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. 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. Therefore, it is no wonder that research on modeling and learning different TPMs has been flourishing recently. 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. At the same time, it is important that these guarantees can be carried out efficiently. either exact or coming with approximation guarantees. For safety-critical systems, such as applications in healthcare and finance, it is crucial that this reasoning is reliable, i.e. AI and ML systems designed and deployed to support decision making in the real world need to perform complex reasoning under uncertainty.
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