of the loss function. We introduce two methods to overcome discretization challenges: (1) an external oracle-based approach that infers uncertain variables using additional evidence from confidently predicted ones, and (2) a technique that identifies and selects the highest-scoring discrete solutions near the continuous output. Experimental results on various probabilistic models demonstrate the effectiveness and scalability of our approach, highlighting its practical impact.
APA
Arya, S., Rahman, T. & Gogate, V.G.. (2025). SINE: Scalable MPE Inference for Probabilistic Graphical Models using Advanced Neural Embeddings. Proceedings of The 28th International Conference on Artificial Intelligence and Statistics, in Proceedings of Machine Learning Research 258:4465-4473 Available from https://proceedings.mlr.press/v258/arya25a.html.

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