Call For Papers

Scope

Tractable probabilistic modeling (TPM) is concerned with the inherent trade-off between the expressivity of the probabilistic models and the complexity of performing various types of inference on them, as well as learning them from data.

Traditional topics in this area include efficient learning of probabilistic models, exact inference, and approximate routines with guarantees. Relevant model classes include low- and bounded-treewidth PGM, determinantal point processes, exchangeable probabilistic models, arithmetic circuits, sum-product networks, cutset networks, probabilistic sentential decision diagrams, and more. Successful real-world applications of such models comprise: image classification, completion and generation, scene understanding, activity recognition, language and speech modeling, bioinformatics, collaborative filtering, verification and diagnosis of physical systems.

This year's workshop will focus especially on bringing together researchers working on the different fronts and communities of TPM. We especially encourage submissions highlighting the challenges and opportunities for tractable inference and modeling within the rising field of probabilistic programming and the neural probabilistic modeling community, recently achieving impressive successes in many application fields.

Submissions can be made through the workshop page:

TPM 2019 Submissions on EasyChair

Submission Types

We invite three types of submissions:

• original research papers: advances in TPM, not previously published in an archival conference or journal.
• recently published research papers: advances in TPM, already published at a recent venue.
• position papers: discussing tendencies, issues or future venues of interest for the TPM community.

Topics

We invite submissions about any topic pertaining tractable probabilistic modeling. Here is a non-exhaustive list of possible venues. Any other work relevant to the TPM community will be highly appreciated.

• Tractable inference with neural probabilistic models
• Challenges in tractable probabilistic programming
• New tractable representations in discrete, continuous and hybrid domains
• Tractable models and explainable AI
• Learning algorithms for tractable probabilistic models
• Theoretical and empirical analysis of tractable modeling
• Approximate inference algorithms with guarantees on approximation quality
• Applications of tractable probabilistic modeling