Connectionists: [CFP] TPM @ UAI 2024: The 7th Workshop on Tractable Probabilistic Modeling

[YooJung Choi]

****The 7th Workshop on Tractable Probabilistic Modeling (TPM): Models We
Trust******

https://tractable-probabilistic-modeling.github.io/tpm2024/
For AI and ML systems aimed to assist decision-making in real-world
scenarios, it is crucial to perform complex reasoning under uncertainty
reliably and efficiently. However, contemporary machine learning is often
criticized as being sensitive to data-perturbations, lacking guarantees on
their predictions, and having little or no causal and symbolic reasoning
capabilities. Further underpinning the relevance of these challenges,
various regulatory bodies have released statements and frameworks aimed at
building trustworthy AI.

The field of tractable probabilistic models (TPMs) is a very appealing
approach for many of these challenges, 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., circuits) and is dynamically
evolving.

This year’s workshop on Tractable Probabilistic Modeling aims to highlight
the connection between trustworthy artificial intelligence (in a broad
sense) and tractable reasoning. The workshop aims to highlight recent
advancements in the field of TPMs and their potential impact in settings of
trustworthy AI (e.g. fairness, robustness, causality, neuro-symbolic AI).

The workshop will be held *in person *on July 19th, 2024, co-located with
UAI 2024 at the Universitat Pompeu Fabra, Barcelona, Spain.

*Important Dates*

   - *Submission deadline: *May 20th, 2024 AoE
   - *Author Notification (tentative): *June 18th, 2024 AoE
   - *Workshop date: *July 19th, 2024

*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
   - TPMs for trustworthy ML
   - 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 and should use the adjusted template TPM format
<https://tractable-probabilistic-modeling.github.io/tpm2024//assets/tpm2024-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 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: tpmworkshop2024 at gmail.com

Submission Link:
https://openreview.net/group?id=auai.org/UAI/2024/Workshop/TPM

*Organizers*
YooJung Choi (Arizona State University)
Martin Trapp (Aalto University)
Fabrizio Ventola (TU Darmstadt)
Benjie Wang (UCLA)

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