Connectionists: [CFP] TPM @ UAI 2023: The 6th Workshop on Tractable Probabilistic Modeling

[YooJung Choi]

****The 6th Workshop on Tractable Probabilistic Modeling (TPM): Building
Bridges******

https://tractable-probabilistic-modeling.github.io/tpm2023/

For AI and ML systems aimed to assist decision-making in real-world
scenarios, it is crucial to perform complex reasoning under uncertainty.
Moreover, in safety-critical settings, such as applications in healthcare
and finance, the reasoning needs to be reliable and efficient. The emerging
field of tractable probabilistic models (TPMs) is a very appealing approach
in such scenarios as TPMs enable reliable (exact or coming with
approximation guarantees) and efficient reasoning for a wide range of
tasks, by design. The spectrum of TPMs consists of a wide variety of
techniques including models with tractable likelihoods (e.g., normalizing
flow and autoregressive models), tractable marginals (e.g.,
bounded-treewidth models and determinantal point processes), and more
complex tractable reasoning tasks (e.g., probabilistic circuits) and is
dynamically evolving.

This year’s workshop on Tractable Probabilistic Modeling aims to build
bridges between the multitude of techniques for tractable reasoning and
fields in which tractability is key (e.g., probabilistic programming,
approximate Bayesian inference, causal reasoning, and complex systems).

The workshop will be held in a *hybrid *format on August 4th, 2023,
co-located with UAI 2023 in Pittsburgh, PA, USA.

*Important Dates*

   - *Submission deadline: *June 5th, 2023 AoE
   - *Author Notification: *July 4th, 2023 AoE
   - *Workshop date: *August 4th, 2023
   - *Camera-ready deadline: *August 18th, 2023 AoE

*Topics of interest*

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 TPMs
   - Theoretical and empirical analysis of TPMs
   - Connections between TPM classes
   - TPMs for responsible, robust, and explainable AI
   - Approximate inference algorithms with guarantees
   - Successful applications of TPMs to real-world problems

*Submission Instructions*
Original papers and retrospective papers are required to follow the style
guidelines of UAI 2023 and should be using the following adjusted template TPM
format
<https://tractable-probabilistic-modeling.github.io/tpm2023/assets/tpm2023-template.zip>.
Submitted papers should be up to 4 pages long, excluding references.
Already accepted papers can be submitted in the format of the venue they
have been accepted to. Supplementary material can be put in the same pdf
paper (after references); it is entirely up to the reviewers to decide
whether they wish to consult this additional material.

All submissions must be electronic (through the link below), and must
closely follow the formatting guidelines in the templates; otherwise they
will automatically be rejected. Reviewing for TPM 2023 is single-blind;
i.e., reviewers will know the authors’ identity but authors won't know the
reviewers' identity. However, we recommend that you refer to your prior
work in the third person wherever possible. We also encourage links to
public repositories such as GitHub to share code and/or data.

For any questions, please contact us at tpmworkshop2023 at gmail.com

Submission Link:
https://openreview.net/group?id=auai.org/UAI/2023/Workshop/TPM

*Organizers*
YooJung Choi (Arizona State University)
Eric Nalisnick (University of Amsterdam)
Martin Trapp (Aalto University)
Fabrizio Ventola (TU Darmstadt)
Antonio Vergari (University of Edinburgh)

****Please consider sharing this CFP in your network****
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