AI and ML systems designed and deployed to support decision making in the real world need to perform complex reasoning under uncertainty. For safety-critical systems, such as applications in healthcare and finance, it is crucial that this reasoning is reliable, i.e. either exact or coming with approximation guarantees. At the same time, it is important that these guarantees can be carried out efficiently. For this, tractable probabilistic models (TPMs) are very appealing because they support reliable and efficient reasoning for a wide range of reasoning scenarios, by design. Therefore, it is no wonder that research on modeling and learning different TPMs has been flourishing recently. The variegated TPM spectrum includes models that deliver tractable computation of likelihoods such as normalizing flow, Gaussian processes and autoregressive models; tractable marginals, such as mixture models, bounded-treewidth models, and determinantal point processes; and models supporting more complex reasoning scenarios such as probabilistic circuits. As the subtitle of this year’s Workshop proposal suggests, we are particularly interested in bridging the latest theoretical advancements in this spectrum with the burgeoning literature on applying TPMs to real-world problems. In particular, TPMs have been successfully used in image classification, completion and generation, activity recognition, language and speech modeling, verification and diagnosis of physical systems, and more recently in computational life science, e.g., for drug discovery and epidemiology modeling.
Prospective authors are invited to submit novel research, retrospective papers or recently accepted papers on relevant topics including, but not limited to:
New tractable representations in logical, continuous and hybrid domains
Learning algorithms for tractable probabilistic models
Theoretical and empirical analysis of tractable probabilistic models
Connections between tractable probabilistic modeling classes
Tractable probabilistic models for responsible, robust and explainable AI
Approximate inference algorithms (with guarantees)
Successful applications of tractable probabilistic models to real-world problems
Submissions of original or retrospective papers should be up to 4 pages and use the TPM format.
See call for papers for further instructions.
Submission deadline: June 13th, 2022 AoE
Notification of acceptance: July 5th, 2022
Camera-ready version: August 12th, 2022
Workshop date: August 5th, 2022
The workshop will be held in-person on August 5th, 2022, co-located with UAI 2022 in Eindhoven, Netherlands.
The tentative schedule can be found here.
YooJung Choi, UCLA, USA
Eric Nalisnick, University of Amsterdam, Netherlands
Martin Trapp, Aalto University, Finland
Fabrizio Ventola, TU Darmstadt, Germany
Antonio Vergari, University of Edinburgh, UK
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